https://www.ccn.ucla.edu/wiki/api.php?action=feedcontributions&feedformat=atom&user=Alenartowicz 2026-05-06T04:25:49Z User contributions MediaWiki 1.43.6 https://www.ccn.ucla.edu/wiki/index.php?title=Principles_of_Neuroimaging_B_-_2017&diff=3267 2017-02-09T04:01:51Z

<p>Alenartowicz: </p> <hr /> <div>Principles of Neuroimaging B, Winter, 2017 - Class Schedule and Syllabus<br /> <br /> == This schedule is a draft only and ''will'' change!==<br /> <br /> :'''[[Principles_of_Neuroimaging_-_2016-2017 | Back to main course page for Principles of Neuroimaging]]'''<br /> <br /> =Course Reading=<br /> ===Required Reading===<br /> :'''Signal Processing for Neuroscientists''' by ''Wim van Drongelen''<br /> ::This can be found as a PDF on scribd.com, for a small fee of $8.99<br /> <br /> ===Supplemental Reading===<br /> :'''Matlab for Neuroscientists'''<br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/88212458/Matlab-Matlab-for-Neuroscientists<br /> <br /> :'''Cartoon Guide to Statistics''' <br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/148072668/Cartoon-Guide-to-Statistics<br /> <br /> :'''NOTE:''' if you subscribe for a Scribd account for a day, you can download as many documents as you like for one fee.<br /> <br /> =Week 1: Magnetic Resonance Imaging: Fundamentals=<br /> <br /> Magnetic Resonance Imaging (MRI) is probably the most influential and most flexible current means of imaging the human brain. It features a vast number of separable contrast mechanisms, and a near ideal combination of non-invasiveness, safety, resolution and metric accuracy. However, it is extraordinarily expensive and has limited temporal resolution, especially for functional studies<br /> <br /> <br /> OUTLINE<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> :*[http://www.cis.rit.edu/htbooks/mri/ These notes] by Joseph Hornak are highly professional and complete coverage of MRI.<br /> :*[http://www.imaios.com/en/e-Courses/e-MRI eMRI] is another excellent online MRI learning resource<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Hahn1950.pdf Erwin Hahn - Spin Echoes: ''Essential reading for the MRI community'']<br /> [[Image:HahnFig1.png]]<br /> ''above'': Figure 1 from Hahn, 1950<br /> <br /> ''Suggested Further Reading''<br /> [[image:PSatSeq.jpg|right]]<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> <br /> ==''Monday 1/9/17'' ==<br /> ===- MR Physics I. ''Speaker'': [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> ==''Wednesday 1/11/17'' ==<br /> ===- MR Physics II. ''Speaker'': [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> <br /> =Week 2: MRI Techniques=<br /> ==''Monday 1/16/17'' ==<br /> ===MLK Day No Class===<br /> <br /> ==''Wednesday 1/18/17'' ==<br /> ===- MR Spatial Encoding Q&amp;A. ''Speaker'' Cameron Rodriguez===<br /> <br /> <br /> =Week 3: MRI Applications =<br /> ==''Monday 1/23/17'' ==<br /> ===- MR Pulse Sequences and Parameters ''Speaker'' [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> ==''Wednesday 1/25/17'' ==<br /> ===- MR Applications (MR, fMRI, DTI, ASL, Spectroscopy). ''Speaker'' [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> <br /> =Week 4: Multimodal Imaging=<br /> ==''Monday 1/30/17 CLASS IN SEMEL 67-415''==<br /> ===- Multimodal I ''Speaker'' Cameron Rodriguez===<br /> <br /> ==''Wednesday 2/1/17'' ==<br /> ===- Multimodal II ''Speaker'' Agatha Lenartowicz===<br /> <br /> :*[[media:eegfmritalk.pdf‎ | Lecture Slides]]<br /> :*[[media:chapter7.pdf‎ | EEG Instrumentation]]<br /> :*[[media:chapter8.pdf‎ | EEG Quality I]]<br /> :*[[media:chapter9.pdf‎ | EEG Quality II]]<br /> :*[[media:chapter10.pdf‎ | Image Quality]]<br /> <br /> =Week 5: Review =<br /> ==''Monday 2/6/17''==<br /> ===- Q&amp;A Class ===<br /> <br /> ==''Wednesday 2/8/17''==<br /> ===- Midterm Review===<br /> <br /> =MIDTERM=<br /> <br /> <br /> =Week 6: Image Processing=<br /> ==''Monday 2/13/17'' ==<br /> ===cont. Midterm Review===<br /> <br /> ==''Wednesday 2/15/17'' ==<br /> ===-Image Processing I. ''Speaker'' Cameron Rodriguez===<br /> <br /> =Week 7: Image Analysis=<br /> ==''Monday 2/20/17'' ==<br /> ===President’s Day Holiday No Class===<br /> <br /> ==''Wednesday 2/22/17'' ==<br /> ===- Image Processing II. ''Speaker'' Agatha Lenartowicz===<br /> <br /> =Week 8: Image Analysis Cont.=<br /> ==''Monday 2/27/17 CLASS IN SEMEL 67-415'' ==<br /> ===Analysis I ''Speaker'' Agatha Lenartowicz===<br /> <br /> ==''Wednesday 3/1/17'' ==<br /> ===- Analysis II ''Speaker'' Agatha Lenartowicz===<br /> <br /> =Week 9: Brain Decoding=<br /> ==''Monday 3/6/17'' ==<br /> ===-ML I: Dimension Reduction, Machine Learning Fundamentals ''Speaker'' Wesley Kerr, Ph.D.===<br /> <br /> ==''Wednesday 3/8/17'' ==<br /> ===- MLII: Machine Learning cont. ''Speaker'' Wesley Kerr, Ph.D.===<br /> <br /> =Week 10: Review=<br /> ==''Monday 3/13/17'' ==<br /> ===-TBD ''Speaker'' TBD===<br /> <br /> ==''Wednesday 3/15/17'' ==<br /> ===-Final Exam Review. ''Speaker'' TBD===<br /> <br /> <br /> <br /> <br /> <br /> &lt;!--<br /> [[Image:ASL.png | right]]<br /> ===- Measurement of Blood Flow by MRI. ''Danny JJ Wang'': [http://www.bri.ucla.edu/people/danny-jiong-jiong-wang-phd]===<br /> Most MRI images in use today are static maps, generally called &quot;structural&quot; images. However, the MRI signal is sensitive to a wide variety of dynamic phenomena. Practitioners typically use the term &quot;functional&quot; to describe images that pick up time-varying signals. The term ''functional MRI'', of course, has been usurped to describe brain activity mapping and, in most cases, refers to BOLD signal effects.<br /> <br /> Measurement of blood ''flow'' in particular is another window into brain activity. Several methods exist to create signal sensitive to blood flow, or blood flow changes. Some label the blood flow based on relaxation effects (Arterial Spin Labeling - ASL - being one important method) and others measure flow by velocity.<br /> <br /> In most cases, MRI is used as a semi quantitative modality. We get accurate spatial metrics but, in general, the image intensities are in arbitrary units. Blood flow is one particular feature that we can measure in native units.<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_perfusion-web.pdf‎ | Dr. Wang's Handout]]<br /> <br /> :*[http://www.cis.rit.edu/htbooks/mri/ These notes] by Joseph Hornak are highly professional and complete coverage of MRI.]<br /> :*[[media:M284B_perfusion-web.pdf‎ | Dr. Wang's Handout]]<br /> [[Image:HahnFig1.png]]<br /> ''above'': Figure 1 from Hahn, 1950<br /> <br /> ''Suggested Further Reading''<br /> :*[http://www.pnas.org/content/89/1/212.long Williams, PNAS 1992]<br /> :*[http://onlinelibrary.wiley.com/doi/10.1002/mrm.1910230106/abstract;jsessionid=E42CC3817A5B7B6BB12B6BA3562C1913.f03t04 Detre, MRM 1992]<br /> :*[http://onlinelibrary.wiley.com/doi/10.1002/mrm.21790/epdf Dai, MRM 2008]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/17969096 Wu, MRM 2007]<br /> :*[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3155947/ Zheng, Neuroimage 2012]<br /> <br /> <br /> <br /> =Week 8: Advanced MRI methods=<br /> ==''Monday 2/22/16'' ==<br /> [[Image:DIffusingSpheres.png | left]]<br /> ===- Diffusion Physics ''Speaker'': [http://radiology.ucla.edu/body.cfm?id=48&amp;action=detail&amp;ref=18 Ben Ellingson]===<br /> ''Required Readings''<br /> Slides should be up soon.<br /> :*[https://www.dropbox.com/s/0feqz7tlojbfgzl/1_DiffusionMRI_Methods_2015.key.pdf?dl=0 Handouts for class on 1/14/15] ''&lt;- New 1/26-2015''<br /> :*[[media:1_DiffusionMRI_Methods_v1 - 2014.pdf | Ben Ellingson Diffusion Slides]] uploaded 3/14/14<br /> ''Suggested Further Reading''<br /> :*[[media:DTIHageman.pdf | Hageman DTI Slides]]<br /> :*[[media:NICourse_DTILecture4Post_11-01-26.pdf | Hageman DTI Notes 2/26/12]]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/15037456?dopt=Citation Diffusion tensor imaging of cerebral white matter: a pictorial review of physics, fiber tract anatomy, and tumor imaging patterns AJNR Am J Neuroradiol. 2004 Mar;25(3):356-69]<br /> <br /> :Sadly, the library does not have a subscription for the journals below (Mark has copies on reserve in his office):<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/8661285?dopt=Citation Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J Magn Reson B. 1996 Jun;111(3):209-19]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/16950152?dopt=Citation Principles of diffusion tensor imaging and its applications to basic neuroscience research. Neuron. 2006 Sep 7;51(5):527-39]<br /> <br /> ==''Wednesday 2/25/16'' ==<br /> [[Image:Callosum.png | right]]<br /> ===- Brain Morphometry. ''Speaker'': [http://www.bmap.ucla.edu/about/peopledetails/roger_woods/ Roger Woods]: ===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_Woods.pdf‎ | Dr. Woods' slides]]<br /> <br /> ''Suggested Further Reading''<br /> :[http://www.ncbi.nlm.nih.gov/pubmed/12667854 *RP Woods, “Characterizing volume and surface deformations in an atlas framework: theory, applications, and implementation.” Neuroimage, 18(3): p. 769-788. 2003]<br /> <br /> =Week 9: MR Spectroscopy=<br /> ==''Monday 2/29/16'' ==<br /> No class<br /> <br /> ==''Wednesday 3/2/16'' ==<br /> === Spectroscopy fundamentals. ''Speaker'': [http://people.healthsciences.ucla.edu/institution/personnel?personnel_id=74852 Joseph O'Neill] ===<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:ONeill2016Spectro.pdf‎ | Dr. O'Neill's slides]]<br /> <br /> ===- Spectroscopic Methods. ''Speaker'': [http://radiology.ucla.edu/body.cfm?id=48&amp;action=detail&amp;ref=65 Albert Thomas] ===<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_AThomas030216.pdf‎ | Dr. Thomas' slides]]<br /> ''Suggested Further Reading''<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/23292856 SA Wijtenburg, LM Rowland, RA Edden and PB Barker, “Reproducibility of brain spectroscopy at 7T using conventional localization and spectral editing techniques.” J Magn Reson Imaging, 38(2): p. 460-467. 2013. PMCID: PMC3620961]<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/20574964 S Lipnick, G Verma, S Ramadan, J Furuyama and MA Thomas, “Echo planar correlated spectroscopic imaging: implementation and pilot evaluation in human calf in vivo.” Magn Reson Med, 64(4): p. 947-956. 2010. PMCID: PMC2946523]<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/22505247 JK Furuyama, NE Wilson, BL Burns, R Nagarajan, DJ Margolis and MA Thomas, “Application of compressed sensing to multidimensional spectroscopic imaging in human prostate.” Magn Reson Med, 67(6): p. 1499-1505. 2012.]<br /> <br /> =Week 10: Multimodal Methods / Course wrap up=<br /> ==''Monday 3/7/16'' ==<br /> [[Image:Kinship.png | right]]<br /> ===- Imaging Genetics. ''Speaker'': [http://www.semel.ucla.edu/bearden-lab Carrie Bearden]: ===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:Bearden_NITP2_2016-3-4.pdf | Dr. Bearden's slides]]<br /> <br /> ''Suggested Further Reading''<br /> :*[[media:Carter-Genomics.pdf | ''Enhancing the Informativeness and Replicability of Imaging Genomics Studies'' Carter et al.]]<br /> :*[[media:Flint_and_Geschwind.pdf | ''Genetics and genomics of psychiatric disease'' Flint and Geschwind]]<br /> <br /> <br /> --&gt;</div>

Alenartowicz https://www.ccn.ucla.edu/wiki/index.php?title=File:Chapter10.pdf&diff=3251 2017-02-01T05:43:19Z

<p>Alenartowicz: </p> <hr /> <div></div>

Alenartowicz https://www.ccn.ucla.edu/wiki/index.php?title=File:Chapter9.pdf&diff=3250 2017-02-01T05:42:55Z

<p>Alenartowicz: </p> <hr /> <div></div>

Alenartowicz https://www.ccn.ucla.edu/wiki/index.php?title=File:Chapter8.pdf&diff=3249 2017-02-01T05:42:37Z

<p>Alenartowicz: </p> <hr /> <div></div>

Alenartowicz https://www.ccn.ucla.edu/wiki/index.php?title=File:Chapter7.pdf&diff=3248 2017-02-01T05:42:19Z

<p>Alenartowicz: </p> <hr /> <div></div>

Alenartowicz https://www.ccn.ucla.edu/wiki/index.php?title=Principles_of_Neuroimaging_B_-_2017&diff=3247 2017-02-01T05:41:57Z

<p>Alenartowicz: /* - Multimodal II Speaker Agatha Lenartowicz */</p> <hr /> <div>Principles of Neuroimaging B, Winter, 2017 - Class Schedule and Syllabus<br /> <br /> == This schedule is a draft only and ''will'' change!==<br /> <br /> :'''[[Principles_of_Neuroimaging_-_2016-2017 | Back to main course page for Principles of Neuroimaging]]'''<br /> <br /> =Course Reading=<br /> ===Required Reading===<br /> :'''Signal Processing for Neuroscientists''' by ''Wim van Drongelen''<br /> ::This can be found as a PDF on scribd.com, for a small fee of $8.99<br /> <br /> ===Supplemental Reading===<br /> :'''Matlab for Neuroscientists'''<br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/88212458/Matlab-Matlab-for-Neuroscientists<br /> <br /> :'''Cartoon Guide to Statistics''' <br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/148072668/Cartoon-Guide-to-Statistics<br /> <br /> :'''NOTE:''' if you subscribe for a Scribd account for a day, you can download as many documents as you like for one fee.<br /> <br /> =Week 1: Magnetic Resonance Imaging: Fundamentals=<br /> <br /> Magnetic Resonance Imaging (MRI) is probably the most influential and most flexible current means of imaging the human brain. It features a vast number of separable contrast mechanisms, and a near ideal combination of non-invasiveness, safety, resolution and metric accuracy. However, it is extraordinarily expensive and has limited temporal resolution, especially for functional studies<br /> <br /> <br /> OUTLINE<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> :*[http://www.cis.rit.edu/htbooks/mri/ These notes] by Joseph Hornak are highly professional and complete coverage of MRI.<br /> :*[http://www.imaios.com/en/e-Courses/e-MRI eMRI] is another excellent online MRI learning resource<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Hahn1950.pdf Erwin Hahn - Spin Echoes: ''Essential reading for the MRI community'']<br /> [[Image:HahnFig1.png]]<br /> ''above'': Figure 1 from Hahn, 1950<br /> <br /> ''Suggested Further Reading''<br /> [[image:PSatSeq.jpg|right]]<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> <br /> ==''Monday 1/9/17'' ==<br /> ===- MR Physics I. ''Speaker'': [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> ==''Wednesday 1/11/17'' ==<br /> ===- MR Physics II. ''Speaker'': [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> <br /> =Week 2: MRI Techniques=<br /> ==''Monday 1/16/17'' ==<br /> ===MLK Day No Class===<br /> <br /> ==''Wednesday 1/18/17'' ==<br /> ===- MR Spatial Encoding Q&amp;A. ''Speaker'' Cameron Rodriguez===<br /> <br /> <br /> =Week 3: MRI Applications =<br /> ==''Monday 1/23/17'' ==<br /> ===- MR Pulse Sequences and Parameters ''Speaker'' [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> ==''Wednesday 1/25/17'' ==<br /> ===- MR Applications (MR, fMRI, DTI, ASL, Spectroscopy). ''Speaker'' [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> <br /> =Week 4: Multimodal Imaging=<br /> ==''Monday 1/30/17 CLASS IN SEMEL 67-415''==<br /> ===- Multimodal I ''Speaker'' Cameron Rodriguez===<br /> <br /> ==''Wednesday 2/1/17'' ==<br /> ===- Multimodal II ''Speaker'' Agatha Lenartowicz===<br /> <br /> :*[[media:eegfmritalk.pdf‎ | Lecture Slides]]<br /> :*[[media:chapter7.pdf‎ | EEG Instrumentation]]<br /> :*[[media:chapter8.pdf‎ | EEG Quality I]]<br /> :*[[media:chapter9.pdf‎ | EEG Quality II]]<br /> :*[[media:chapter10.pdf‎ | Image Quality]]<br /> <br /> =Week 5: Review =<br /> ==''Monday 2/6/17''==<br /> ===- Q&amp;A Class ===<br /> <br /> ==''Wednesday 2/8/17''==<br /> ===- Midterm Review===<br /> <br /> =MIDTERM=<br /> <br /> <br /> =Week 6: Image Processing=<br /> ==''Monday 2/13/17'' ==<br /> ===-Image Processing I. ''Speaker'' Cameron Rodriguez===<br /> <br /> ==''Wednesday 2/15/17'' ==<br /> ===-Image Processing II. ''Speaker'' Agatha Lenartowicz===<br /> <br /> =Week 7: Image Analysis=<br /> ==''Monday 2/20/17'' ==<br /> ===President’s Day Holiday No Class===<br /> <br /> ==''Wednesday 2/22/17'' ==<br /> ===- Analysis I ''Speaker'' Agatha Lenartowicz===<br /> <br /> =Week 8: Image Analysis Cont.=<br /> ==''Monday 2/27/17 CLASS IN SEMEL 67-415'' ==<br /> ===Analysis II ''Speaker'' Agatha Lenartowicz===<br /> <br /> ==''Wednesday 3/1/17'' ==<br /> ===- TBD ''Speaker'' TBD===<br /> <br /> =Week 9: Brain Decoding=<br /> ==''Monday 3/6/17'' ==<br /> ===-ML I: Dimension Reduction, Machine Learning Fundamentals ''Speaker'' Wesley Kerr, Ph.D.===<br /> <br /> ==''Wednesday 3/8/17'' ==<br /> ===- MLII: Machine Learning cont. ''Speaker'' Wesley Kerr, Ph.D.===<br /> <br /> =Week 10: Review=<br /> ==''Monday 3/13/17'' ==<br /> ===-TBD ''Speaker'' TBD===<br /> <br /> ==''Wednesday 3/15/17'' ==<br /> ===-Final Exam Review. ''Speaker'' TBD===<br /> <br /> <br /> <br /> <br /> <br /> &lt;!--<br /> [[Image:ASL.png | right]]<br /> ===- Measurement of Blood Flow by MRI. ''Danny JJ Wang'': [http://www.bri.ucla.edu/people/danny-jiong-jiong-wang-phd]===<br /> Most MRI images in use today are static maps, generally called &quot;structural&quot; images. However, the MRI signal is sensitive to a wide variety of dynamic phenomena. Practitioners typically use the term &quot;functional&quot; to describe images that pick up time-varying signals. The term ''functional MRI'', of course, has been usurped to describe brain activity mapping and, in most cases, refers to BOLD signal effects.<br /> <br /> Measurement of blood ''flow'' in particular is another window into brain activity. Several methods exist to create signal sensitive to blood flow, or blood flow changes. Some label the blood flow based on relaxation effects (Arterial Spin Labeling - ASL - being one important method) and others measure flow by velocity.<br /> <br /> In most cases, MRI is used as a semi quantitative modality. We get accurate spatial metrics but, in general, the image intensities are in arbitrary units. Blood flow is one particular feature that we can measure in native units.<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_perfusion-web.pdf‎ | Dr. Wang's Handout]]<br /> <br /> :*[http://www.cis.rit.edu/htbooks/mri/ These notes] by Joseph Hornak are highly professional and complete coverage of MRI.]<br /> :*[[media:M284B_perfusion-web.pdf‎ | Dr. Wang's Handout]]<br /> [[Image:HahnFig1.png]]<br /> ''above'': Figure 1 from Hahn, 1950<br /> <br /> ''Suggested Further Reading''<br /> :*[http://www.pnas.org/content/89/1/212.long Williams, PNAS 1992]<br /> :*[http://onlinelibrary.wiley.com/doi/10.1002/mrm.1910230106/abstract;jsessionid=E42CC3817A5B7B6BB12B6BA3562C1913.f03t04 Detre, MRM 1992]<br /> :*[http://onlinelibrary.wiley.com/doi/10.1002/mrm.21790/epdf Dai, MRM 2008]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/17969096 Wu, MRM 2007]<br /> :*[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3155947/ Zheng, Neuroimage 2012]<br /> <br /> <br /> <br /> =Week 8: Advanced MRI methods=<br /> ==''Monday 2/22/16'' ==<br /> [[Image:DIffusingSpheres.png | left]]<br /> ===- Diffusion Physics ''Speaker'': [http://radiology.ucla.edu/body.cfm?id=48&amp;action=detail&amp;ref=18 Ben Ellingson]===<br /> ''Required Readings''<br /> Slides should be up soon.<br /> :*[https://www.dropbox.com/s/0feqz7tlojbfgzl/1_DiffusionMRI_Methods_2015.key.pdf?dl=0 Handouts for class on 1/14/15] ''&lt;- New 1/26-2015''<br /> :*[[media:1_DiffusionMRI_Methods_v1 - 2014.pdf | Ben Ellingson Diffusion Slides]] uploaded 3/14/14<br /> ''Suggested Further Reading''<br /> :*[[media:DTIHageman.pdf | Hageman DTI Slides]]<br /> :*[[media:NICourse_DTILecture4Post_11-01-26.pdf | Hageman DTI Notes 2/26/12]]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/15037456?dopt=Citation Diffusion tensor imaging of cerebral white matter: a pictorial review of physics, fiber tract anatomy, and tumor imaging patterns AJNR Am J Neuroradiol. 2004 Mar;25(3):356-69]<br /> <br /> :Sadly, the library does not have a subscription for the journals below (Mark has copies on reserve in his office):<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/8661285?dopt=Citation Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J Magn Reson B. 1996 Jun;111(3):209-19]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/16950152?dopt=Citation Principles of diffusion tensor imaging and its applications to basic neuroscience research. Neuron. 2006 Sep 7;51(5):527-39]<br /> <br /> ==''Wednesday 2/25/16'' ==<br /> [[Image:Callosum.png | right]]<br /> ===- Brain Morphometry. ''Speaker'': [http://www.bmap.ucla.edu/about/peopledetails/roger_woods/ Roger Woods]: ===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_Woods.pdf‎ | Dr. Woods' slides]]<br /> <br /> ''Suggested Further Reading''<br /> :[http://www.ncbi.nlm.nih.gov/pubmed/12667854 *RP Woods, “Characterizing volume and surface deformations in an atlas framework: theory, applications, and implementation.” Neuroimage, 18(3): p. 769-788. 2003]<br /> <br /> =Week 9: MR Spectroscopy=<br /> ==''Monday 2/29/16'' ==<br /> No class<br /> <br /> ==''Wednesday 3/2/16'' ==<br /> === Spectroscopy fundamentals. ''Speaker'': [http://people.healthsciences.ucla.edu/institution/personnel?personnel_id=74852 Joseph O'Neill] ===<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:ONeill2016Spectro.pdf‎ | Dr. O'Neill's slides]]<br /> <br /> ===- Spectroscopic Methods. ''Speaker'': [http://radiology.ucla.edu/body.cfm?id=48&amp;action=detail&amp;ref=65 Albert Thomas] ===<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_AThomas030216.pdf‎ | Dr. Thomas' slides]]<br /> ''Suggested Further Reading''<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/23292856 SA Wijtenburg, LM Rowland, RA Edden and PB Barker, “Reproducibility of brain spectroscopy at 7T using conventional localization and spectral editing techniques.” J Magn Reson Imaging, 38(2): p. 460-467. 2013. PMCID: PMC3620961]<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/20574964 S Lipnick, G Verma, S Ramadan, J Furuyama and MA Thomas, “Echo planar correlated spectroscopic imaging: implementation and pilot evaluation in human calf in vivo.” Magn Reson Med, 64(4): p. 947-956. 2010. PMCID: PMC2946523]<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/22505247 JK Furuyama, NE Wilson, BL Burns, R Nagarajan, DJ Margolis and MA Thomas, “Application of compressed sensing to multidimensional spectroscopic imaging in human prostate.” Magn Reson Med, 67(6): p. 1499-1505. 2012.]<br /> <br /> =Week 10: Multimodal Methods / Course wrap up=<br /> ==''Monday 3/7/16'' ==<br /> [[Image:Kinship.png | right]]<br /> ===- Imaging Genetics. ''Speaker'': [http://www.semel.ucla.edu/bearden-lab Carrie Bearden]: ===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:Bearden_NITP2_2016-3-4.pdf | Dr. Bearden's slides]]<br /> <br /> ''Suggested Further Reading''<br /> :*[[media:Carter-Genomics.pdf | ''Enhancing the Informativeness and Replicability of Imaging Genomics Studies'' Carter et al.]]<br /> :*[[media:Flint_and_Geschwind.pdf | ''Genetics and genomics of psychiatric disease'' Flint and Geschwind]]<br /> <br /> <br /> --&gt;</div>

Alenartowicz https://www.ccn.ucla.edu/wiki/index.php?title=Principles_of_Neuroimaging_B_-_2017&diff=3246 2017-02-01T05:35:14Z

<p>Alenartowicz: </p> <hr /> <div>Principles of Neuroimaging B, Winter, 2017 - Class Schedule and Syllabus<br /> <br /> == This schedule is a draft only and ''will'' change!==<br /> <br /> :'''[[Principles_of_Neuroimaging_-_2016-2017 | Back to main course page for Principles of Neuroimaging]]'''<br /> <br /> =Course Reading=<br /> ===Required Reading===<br /> :'''Signal Processing for Neuroscientists''' by ''Wim van Drongelen''<br /> ::This can be found as a PDF on scribd.com, for a small fee of $8.99<br /> <br /> ===Supplemental Reading===<br /> :'''Matlab for Neuroscientists'''<br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/88212458/Matlab-Matlab-for-Neuroscientists<br /> <br /> :'''Cartoon Guide to Statistics''' <br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/148072668/Cartoon-Guide-to-Statistics<br /> <br /> :'''NOTE:''' if you subscribe for a Scribd account for a day, you can download as many documents as you like for one fee.<br /> <br /> =Week 1: Magnetic Resonance Imaging: Fundamentals=<br /> <br /> Magnetic Resonance Imaging (MRI) is probably the most influential and most flexible current means of imaging the human brain. It features a vast number of separable contrast mechanisms, and a near ideal combination of non-invasiveness, safety, resolution and metric accuracy. However, it is extraordinarily expensive and has limited temporal resolution, especially for functional studies<br /> <br /> <br /> OUTLINE<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> :*[http://www.cis.rit.edu/htbooks/mri/ These notes] by Joseph Hornak are highly professional and complete coverage of MRI.<br /> :*[http://www.imaios.com/en/e-Courses/e-MRI eMRI] is another excellent online MRI learning resource<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Hahn1950.pdf Erwin Hahn - Spin Echoes: ''Essential reading for the MRI community'']<br /> [[Image:HahnFig1.png]]<br /> ''above'': Figure 1 from Hahn, 1950<br /> <br /> ''Suggested Further Reading''<br /> [[image:PSatSeq.jpg|right]]<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> <br /> ==''Monday 1/9/17'' ==<br /> ===- MR Physics I. ''Speaker'': [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> ==''Wednesday 1/11/17'' ==<br /> ===- MR Physics II. ''Speaker'': [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> <br /> =Week 2: MRI Techniques=<br /> ==''Monday 1/16/17'' ==<br /> ===MLK Day No Class===<br /> <br /> ==''Wednesday 1/18/17'' ==<br /> ===- MR Spatial Encoding Q&amp;A. ''Speaker'' Cameron Rodriguez===<br /> <br /> <br /> =Week 3: MRI Applications =<br /> ==''Monday 1/23/17'' ==<br /> ===- MR Pulse Sequences and Parameters ''Speaker'' [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> ==''Wednesday 1/25/17'' ==<br /> ===- MR Applications (MR, fMRI, DTI, ASL, Spectroscopy). ''Speaker'' [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> <br /> =Week 4: Multimodal Imaging=<br /> ==''Monday 1/30/17 CLASS IN SEMEL 67-415''==<br /> ===- Multimodal I ''Speaker'' Cameron Rodriguez===<br /> <br /> ==''Wednesday 2/1/17'' ==<br /> ===- Multimodal II ''Speaker'' Agatha Lenartowicz===<br /> <br /> :*[[media:eegfmritalk.pdf‎ | Lecture Slides]]<br /> <br /> <br /> =Week 5: Review =<br /> ==''Monday 2/6/17''==<br /> ===- Q&amp;A Class ===<br /> <br /> ==''Wednesday 2/8/17''==<br /> ===- Midterm Review===<br /> <br /> =MIDTERM=<br /> <br /> <br /> =Week 6: Image Processing=<br /> ==''Monday 2/13/17'' ==<br /> ===-Image Processing I. ''Speaker'' Cameron Rodriguez===<br /> <br /> ==''Wednesday 2/15/17'' ==<br /> ===-Image Processing II. ''Speaker'' Agatha Lenartowicz===<br /> <br /> =Week 7: Image Analysis=<br /> ==''Monday 2/20/17'' ==<br /> ===President’s Day Holiday No Class===<br /> <br /> ==''Wednesday 2/22/17'' ==<br /> ===- Analysis I ''Speaker'' Agatha Lenartowicz===<br /> <br /> =Week 8: Image Analysis Cont.=<br /> ==''Monday 2/27/17 CLASS IN SEMEL 67-415'' ==<br /> ===Analysis II ''Speaker'' Agatha Lenartowicz===<br /> <br /> ==''Wednesday 3/1/17'' ==<br /> ===- TBD ''Speaker'' TBD===<br /> <br /> =Week 9: Brain Decoding=<br /> ==''Monday 3/6/17'' ==<br /> ===-ML I: Dimension Reduction, Machine Learning Fundamentals ''Speaker'' Wesley Kerr, Ph.D.===<br /> <br /> ==''Wednesday 3/8/17'' ==<br /> ===- MLII: Machine Learning cont. ''Speaker'' Wesley Kerr, Ph.D.===<br /> <br /> =Week 10: Review=<br /> ==''Monday 3/13/17'' ==<br /> ===-TBD ''Speaker'' TBD===<br /> <br /> ==''Wednesday 3/15/17'' ==<br /> ===-Final Exam Review. ''Speaker'' TBD===<br /> <br /> <br /> <br /> <br /> <br /> &lt;!--<br /> [[Image:ASL.png | right]]<br /> ===- Measurement of Blood Flow by MRI. ''Danny JJ Wang'': [http://www.bri.ucla.edu/people/danny-jiong-jiong-wang-phd]===<br /> Most MRI images in use today are static maps, generally called &quot;structural&quot; images. However, the MRI signal is sensitive to a wide variety of dynamic phenomena. Practitioners typically use the term &quot;functional&quot; to describe images that pick up time-varying signals. The term ''functional MRI'', of course, has been usurped to describe brain activity mapping and, in most cases, refers to BOLD signal effects.<br /> <br /> Measurement of blood ''flow'' in particular is another window into brain activity. Several methods exist to create signal sensitive to blood flow, or blood flow changes. Some label the blood flow based on relaxation effects (Arterial Spin Labeling - ASL - being one important method) and others measure flow by velocity.<br /> <br /> In most cases, MRI is used as a semi quantitative modality. We get accurate spatial metrics but, in general, the image intensities are in arbitrary units. Blood flow is one particular feature that we can measure in native units.<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_perfusion-web.pdf‎ | Dr. Wang's Handout]]<br /> <br /> :*[http://www.cis.rit.edu/htbooks/mri/ These notes] by Joseph Hornak are highly professional and complete coverage of MRI.]<br /> :*[[media:M284B_perfusion-web.pdf‎ | Dr. Wang's Handout]]<br /> [[Image:HahnFig1.png]]<br /> ''above'': Figure 1 from Hahn, 1950<br /> <br /> ''Suggested Further Reading''<br /> :*[http://www.pnas.org/content/89/1/212.long Williams, PNAS 1992]<br /> :*[http://onlinelibrary.wiley.com/doi/10.1002/mrm.1910230106/abstract;jsessionid=E42CC3817A5B7B6BB12B6BA3562C1913.f03t04 Detre, MRM 1992]<br /> :*[http://onlinelibrary.wiley.com/doi/10.1002/mrm.21790/epdf Dai, MRM 2008]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/17969096 Wu, MRM 2007]<br /> :*[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3155947/ Zheng, Neuroimage 2012]<br /> <br /> <br /> <br /> =Week 8: Advanced MRI methods=<br /> ==''Monday 2/22/16'' ==<br /> [[Image:DIffusingSpheres.png | left]]<br /> ===- Diffusion Physics ''Speaker'': [http://radiology.ucla.edu/body.cfm?id=48&amp;action=detail&amp;ref=18 Ben Ellingson]===<br /> ''Required Readings''<br /> Slides should be up soon.<br /> :*[https://www.dropbox.com/s/0feqz7tlojbfgzl/1_DiffusionMRI_Methods_2015.key.pdf?dl=0 Handouts for class on 1/14/15] ''&lt;- New 1/26-2015''<br /> :*[[media:1_DiffusionMRI_Methods_v1 - 2014.pdf | Ben Ellingson Diffusion Slides]] uploaded 3/14/14<br /> ''Suggested Further Reading''<br /> :*[[media:DTIHageman.pdf | Hageman DTI Slides]]<br /> :*[[media:NICourse_DTILecture4Post_11-01-26.pdf | Hageman DTI Notes 2/26/12]]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/15037456?dopt=Citation Diffusion tensor imaging of cerebral white matter: a pictorial review of physics, fiber tract anatomy, and tumor imaging patterns AJNR Am J Neuroradiol. 2004 Mar;25(3):356-69]<br /> <br /> :Sadly, the library does not have a subscription for the journals below (Mark has copies on reserve in his office):<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/8661285?dopt=Citation Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J Magn Reson B. 1996 Jun;111(3):209-19]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/16950152?dopt=Citation Principles of diffusion tensor imaging and its applications to basic neuroscience research. Neuron. 2006 Sep 7;51(5):527-39]<br /> <br /> ==''Wednesday 2/25/16'' ==<br /> [[Image:Callosum.png | right]]<br /> ===- Brain Morphometry. ''Speaker'': [http://www.bmap.ucla.edu/about/peopledetails/roger_woods/ Roger Woods]: ===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_Woods.pdf‎ | Dr. Woods' slides]]<br /> <br /> ''Suggested Further Reading''<br /> :[http://www.ncbi.nlm.nih.gov/pubmed/12667854 *RP Woods, “Characterizing volume and surface deformations in an atlas framework: theory, applications, and implementation.” Neuroimage, 18(3): p. 769-788. 2003]<br /> <br /> =Week 9: MR Spectroscopy=<br /> ==''Monday 2/29/16'' ==<br /> No class<br /> <br /> ==''Wednesday 3/2/16'' ==<br /> === Spectroscopy fundamentals. ''Speaker'': [http://people.healthsciences.ucla.edu/institution/personnel?personnel_id=74852 Joseph O'Neill] ===<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:ONeill2016Spectro.pdf‎ | Dr. O'Neill's slides]]<br /> <br /> ===- Spectroscopic Methods. ''Speaker'': [http://radiology.ucla.edu/body.cfm?id=48&amp;action=detail&amp;ref=65 Albert Thomas] ===<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_AThomas030216.pdf‎ | Dr. Thomas' slides]]<br /> ''Suggested Further Reading''<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/23292856 SA Wijtenburg, LM Rowland, RA Edden and PB Barker, “Reproducibility of brain spectroscopy at 7T using conventional localization and spectral editing techniques.” J Magn Reson Imaging, 38(2): p. 460-467. 2013. PMCID: PMC3620961]<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/20574964 S Lipnick, G Verma, S Ramadan, J Furuyama and MA Thomas, “Echo planar correlated spectroscopic imaging: implementation and pilot evaluation in human calf in vivo.” Magn Reson Med, 64(4): p. 947-956. 2010. PMCID: PMC2946523]<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/22505247 JK Furuyama, NE Wilson, BL Burns, R Nagarajan, DJ Margolis and MA Thomas, “Application of compressed sensing to multidimensional spectroscopic imaging in human prostate.” Magn Reson Med, 67(6): p. 1499-1505. 2012.]<br /> <br /> =Week 10: Multimodal Methods / Course wrap up=<br /> ==''Monday 3/7/16'' ==<br /> [[Image:Kinship.png | right]]<br /> ===- Imaging Genetics. ''Speaker'': [http://www.semel.ucla.edu/bearden-lab Carrie Bearden]: ===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:Bearden_NITP2_2016-3-4.pdf | Dr. Bearden's slides]]<br /> <br /> ''Suggested Further Reading''<br /> :*[[media:Carter-Genomics.pdf | ''Enhancing the Informativeness and Replicability of Imaging Genomics Studies'' Carter et al.]]<br /> :*[[media:Flint_and_Geschwind.pdf | ''Genetics and genomics of psychiatric disease'' Flint and Geschwind]]<br /> <br /> <br /> --&gt;</div>

Alenartowicz https://www.ccn.ucla.edu/wiki/index.php?title=File:Eegfmritalk.pdf&diff=3245 2017-02-01T05:33:40Z

<p>Alenartowicz: </p> <hr /> <div></div>

Alenartowicz https://www.ccn.ucla.edu/wiki/index.php?title=Principles_of_Neuroimaging_B_-_2017&diff=3244 2017-02-01T05:30:30Z

<p>Alenartowicz: /* - Multimodal II Speaker Agatha Lenartowicz */</p> <hr /> <div>Principles of Neuroimaging B, Winter, 2017 - Class Schedule and Syllabus<br /> <br /> == This schedule is a draft only and ''will'' change!==<br /> <br /> :'''[[Principles_of_Neuroimaging_-_2016-2017 | Back to main course page for Principles of Neuroimaging]]'''<br /> <br /> =Course Reading=<br /> ===Required Reading===<br /> :'''Signal Processing for Neuroscientists''' by ''Wim van Drongelen''<br /> ::This can be found as a PDF on scribd.com, for a small fee of $8.99<br /> <br /> ===Supplemental Reading===<br /> :'''Matlab for Neuroscientists'''<br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/88212458/Matlab-Matlab-for-Neuroscientists<br /> <br /> :'''Cartoon Guide to Statistics''' <br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/148072668/Cartoon-Guide-to-Statistics<br /> <br /> :'''NOTE:''' if you subscribe for a Scribd account for a day, you can download as many documents as you like for one fee.<br /> <br /> =Week 1: Magnetic Resonance Imaging: Fundamentals=<br /> <br /> Magnetic Resonance Imaging (MRI) is probably the most influential and most flexible current means of imaging the human brain. It features a vast number of separable contrast mechanisms, and a near ideal combination of non-invasiveness, safety, resolution and metric accuracy. However, it is extraordinarily expensive and has limited temporal resolution, especially for functional studies<br /> <br /> <br /> OUTLINE<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> :*[http://www.cis.rit.edu/htbooks/mri/ These notes] by Joseph Hornak are highly professional and complete coverage of MRI.<br /> :*[http://www.imaios.com/en/e-Courses/e-MRI eMRI] is another excellent online MRI learning resource<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Hahn1950.pdf Erwin Hahn - Spin Echoes: ''Essential reading for the MRI community'']<br /> [[Image:HahnFig1.png]]<br /> ''above'': Figure 1 from Hahn, 1950<br /> <br /> ''Suggested Further Reading''<br /> [[image:PSatSeq.jpg|right]]<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> <br /> ==''Monday 1/9/17'' ==<br /> ===- MR Physics I. ''Speaker'': [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> ==''Wednesday 1/11/17'' ==<br /> ===- MR Physics II. ''Speaker'': [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> =Week 2: MRI Techniques=<br /> ==''Monday 1/16/17'' ==<br /> ===MLK Day No Class===<br /> <br /> ==''Wednesday 1/18/17'' ==<br /> ===- MR Spatial Encoding Q&amp;A. ''Speaker'' Cameron Rodriguez===<br /> <br /> =Week 3: MRI Applications =<br /> ==''Monday 1/23/17'' ==<br /> ===- MR Pulse Sequences and Parameters ''Speaker'' [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> ==''Wednesday 1/25/17'' ==<br /> ===- MR Applications (MR, fMRI, DTI, ASL, Spectroscopy). ''Speaker'' [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> =Week 4: Multimodal Imaging=<br /> ==''Monday 1/30/17 CLASS IN SEMEL 67-415''==<br /> ===- Multimodal I ''Speaker'' Cameron Rodriguez===<br /> <br /> ==''Wednesday 2/1/17'' ==<br /> ===- Multimodal II ''Speaker'' Agatha Lenartowicz===<br /> :*[[media:eegfmritalk.pdf‎ | Lecture Slides]]<br /> <br /> =Week 5: Review =<br /> ==''Monday 2/6/17''==<br /> ===- Q&amp;A Class ===<br /> <br /> ==''Wednesday 2/8/17''==<br /> ===- Midterm Review===<br /> <br /> =MIDTERM=<br /> <br /> =Week 6: Image Processing=<br /> ==''Monday 2/13/17'' ==<br /> ===-Image Processing I. ''Speaker'' Cameron Rodriguez===<br /> <br /> ==''Wednesday 2/15/17'' ==<br /> ===-Image Processing II. ''Speaker'' Agatha Lenartowicz===<br /> <br /> =Week 7: Image Analysis=<br /> ==''Monday 2/20/17'' ==<br /> ===President’s Day Holiday No Class===<br /> <br /> ==''Wednesday 2/22/17'' ==<br /> ===- Analysis I ''Speaker'' Agatha Lenartowicz===<br /> <br /> =Week 8: Image Analysis Cont.=<br /> ==''Monday 2/27/17 CLASS IN SEMEL 67-415'' ==<br /> ===Analysis II ''Speaker'' Agatha Lenartowicz===<br /> <br /> ==''Wednesday 3/1/17'' ==<br /> ===- TBD ''Speaker'' TBD===<br /> <br /> =Week 9: Brain Decoding=<br /> ==''Monday 3/6/17'' ==<br /> ===-ML I: Dimension Reduction, Machine Learning Fundamentals ''Speaker'' Wesley Kerr, Ph.D.===<br /> <br /> ==''Wednesday 3/8/17'' ==<br /> ===- MLII: Machine Learning cont. ''Speaker'' Wesley Kerr, Ph.D.===<br /> <br /> =Week 10: Review=<br /> ==''Monday 3/13/17'' ==<br /> ===-TBD ''Speaker'' TBD===<br /> <br /> ==''Wednesday 3/15/17'' ==<br /> ===-Final Exam Review. ''Speaker'' TBD===<br /> <br /> <br /> <br /> <br /> <br /> &lt;!--<br /> [[Image:ASL.png | right]]<br /> ===- Measurement of Blood Flow by MRI. ''Danny JJ Wang'': [http://www.bri.ucla.edu/people/danny-jiong-jiong-wang-phd]===<br /> Most MRI images in use today are static maps, generally called &quot;structural&quot; images. However, the MRI signal is sensitive to a wide variety of dynamic phenomena. Practitioners typically use the term &quot;functional&quot; to describe images that pick up time-varying signals. The term ''functional MRI'', of course, has been usurped to describe brain activity mapping and, in most cases, refers to BOLD signal effects.<br /> <br /> Measurement of blood ''flow'' in particular is another window into brain activity. Several methods exist to create signal sensitive to blood flow, or blood flow changes. Some label the blood flow based on relaxation effects (Arterial Spin Labeling - ASL - being one important method) and others measure flow by velocity.<br /> <br /> In most cases, MRI is used as a semi quantitative modality. We get accurate spatial metrics but, in general, the image intensities are in arbitrary units. Blood flow is one particular feature that we can measure in native units.<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_perfusion-web.pdf‎ | Dr. Wang's Handout]]<br /> <br /> :*[http://www.cis.rit.edu/htbooks/mri/ These notes] by Joseph Hornak are highly professional and complete coverage of MRI.]<br /> :*[[media:M284B_perfusion-web.pdf‎ | Dr. Wang's Handout]]<br /> [[Image:HahnFig1.png]]<br /> ''above'': Figure 1 from Hahn, 1950<br /> <br /> ''Suggested Further Reading''<br /> :*[http://www.pnas.org/content/89/1/212.long Williams, PNAS 1992]<br /> :*[http://onlinelibrary.wiley.com/doi/10.1002/mrm.1910230106/abstract;jsessionid=E42CC3817A5B7B6BB12B6BA3562C1913.f03t04 Detre, MRM 1992]<br /> :*[http://onlinelibrary.wiley.com/doi/10.1002/mrm.21790/epdf Dai, MRM 2008]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/17969096 Wu, MRM 2007]<br /> :*[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3155947/ Zheng, Neuroimage 2012]<br /> <br /> <br /> <br /> =Week 8: Advanced MRI methods=<br /> ==''Monday 2/22/16'' ==<br /> [[Image:DIffusingSpheres.png | left]]<br /> ===- Diffusion Physics ''Speaker'': [http://radiology.ucla.edu/body.cfm?id=48&amp;action=detail&amp;ref=18 Ben Ellingson]===<br /> ''Required Readings''<br /> Slides should be up soon.<br /> :*[https://www.dropbox.com/s/0feqz7tlojbfgzl/1_DiffusionMRI_Methods_2015.key.pdf?dl=0 Handouts for class on 1/14/15] ''&lt;- New 1/26-2015''<br /> :*[[media:1_DiffusionMRI_Methods_v1 - 2014.pdf | Ben Ellingson Diffusion Slides]] uploaded 3/14/14<br /> ''Suggested Further Reading''<br /> :*[[media:DTIHageman.pdf | Hageman DTI Slides]]<br /> :*[[media:NICourse_DTILecture4Post_11-01-26.pdf | Hageman DTI Notes 2/26/12]]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/15037456?dopt=Citation Diffusion tensor imaging of cerebral white matter: a pictorial review of physics, fiber tract anatomy, and tumor imaging patterns AJNR Am J Neuroradiol. 2004 Mar;25(3):356-69]<br /> <br /> :Sadly, the library does not have a subscription for the journals below (Mark has copies on reserve in his office):<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/8661285?dopt=Citation Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J Magn Reson B. 1996 Jun;111(3):209-19]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/16950152?dopt=Citation Principles of diffusion tensor imaging and its applications to basic neuroscience research. Neuron. 2006 Sep 7;51(5):527-39]<br /> <br /> ==''Wednesday 2/25/16'' ==<br /> [[Image:Callosum.png | right]]<br /> ===- Brain Morphometry. ''Speaker'': [http://www.bmap.ucla.edu/about/peopledetails/roger_woods/ Roger Woods]: ===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_Woods.pdf‎ | Dr. Woods' slides]]<br /> <br /> ''Suggested Further Reading''<br /> :[http://www.ncbi.nlm.nih.gov/pubmed/12667854 *RP Woods, “Characterizing volume and surface deformations in an atlas framework: theory, applications, and implementation.” Neuroimage, 18(3): p. 769-788. 2003]<br /> <br /> =Week 9: MR Spectroscopy=<br /> ==''Monday 2/29/16'' ==<br /> No class<br /> <br /> ==''Wednesday 3/2/16'' ==<br /> === Spectroscopy fundamentals. ''Speaker'': [http://people.healthsciences.ucla.edu/institution/personnel?personnel_id=74852 Joseph O'Neill] ===<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:ONeill2016Spectro.pdf‎ | Dr. O'Neill's slides]]<br /> <br /> ===- Spectroscopic Methods. ''Speaker'': [http://radiology.ucla.edu/body.cfm?id=48&amp;action=detail&amp;ref=65 Albert Thomas] ===<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_AThomas030216.pdf‎ | Dr. Thomas' slides]]<br /> ''Suggested Further Reading''<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/23292856 SA Wijtenburg, LM Rowland, RA Edden and PB Barker, “Reproducibility of brain spectroscopy at 7T using conventional localization and spectral editing techniques.” J Magn Reson Imaging, 38(2): p. 460-467. 2013. PMCID: PMC3620961]<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/20574964 S Lipnick, G Verma, S Ramadan, J Furuyama and MA Thomas, “Echo planar correlated spectroscopic imaging: implementation and pilot evaluation in human calf in vivo.” Magn Reson Med, 64(4): p. 947-956. 2010. PMCID: PMC2946523]<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/22505247 JK Furuyama, NE Wilson, BL Burns, R Nagarajan, DJ Margolis and MA Thomas, “Application of compressed sensing to multidimensional spectroscopic imaging in human prostate.” Magn Reson Med, 67(6): p. 1499-1505. 2012.]<br /> <br /> =Week 10: Multimodal Methods / Course wrap up=<br /> ==''Monday 3/7/16'' ==<br /> [[Image:Kinship.png | right]]<br /> ===- Imaging Genetics. ''Speaker'': [http://www.semel.ucla.edu/bearden-lab Carrie Bearden]: ===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:Bearden_NITP2_2016-3-4.pdf | Dr. Bearden's slides]]<br /> <br /> ''Suggested Further Reading''<br /> :*[[media:Carter-Genomics.pdf | ''Enhancing the Informativeness and Replicability of Imaging Genomics Studies'' Carter et al.]]<br /> :*[[media:Flint_and_Geschwind.pdf | ''Genetics and genomics of psychiatric disease'' Flint and Geschwind]]<br /> <br /> <br /> --&gt;</div>

Alenartowicz https://www.ccn.ucla.edu/wiki/index.php?title=Principles_of_Neuroimaging_B_-_2017&diff=3243 2017-02-01T05:29:30Z

<p>Alenartowicz: /* - Multimodal II Speaker Agatha Lenartowicz */</p> <hr /> <div>Principles of Neuroimaging B, Winter, 2017 - Class Schedule and Syllabus<br /> <br /> == This schedule is a draft only and ''will'' change!==<br /> <br /> :'''[[Principles_of_Neuroimaging_-_2016-2017 | Back to main course page for Principles of Neuroimaging]]'''<br /> <br /> =Course Reading=<br /> ===Required Reading===<br /> :'''Signal Processing for Neuroscientists''' by ''Wim van Drongelen''<br /> ::This can be found as a PDF on scribd.com, for a small fee of $8.99<br /> <br /> ===Supplemental Reading===<br /> :'''Matlab for Neuroscientists'''<br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/88212458/Matlab-Matlab-for-Neuroscientists<br /> <br /> :'''Cartoon Guide to Statistics''' <br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/148072668/Cartoon-Guide-to-Statistics<br /> <br /> :'''NOTE:''' if you subscribe for a Scribd account for a day, you can download as many documents as you like for one fee.<br /> <br /> =Week 1: Magnetic Resonance Imaging: Fundamentals=<br /> <br /> Magnetic Resonance Imaging (MRI) is probably the most influential and most flexible current means of imaging the human brain. It features a vast number of separable contrast mechanisms, and a near ideal combination of non-invasiveness, safety, resolution and metric accuracy. However, it is extraordinarily expensive and has limited temporal resolution, especially for functional studies<br /> <br /> <br /> OUTLINE<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> :*[http://www.cis.rit.edu/htbooks/mri/ These notes] by Joseph Hornak are highly professional and complete coverage of MRI.<br /> :*[http://www.imaios.com/en/e-Courses/e-MRI eMRI] is another excellent online MRI learning resource<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Hahn1950.pdf Erwin Hahn - Spin Echoes: ''Essential reading for the MRI community'']<br /> [[Image:HahnFig1.png]]<br /> ''above'': Figure 1 from Hahn, 1950<br /> <br /> ''Suggested Further Reading''<br /> [[image:PSatSeq.jpg|right]]<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> <br /> ==''Monday 1/9/17'' ==<br /> ===- MR Physics I. ''Speaker'': [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> ==''Wednesday 1/11/17'' ==<br /> ===- MR Physics II. ''Speaker'': [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> =Week 2: MRI Techniques=<br /> ==''Monday 1/16/17'' ==<br /> ===MLK Day No Class===<br /> <br /> ==''Wednesday 1/18/17'' ==<br /> ===- MR Spatial Encoding Q&amp;A. ''Speaker'' Cameron Rodriguez===<br /> <br /> =Week 3: MRI Applications =<br /> ==''Monday 1/23/17'' ==<br /> ===- MR Pulse Sequences and Parameters ''Speaker'' [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> ==''Wednesday 1/25/17'' ==<br /> ===- MR Applications (MR, fMRI, DTI, ASL, Spectroscopy). ''Speaker'' [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> =Week 4: Multimodal Imaging=<br /> ==''Monday 1/30/17 CLASS IN SEMEL 67-415''==<br /> ===- Multimodal I ''Speaker'' Cameron Rodriguez===<br /> <br /> ==''Wednesday 2/1/17'' ==<br /> ===- Multimodal II ''Speaker'' Agatha Lenartowicz===<br /> [[eegfmritalk.pdf: Lecture Slides]]<br /> <br /> =Week 5: Review =<br /> ==''Monday 2/6/17''==<br /> ===- Q&amp;A Class ===<br /> <br /> ==''Wednesday 2/8/17''==<br /> ===- Midterm Review===<br /> <br /> =MIDTERM=<br /> <br /> =Week 6: Image Processing=<br /> ==''Monday 2/13/17'' ==<br /> ===-Image Processing I. ''Speaker'' Cameron Rodriguez===<br /> <br /> ==''Wednesday 2/15/17'' ==<br /> ===-Image Processing II. ''Speaker'' Agatha Lenartowicz===<br /> <br /> =Week 7: Image Analysis=<br /> ==''Monday 2/20/17'' ==<br /> ===President’s Day Holiday No Class===<br /> <br /> ==''Wednesday 2/22/17'' ==<br /> ===- Analysis I ''Speaker'' Agatha Lenartowicz===<br /> <br /> =Week 8: Image Analysis Cont.=<br /> ==''Monday 2/27/17 CLASS IN SEMEL 67-415'' ==<br /> ===Analysis II ''Speaker'' Agatha Lenartowicz===<br /> <br /> ==''Wednesday 3/1/17'' ==<br /> ===- TBD ''Speaker'' TBD===<br /> <br /> =Week 9: Brain Decoding=<br /> ==''Monday 3/6/17'' ==<br /> ===-ML I: Dimension Reduction, Machine Learning Fundamentals ''Speaker'' Wesley Kerr, Ph.D.===<br /> <br /> ==''Wednesday 3/8/17'' ==<br /> ===- MLII: Machine Learning cont. ''Speaker'' Wesley Kerr, Ph.D.===<br /> <br /> =Week 10: Review=<br /> ==''Monday 3/13/17'' ==<br /> ===-TBD ''Speaker'' TBD===<br /> <br /> ==''Wednesday 3/15/17'' ==<br /> ===-Final Exam Review. ''Speaker'' TBD===<br /> <br /> <br /> <br /> <br /> <br /> &lt;!--<br /> [[Image:ASL.png | right]]<br /> ===- Measurement of Blood Flow by MRI. ''Danny JJ Wang'': [http://www.bri.ucla.edu/people/danny-jiong-jiong-wang-phd]===<br /> Most MRI images in use today are static maps, generally called &quot;structural&quot; images. However, the MRI signal is sensitive to a wide variety of dynamic phenomena. Practitioners typically use the term &quot;functional&quot; to describe images that pick up time-varying signals. The term ''functional MRI'', of course, has been usurped to describe brain activity mapping and, in most cases, refers to BOLD signal effects.<br /> <br /> Measurement of blood ''flow'' in particular is another window into brain activity. Several methods exist to create signal sensitive to blood flow, or blood flow changes. Some label the blood flow based on relaxation effects (Arterial Spin Labeling - ASL - being one important method) and others measure flow by velocity.<br /> <br /> In most cases, MRI is used as a semi quantitative modality. We get accurate spatial metrics but, in general, the image intensities are in arbitrary units. Blood flow is one particular feature that we can measure in native units.<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_perfusion-web.pdf‎ | Dr. Wang's Handout]]<br /> <br /> :*[http://www.cis.rit.edu/htbooks/mri/ These notes] by Joseph Hornak are highly professional and complete coverage of MRI.]<br /> :*[[media:M284B_perfusion-web.pdf‎ | Dr. Wang's Handout]]<br /> [[Image:HahnFig1.png]]<br /> ''above'': Figure 1 from Hahn, 1950<br /> <br /> ''Suggested Further Reading''<br /> :*[http://www.pnas.org/content/89/1/212.long Williams, PNAS 1992]<br /> :*[http://onlinelibrary.wiley.com/doi/10.1002/mrm.1910230106/abstract;jsessionid=E42CC3817A5B7B6BB12B6BA3562C1913.f03t04 Detre, MRM 1992]<br /> :*[http://onlinelibrary.wiley.com/doi/10.1002/mrm.21790/epdf Dai, MRM 2008]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/17969096 Wu, MRM 2007]<br /> :*[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3155947/ Zheng, Neuroimage 2012]<br /> <br /> <br /> <br /> =Week 8: Advanced MRI methods=<br /> ==''Monday 2/22/16'' ==<br /> [[Image:DIffusingSpheres.png | left]]<br /> ===- Diffusion Physics ''Speaker'': [http://radiology.ucla.edu/body.cfm?id=48&amp;action=detail&amp;ref=18 Ben Ellingson]===<br /> ''Required Readings''<br /> Slides should be up soon.<br /> :*[https://www.dropbox.com/s/0feqz7tlojbfgzl/1_DiffusionMRI_Methods_2015.key.pdf?dl=0 Handouts for class on 1/14/15] ''&lt;- New 1/26-2015''<br /> :*[[media:1_DiffusionMRI_Methods_v1 - 2014.pdf | Ben Ellingson Diffusion Slides]] uploaded 3/14/14<br /> ''Suggested Further Reading''<br /> :*[[media:DTIHageman.pdf | Hageman DTI Slides]]<br /> :*[[media:NICourse_DTILecture4Post_11-01-26.pdf | Hageman DTI Notes 2/26/12]]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/15037456?dopt=Citation Diffusion tensor imaging of cerebral white matter: a pictorial review of physics, fiber tract anatomy, and tumor imaging patterns AJNR Am J Neuroradiol. 2004 Mar;25(3):356-69]<br /> <br /> :Sadly, the library does not have a subscription for the journals below (Mark has copies on reserve in his office):<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/8661285?dopt=Citation Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J Magn Reson B. 1996 Jun;111(3):209-19]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/16950152?dopt=Citation Principles of diffusion tensor imaging and its applications to basic neuroscience research. Neuron. 2006 Sep 7;51(5):527-39]<br /> <br /> ==''Wednesday 2/25/16'' ==<br /> [[Image:Callosum.png | right]]<br /> ===- Brain Morphometry. ''Speaker'': [http://www.bmap.ucla.edu/about/peopledetails/roger_woods/ Roger Woods]: ===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_Woods.pdf‎ | Dr. Woods' slides]]<br /> <br /> ''Suggested Further Reading''<br /> :[http://www.ncbi.nlm.nih.gov/pubmed/12667854 *RP Woods, “Characterizing volume and surface deformations in an atlas framework: theory, applications, and implementation.” Neuroimage, 18(3): p. 769-788. 2003]<br /> <br /> =Week 9: MR Spectroscopy=<br /> ==''Monday 2/29/16'' ==<br /> No class<br /> <br /> ==''Wednesday 3/2/16'' ==<br /> === Spectroscopy fundamentals. ''Speaker'': [http://people.healthsciences.ucla.edu/institution/personnel?personnel_id=74852 Joseph O'Neill] ===<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:ONeill2016Spectro.pdf‎ | Dr. O'Neill's slides]]<br /> <br /> ===- Spectroscopic Methods. ''Speaker'': [http://radiology.ucla.edu/body.cfm?id=48&amp;action=detail&amp;ref=65 Albert Thomas] ===<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_AThomas030216.pdf‎ | Dr. Thomas' slides]]<br /> ''Suggested Further Reading''<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/23292856 SA Wijtenburg, LM Rowland, RA Edden and PB Barker, “Reproducibility of brain spectroscopy at 7T using conventional localization and spectral editing techniques.” J Magn Reson Imaging, 38(2): p. 460-467. 2013. PMCID: PMC3620961]<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/20574964 S Lipnick, G Verma, S Ramadan, J Furuyama and MA Thomas, “Echo planar correlated spectroscopic imaging: implementation and pilot evaluation in human calf in vivo.” Magn Reson Med, 64(4): p. 947-956. 2010. PMCID: PMC2946523]<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/22505247 JK Furuyama, NE Wilson, BL Burns, R Nagarajan, DJ Margolis and MA Thomas, “Application of compressed sensing to multidimensional spectroscopic imaging in human prostate.” Magn Reson Med, 67(6): p. 1499-1505. 2012.]<br /> <br /> =Week 10: Multimodal Methods / Course wrap up=<br /> ==''Monday 3/7/16'' ==<br /> [[Image:Kinship.png | right]]<br /> ===- Imaging Genetics. ''Speaker'': [http://www.semel.ucla.edu/bearden-lab Carrie Bearden]: ===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:Bearden_NITP2_2016-3-4.pdf | Dr. Bearden's slides]]<br /> <br /> ''Suggested Further Reading''<br /> :*[[media:Carter-Genomics.pdf | ''Enhancing the Informativeness and Replicability of Imaging Genomics Studies'' Carter et al.]]<br /> :*[[media:Flint_and_Geschwind.pdf | ''Genetics and genomics of psychiatric disease'' Flint and Geschwind]]<br /> <br /> <br /> --&gt;</div>

Alenartowicz https://www.ccn.ucla.edu/wiki/index.php?title=Principles_of_Neuroimaging_B_-_2017&diff=3242 2017-02-01T05:28:37Z

<p>Alenartowicz: </p> <hr /> <div>Principles of Neuroimaging B, Winter, 2017 - Class Schedule and Syllabus<br /> <br /> == This schedule is a draft only and ''will'' change!==<br /> <br /> :'''[[Principles_of_Neuroimaging_-_2016-2017 | Back to main course page for Principles of Neuroimaging]]'''<br /> <br /> =Course Reading=<br /> ===Required Reading===<br /> :'''Signal Processing for Neuroscientists''' by ''Wim van Drongelen''<br /> ::This can be found as a PDF on scribd.com, for a small fee of $8.99<br /> <br /> ===Supplemental Reading===<br /> :'''Matlab for Neuroscientists'''<br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/88212458/Matlab-Matlab-for-Neuroscientists<br /> <br /> :'''Cartoon Guide to Statistics''' <br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/148072668/Cartoon-Guide-to-Statistics<br /> <br /> :'''NOTE:''' if you subscribe for a Scribd account for a day, you can download as many documents as you like for one fee.<br /> <br /> =Week 1: Magnetic Resonance Imaging: Fundamentals=<br /> <br /> Magnetic Resonance Imaging (MRI) is probably the most influential and most flexible current means of imaging the human brain. It features a vast number of separable contrast mechanisms, and a near ideal combination of non-invasiveness, safety, resolution and metric accuracy. However, it is extraordinarily expensive and has limited temporal resolution, especially for functional studies<br /> <br /> <br /> OUTLINE<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> :*[http://www.cis.rit.edu/htbooks/mri/ These notes] by Joseph Hornak are highly professional and complete coverage of MRI.<br /> :*[http://www.imaios.com/en/e-Courses/e-MRI eMRI] is another excellent online MRI learning resource<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Hahn1950.pdf Erwin Hahn - Spin Echoes: ''Essential reading for the MRI community'']<br /> [[Image:HahnFig1.png]]<br /> ''above'': Figure 1 from Hahn, 1950<br /> <br /> ''Suggested Further Reading''<br /> [[image:PSatSeq.jpg|right]]<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> <br /> ==''Monday 1/9/17'' ==<br /> ===- MR Physics I. ''Speaker'': [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> ==''Wednesday 1/11/17'' ==<br /> ===- MR Physics II. ''Speaker'': [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> =Week 2: MRI Techniques=<br /> ==''Monday 1/16/17'' ==<br /> ===MLK Day No Class===<br /> <br /> ==''Wednesday 1/18/17'' ==<br /> ===- MR Spatial Encoding Q&amp;A. ''Speaker'' Cameron Rodriguez===<br /> <br /> =Week 3: MRI Applications =<br /> ==''Monday 1/23/17'' ==<br /> ===- MR Pulse Sequences and Parameters ''Speaker'' [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> ==''Wednesday 1/25/17'' ==<br /> ===- MR Applications (MR, fMRI, DTI, ASL, Spectroscopy). ''Speaker'' [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> =Week 4: Multimodal Imaging=<br /> ==''Monday 1/30/17 CLASS IN SEMEL 67-415''==<br /> ===- Multimodal I ''Speaker'' Cameron Rodriguez===<br /> <br /> ==''Wednesday 2/1/17'' ==<br /> ===- Multimodal II ''Speaker'' Agatha Lenartowicz===<br /> [[Lecture Slides: eegfmritalk.pdf]]<br /> <br /> =Week 5: Review =<br /> ==''Monday 2/6/17''==<br /> ===- Q&amp;A Class ===<br /> <br /> ==''Wednesday 2/8/17''==<br /> ===- Midterm Review===<br /> <br /> =MIDTERM=<br /> <br /> =Week 6: Image Processing=<br /> ==''Monday 2/13/17'' ==<br /> ===-Image Processing I. ''Speaker'' Cameron Rodriguez===<br /> <br /> ==''Wednesday 2/15/17'' ==<br /> ===-Image Processing II. ''Speaker'' Agatha Lenartowicz===<br /> <br /> =Week 7: Image Analysis=<br /> ==''Monday 2/20/17'' ==<br /> ===President’s Day Holiday No Class===<br /> <br /> ==''Wednesday 2/22/17'' ==<br /> ===- Analysis I ''Speaker'' Agatha Lenartowicz===<br /> <br /> =Week 8: Image Analysis Cont.=<br /> ==''Monday 2/27/17 CLASS IN SEMEL 67-415'' ==<br /> ===Analysis II ''Speaker'' Agatha Lenartowicz===<br /> <br /> ==''Wednesday 3/1/17'' ==<br /> ===- TBD ''Speaker'' TBD===<br /> <br /> =Week 9: Brain Decoding=<br /> ==''Monday 3/6/17'' ==<br /> ===-ML I: Dimension Reduction, Machine Learning Fundamentals ''Speaker'' Wesley Kerr, Ph.D.===<br /> <br /> ==''Wednesday 3/8/17'' ==<br /> ===- MLII: Machine Learning cont. ''Speaker'' Wesley Kerr, Ph.D.===<br /> <br /> =Week 10: Review=<br /> ==''Monday 3/13/17'' ==<br /> ===-TBD ''Speaker'' TBD===<br /> <br /> ==''Wednesday 3/15/17'' ==<br /> ===-Final Exam Review. ''Speaker'' TBD===<br /> <br /> <br /> <br /> <br /> <br /> &lt;!--<br /> [[Image:ASL.png | right]]<br /> ===- Measurement of Blood Flow by MRI. ''Danny JJ Wang'': [http://www.bri.ucla.edu/people/danny-jiong-jiong-wang-phd]===<br /> Most MRI images in use today are static maps, generally called &quot;structural&quot; images. However, the MRI signal is sensitive to a wide variety of dynamic phenomena. Practitioners typically use the term &quot;functional&quot; to describe images that pick up time-varying signals. The term ''functional MRI'', of course, has been usurped to describe brain activity mapping and, in most cases, refers to BOLD signal effects.<br /> <br /> Measurement of blood ''flow'' in particular is another window into brain activity. Several methods exist to create signal sensitive to blood flow, or blood flow changes. Some label the blood flow based on relaxation effects (Arterial Spin Labeling - ASL - being one important method) and others measure flow by velocity.<br /> <br /> In most cases, MRI is used as a semi quantitative modality. We get accurate spatial metrics but, in general, the image intensities are in arbitrary units. Blood flow is one particular feature that we can measure in native units.<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_perfusion-web.pdf‎ | Dr. Wang's Handout]]<br /> <br /> :*[http://www.cis.rit.edu/htbooks/mri/ These notes] by Joseph Hornak are highly professional and complete coverage of MRI.]<br /> :*[[media:M284B_perfusion-web.pdf‎ | Dr. Wang's Handout]]<br /> [[Image:HahnFig1.png]]<br /> ''above'': Figure 1 from Hahn, 1950<br /> <br /> ''Suggested Further Reading''<br /> :*[http://www.pnas.org/content/89/1/212.long Williams, PNAS 1992]<br /> :*[http://onlinelibrary.wiley.com/doi/10.1002/mrm.1910230106/abstract;jsessionid=E42CC3817A5B7B6BB12B6BA3562C1913.f03t04 Detre, MRM 1992]<br /> :*[http://onlinelibrary.wiley.com/doi/10.1002/mrm.21790/epdf Dai, MRM 2008]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/17969096 Wu, MRM 2007]<br /> :*[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3155947/ Zheng, Neuroimage 2012]<br /> <br /> <br /> <br /> =Week 8: Advanced MRI methods=<br /> ==''Monday 2/22/16'' ==<br /> [[Image:DIffusingSpheres.png | left]]<br /> ===- Diffusion Physics ''Speaker'': [http://radiology.ucla.edu/body.cfm?id=48&amp;action=detail&amp;ref=18 Ben Ellingson]===<br /> ''Required Readings''<br /> Slides should be up soon.<br /> :*[https://www.dropbox.com/s/0feqz7tlojbfgzl/1_DiffusionMRI_Methods_2015.key.pdf?dl=0 Handouts for class on 1/14/15] ''&lt;- New 1/26-2015''<br /> :*[[media:1_DiffusionMRI_Methods_v1 - 2014.pdf | Ben Ellingson Diffusion Slides]] uploaded 3/14/14<br /> ''Suggested Further Reading''<br /> :*[[media:DTIHageman.pdf | Hageman DTI Slides]]<br /> :*[[media:NICourse_DTILecture4Post_11-01-26.pdf | Hageman DTI Notes 2/26/12]]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/15037456?dopt=Citation Diffusion tensor imaging of cerebral white matter: a pictorial review of physics, fiber tract anatomy, and tumor imaging patterns AJNR Am J Neuroradiol. 2004 Mar;25(3):356-69]<br /> <br /> :Sadly, the library does not have a subscription for the journals below (Mark has copies on reserve in his office):<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/8661285?dopt=Citation Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J Magn Reson B. 1996 Jun;111(3):209-19]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/16950152?dopt=Citation Principles of diffusion tensor imaging and its applications to basic neuroscience research. Neuron. 2006 Sep 7;51(5):527-39]<br /> <br /> ==''Wednesday 2/25/16'' ==<br /> [[Image:Callosum.png | right]]<br /> ===- Brain Morphometry. ''Speaker'': [http://www.bmap.ucla.edu/about/peopledetails/roger_woods/ Roger Woods]: ===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_Woods.pdf‎ | Dr. Woods' slides]]<br /> <br /> ''Suggested Further Reading''<br /> :[http://www.ncbi.nlm.nih.gov/pubmed/12667854 *RP Woods, “Characterizing volume and surface deformations in an atlas framework: theory, applications, and implementation.” Neuroimage, 18(3): p. 769-788. 2003]<br /> <br /> =Week 9: MR Spectroscopy=<br /> ==''Monday 2/29/16'' ==<br /> No class<br /> <br /> ==''Wednesday 3/2/16'' ==<br /> === Spectroscopy fundamentals. ''Speaker'': [http://people.healthsciences.ucla.edu/institution/personnel?personnel_id=74852 Joseph O'Neill] ===<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:ONeill2016Spectro.pdf‎ | Dr. O'Neill's slides]]<br /> <br /> ===- Spectroscopic Methods. ''Speaker'': [http://radiology.ucla.edu/body.cfm?id=48&amp;action=detail&amp;ref=65 Albert Thomas] ===<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_AThomas030216.pdf‎ | Dr. Thomas' slides]]<br /> ''Suggested Further Reading''<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/23292856 SA Wijtenburg, LM Rowland, RA Edden and PB Barker, “Reproducibility of brain spectroscopy at 7T using conventional localization and spectral editing techniques.” J Magn Reson Imaging, 38(2): p. 460-467. 2013. PMCID: PMC3620961]<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/20574964 S Lipnick, G Verma, S Ramadan, J Furuyama and MA Thomas, “Echo planar correlated spectroscopic imaging: implementation and pilot evaluation in human calf in vivo.” Magn Reson Med, 64(4): p. 947-956. 2010. PMCID: PMC2946523]<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/22505247 JK Furuyama, NE Wilson, BL Burns, R Nagarajan, DJ Margolis and MA Thomas, “Application of compressed sensing to multidimensional spectroscopic imaging in human prostate.” Magn Reson Med, 67(6): p. 1499-1505. 2012.]<br /> <br /> =Week 10: Multimodal Methods / Course wrap up=<br /> ==''Monday 3/7/16'' ==<br /> [[Image:Kinship.png | right]]<br /> ===- Imaging Genetics. ''Speaker'': [http://www.semel.ucla.edu/bearden-lab Carrie Bearden]: ===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:Bearden_NITP2_2016-3-4.pdf | Dr. Bearden's slides]]<br /> <br /> ''Suggested Further Reading''<br /> :*[[media:Carter-Genomics.pdf | ''Enhancing the Informativeness and Replicability of Imaging Genomics Studies'' Carter et al.]]<br /> :*[[media:Flint_and_Geschwind.pdf | ''Genetics and genomics of psychiatric disease'' Flint and Geschwind]]<br /> <br /> <br /> --&gt;</div>

Alenartowicz https://www.ccn.ucla.edu/wiki/index.php?title=Principles_of_Neuroimaging_B_-_2017&diff=3239 2017-01-31T19:33:50Z

<p>Alenartowicz: /* Analysis II &quot;Speaker&quot; Agatha Lenartowicz */</p> <hr /> <div>Principles of Neuroimaging B, Winter, 2017 - Class Schedule and Syllabus<br /> <br /> == This schedule is a draft only and ''will'' change!==<br /> <br /> :'''[[Principles_of_Neuroimaging_-_2016-2017 | Back to main course page for Principles of Neuroimaging]]'''<br /> <br /> =Course Reading=<br /> ===Required Reading===<br /> :'''Signal Processing for Neuroscientists''' by ''Wim van Drongelen''<br /> ::This can be found as a PDF on scribd.com, for a small fee of $8.99<br /> <br /> ===Supplemental Reading===<br /> :'''Matlab for Neuroscientists'''<br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/88212458/Matlab-Matlab-for-Neuroscientists<br /> <br /> :'''Cartoon Guide to Statistics''' <br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/148072668/Cartoon-Guide-to-Statistics<br /> <br /> :'''NOTE:''' if you subscribe for a Scribd account for a day, you can download as many documents as you like for one fee.<br /> <br /> =Week 1: Magnetic Resonance Imaging: Fundamentals=<br /> <br /> Magnetic Resonance Imaging (MRI) is probably the most influential and most flexible current means of imaging the human brain. It features a vast number of separable contrast mechanisms, and a near ideal combination of non-invasiveness, safety, resolution and metric accuracy. However, it is extraordinarily expensive and has limited temporal resolution, especially for functional studies<br /> <br /> <br /> OUTLINE<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> :*[http://www.cis.rit.edu/htbooks/mri/ These notes] by Joseph Hornak are highly professional and complete coverage of MRI.<br /> :*[http://www.imaios.com/en/e-Courses/e-MRI eMRI] is another excellent online MRI learning resource<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Hahn1950.pdf Erwin Hahn - Spin Echoes: ''Essential reading for the MRI community'']<br /> [[Image:HahnFig1.png]]<br /> ''above'': Figure 1 from Hahn, 1950<br /> <br /> ''Suggested Further Reading''<br /> [[image:PSatSeq.jpg|right]]<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> <br /> ==''Monday 1/9/17'' ==<br /> ===- MR Physics I. ''Speaker'': [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> ==''Wednesday 1/11/17'' ==<br /> ===- MR Physics II. ''Speaker'': [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> =Week 2: MRI Techniques=<br /> ==''Monday 1/16/17'' ==<br /> ===MLK Day No Class===<br /> <br /> ==''Wednesday 1/18/17'' ==<br /> ===- MR Spatial Encoding Q&amp;A. ''Speaker'' Cameron Rodriguez===<br /> <br /> =Week 3: MRI Applications =<br /> ==''Monday 1/23/17'' ==<br /> ===- MR Pulse Sequences and Parameters ''Speaker'' [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> ==''Wednesday 1/25/17'' ==<br /> ===- MR Applications (MR, fMRI, DTI, ASL, Spectroscopy). ''Speaker'' [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> =Week 4: Multimodal Imaging=<br /> ==''Monday 1/30/17 CLASS IN SEMEL 67-415''==<br /> ===- Multimodal I ''Speaker'' Cameron Rodriguez===<br /> <br /> ==''Wednesday 2/1/17'' ==<br /> ===- Multimodal II ''Speaker'' Agatha Lenartowicz===<br /> <br /> =Week 5: Review =<br /> ==''Monday 2/6/17''==<br /> ===- Q&amp;A Class ===<br /> <br /> ==''Wednesday 2/8/17''==<br /> ===- Midterm Review===<br /> <br /> =MIDTERM=<br /> <br /> =Week 6: Image Processing=<br /> ==''Monday 2/13/17'' ==<br /> ===-Image Processing I. ''Speaker'' Cameron Rodriguez===<br /> <br /> ==''Wednesday 2/15/17'' ==<br /> ===-Image Processing II. ''Speaker'' Agatha Lenartowicz===<br /> <br /> =Week 7: Image Analysis=<br /> ==''Monday 2/20/17'' ==<br /> ===President’s Day Holiday No Class===<br /> <br /> ==''Wednesday 2/22/17'' ==<br /> ===- Analysis I ''Speaker'' Agatha Lenartowicz===<br /> <br /> =Week 8: Image Analysis Cont.=<br /> ==''Monday 2/27/17 CLASS IN SEMEL 67-415'' ==<br /> ===Analysis II ''Speaker'' Agatha Lenartowicz===<br /> <br /> ==''Wednesday 3/1/17'' ==<br /> ===- TBD ''Speaker'' TBD===<br /> <br /> =Week 9: Brain Decoding=<br /> ==''Monday 3/6/17'' ==<br /> ===-ML I: Dimension Reduction, Machine Learning Fundamentals ''Speaker'' Wesley Kerr, Ph.D.===<br /> <br /> ==''Wednesday 3/8/17'' ==<br /> ===- MLII: Machine Learning cont. ''Speaker'' Wesley Kerr, Ph.D.===<br /> <br /> =Week 10: Review=<br /> ==''Monday 3/13/17'' ==<br /> ===-TBD ''Speaker'' TBD===<br /> <br /> ==''Wednesday 3/15/17'' ==<br /> ===-Final Exam Review. ''Speaker'' TBD===<br /> <br /> <br /> <br /> <br /> <br /> &lt;!--<br /> [[Image:ASL.png | right]]<br /> ===- Measurement of Blood Flow by MRI. ''Danny JJ Wang'': [http://www.bri.ucla.edu/people/danny-jiong-jiong-wang-phd]===<br /> Most MRI images in use today are static maps, generally called &quot;structural&quot; images. However, the MRI signal is sensitive to a wide variety of dynamic phenomena. Practitioners typically use the term &quot;functional&quot; to describe images that pick up time-varying signals. The term ''functional MRI'', of course, has been usurped to describe brain activity mapping and, in most cases, refers to BOLD signal effects.<br /> <br /> Measurement of blood ''flow'' in particular is another window into brain activity. Several methods exist to create signal sensitive to blood flow, or blood flow changes. Some label the blood flow based on relaxation effects (Arterial Spin Labeling - ASL - being one important method) and others measure flow by velocity.<br /> <br /> In most cases, MRI is used as a semi quantitative modality. We get accurate spatial metrics but, in general, the image intensities are in arbitrary units. Blood flow is one particular feature that we can measure in native units.<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_perfusion-web.pdf‎ | Dr. Wang's Handout]]<br /> <br /> :*[http://www.cis.rit.edu/htbooks/mri/ These notes] by Joseph Hornak are highly professional and complete coverage of MRI.]<br /> :*[[media:M284B_perfusion-web.pdf‎ | Dr. Wang's Handout]]<br /> [[Image:HahnFig1.png]]<br /> ''above'': Figure 1 from Hahn, 1950<br /> <br /> ''Suggested Further Reading''<br /> :*[http://www.pnas.org/content/89/1/212.long Williams, PNAS 1992]<br /> :*[http://onlinelibrary.wiley.com/doi/10.1002/mrm.1910230106/abstract;jsessionid=E42CC3817A5B7B6BB12B6BA3562C1913.f03t04 Detre, MRM 1992]<br /> :*[http://onlinelibrary.wiley.com/doi/10.1002/mrm.21790/epdf Dai, MRM 2008]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/17969096 Wu, MRM 2007]<br /> :*[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3155947/ Zheng, Neuroimage 2012]<br /> <br /> <br /> <br /> =Week 8: Advanced MRI methods=<br /> ==''Monday 2/22/16'' ==<br /> [[Image:DIffusingSpheres.png | left]]<br /> ===- Diffusion Physics ''Speaker'': [http://radiology.ucla.edu/body.cfm?id=48&amp;action=detail&amp;ref=18 Ben Ellingson]===<br /> ''Required Readings''<br /> Slides should be up soon.<br /> :*[https://www.dropbox.com/s/0feqz7tlojbfgzl/1_DiffusionMRI_Methods_2015.key.pdf?dl=0 Handouts for class on 1/14/15] ''&lt;- New 1/26-2015''<br /> :*[[media:1_DiffusionMRI_Methods_v1 - 2014.pdf | Ben Ellingson Diffusion Slides]] uploaded 3/14/14<br /> ''Suggested Further Reading''<br /> :*[[media:DTIHageman.pdf | Hageman DTI Slides]]<br /> :*[[media:NICourse_DTILecture4Post_11-01-26.pdf | Hageman DTI Notes 2/26/12]]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/15037456?dopt=Citation Diffusion tensor imaging of cerebral white matter: a pictorial review of physics, fiber tract anatomy, and tumor imaging patterns AJNR Am J Neuroradiol. 2004 Mar;25(3):356-69]<br /> <br /> :Sadly, the library does not have a subscription for the journals below (Mark has copies on reserve in his office):<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/8661285?dopt=Citation Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J Magn Reson B. 1996 Jun;111(3):209-19]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/16950152?dopt=Citation Principles of diffusion tensor imaging and its applications to basic neuroscience research. Neuron. 2006 Sep 7;51(5):527-39]<br /> <br /> ==''Wednesday 2/25/16'' ==<br /> [[Image:Callosum.png | right]]<br /> ===- Brain Morphometry. ''Speaker'': [http://www.bmap.ucla.edu/about/peopledetails/roger_woods/ Roger Woods]: ===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_Woods.pdf‎ | Dr. Woods' slides]]<br /> <br /> ''Suggested Further Reading''<br /> :[http://www.ncbi.nlm.nih.gov/pubmed/12667854 *RP Woods, “Characterizing volume and surface deformations in an atlas framework: theory, applications, and implementation.” Neuroimage, 18(3): p. 769-788. 2003]<br /> <br /> =Week 9: MR Spectroscopy=<br /> ==''Monday 2/29/16'' ==<br /> No class<br /> <br /> ==''Wednesday 3/2/16'' ==<br /> === Spectroscopy fundamentals. ''Speaker'': [http://people.healthsciences.ucla.edu/institution/personnel?personnel_id=74852 Joseph O'Neill] ===<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:ONeill2016Spectro.pdf‎ | Dr. O'Neill's slides]]<br /> <br /> ===- Spectroscopic Methods. ''Speaker'': [http://radiology.ucla.edu/body.cfm?id=48&amp;action=detail&amp;ref=65 Albert Thomas] ===<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_AThomas030216.pdf‎ | Dr. Thomas' slides]]<br /> ''Suggested Further Reading''<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/23292856 SA Wijtenburg, LM Rowland, RA Edden and PB Barker, “Reproducibility of brain spectroscopy at 7T using conventional localization and spectral editing techniques.” J Magn Reson Imaging, 38(2): p. 460-467. 2013. PMCID: PMC3620961]<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/20574964 S Lipnick, G Verma, S Ramadan, J Furuyama and MA Thomas, “Echo planar correlated spectroscopic imaging: implementation and pilot evaluation in human calf in vivo.” Magn Reson Med, 64(4): p. 947-956. 2010. PMCID: PMC2946523]<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/22505247 JK Furuyama, NE Wilson, BL Burns, R Nagarajan, DJ Margolis and MA Thomas, “Application of compressed sensing to multidimensional spectroscopic imaging in human prostate.” Magn Reson Med, 67(6): p. 1499-1505. 2012.]<br /> <br /> =Week 10: Multimodal Methods / Course wrap up=<br /> ==''Monday 3/7/16'' ==<br /> [[Image:Kinship.png | right]]<br /> ===- Imaging Genetics. ''Speaker'': [http://www.semel.ucla.edu/bearden-lab Carrie Bearden]: ===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:Bearden_NITP2_2016-3-4.pdf | Dr. Bearden's slides]]<br /> <br /> ''Suggested Further Reading''<br /> :*[[media:Carter-Genomics.pdf | ''Enhancing the Informativeness and Replicability of Imaging Genomics Studies'' Carter et al.]]<br /> :*[[media:Flint_and_Geschwind.pdf | ''Genetics and genomics of psychiatric disease'' Flint and Geschwind]]<br /> <br /> <br /> --&gt;</div>

Alenartowicz https://www.ccn.ucla.edu/wiki/index.php?title=Principles_of_Neuroimaging_B_-_2017&diff=3238 2017-01-31T19:33:18Z

<p>Alenartowicz: /* - MR Spatial Encoding Q&amp;A. &quot;Speaker&quot; Cameron Rodriguez */</p> <hr /> <div>Principles of Neuroimaging B, Winter, 2017 - Class Schedule and Syllabus<br /> <br /> == This schedule is a draft only and ''will'' change!==<br /> <br /> :'''[[Principles_of_Neuroimaging_-_2016-2017 | Back to main course page for Principles of Neuroimaging]]'''<br /> <br /> =Course Reading=<br /> ===Required Reading===<br /> :'''Signal Processing for Neuroscientists''' by ''Wim van Drongelen''<br /> ::This can be found as a PDF on scribd.com, for a small fee of $8.99<br /> <br /> ===Supplemental Reading===<br /> :'''Matlab for Neuroscientists'''<br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/88212458/Matlab-Matlab-for-Neuroscientists<br /> <br /> :'''Cartoon Guide to Statistics''' <br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/148072668/Cartoon-Guide-to-Statistics<br /> <br /> :'''NOTE:''' if you subscribe for a Scribd account for a day, you can download as many documents as you like for one fee.<br /> <br /> =Week 1: Magnetic Resonance Imaging: Fundamentals=<br /> <br /> Magnetic Resonance Imaging (MRI) is probably the most influential and most flexible current means of imaging the human brain. It features a vast number of separable contrast mechanisms, and a near ideal combination of non-invasiveness, safety, resolution and metric accuracy. However, it is extraordinarily expensive and has limited temporal resolution, especially for functional studies<br /> <br /> <br /> OUTLINE<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> :*[http://www.cis.rit.edu/htbooks/mri/ These notes] by Joseph Hornak are highly professional and complete coverage of MRI.<br /> :*[http://www.imaios.com/en/e-Courses/e-MRI eMRI] is another excellent online MRI learning resource<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Hahn1950.pdf Erwin Hahn - Spin Echoes: ''Essential reading for the MRI community'']<br /> [[Image:HahnFig1.png]]<br /> ''above'': Figure 1 from Hahn, 1950<br /> <br /> ''Suggested Further Reading''<br /> [[image:PSatSeq.jpg|right]]<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> <br /> ==''Monday 1/9/17'' ==<br /> ===- MR Physics I. ''Speaker'': [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> ==''Wednesday 1/11/17'' ==<br /> ===- MR Physics II. ''Speaker'': [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> =Week 2: MRI Techniques=<br /> ==''Monday 1/16/17'' ==<br /> ===MLK Day No Class===<br /> <br /> ==''Wednesday 1/18/17'' ==<br /> ===- MR Spatial Encoding Q&amp;A. ''Speaker'' Cameron Rodriguez===<br /> <br /> =Week 3: MRI Applications =<br /> ==''Monday 1/23/17'' ==<br /> ===- MR Pulse Sequences and Parameters ''Speaker'' [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> ==''Wednesday 1/25/17'' ==<br /> ===- MR Applications (MR, fMRI, DTI, ASL, Spectroscopy). ''Speaker'' [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> =Week 4: Multimodal Imaging=<br /> ==''Monday 1/30/17 CLASS IN SEMEL 67-415''==<br /> ===- Multimodal I ''Speaker'' Cameron Rodriguez===<br /> <br /> ==''Wednesday 2/1/17'' ==<br /> ===- Multimodal II ''Speaker'' Agatha Lenartowicz===<br /> <br /> =Week 5: Review =<br /> ==''Monday 2/6/17''==<br /> ===- Q&amp;A Class ===<br /> <br /> ==''Wednesday 2/8/17''==<br /> ===- Midterm Review===<br /> <br /> =MIDTERM=<br /> <br /> =Week 6: Image Processing=<br /> ==''Monday 2/13/17'' ==<br /> ===-Image Processing I. ''Speaker'' Cameron Rodriguez===<br /> <br /> ==''Wednesday 2/15/17'' ==<br /> ===-Image Processing II. ''Speaker'' Agatha Lenartowicz===<br /> <br /> =Week 7: Image Analysis=<br /> ==''Monday 2/20/17'' ==<br /> ===President’s Day Holiday No Class===<br /> <br /> ==''Wednesday 2/22/17'' ==<br /> ===- Analysis I ''Speaker'' Agatha Lenartowicz===<br /> <br /> =Week 8: Image Analysis Cont.=<br /> ==''Monday 2/27/17 CLASS IN SEMEL 67-415'' ==<br /> ===Analysis II &quot;Speaker&quot; Agatha Lenartowicz===<br /> <br /> ==''Wednesday 3/1/17'' ==<br /> ===- TBD ''Speaker'' TBD===<br /> <br /> =Week 9: Brain Decoding=<br /> ==''Monday 3/6/17'' ==<br /> ===-ML I: Dimension Reduction, Machine Learning Fundamentals ''Speaker'' Wesley Kerr, Ph.D.===<br /> <br /> ==''Wednesday 3/8/17'' ==<br /> ===- MLII: Machine Learning cont. ''Speaker'' Wesley Kerr, Ph.D.===<br /> <br /> =Week 10: Review=<br /> ==''Monday 3/13/17'' ==<br /> ===-TBD ''Speaker'' TBD===<br /> <br /> ==''Wednesday 3/15/17'' ==<br /> ===-Final Exam Review. ''Speaker'' TBD===<br /> <br /> <br /> <br /> <br /> <br /> &lt;!--<br /> [[Image:ASL.png | right]]<br /> ===- Measurement of Blood Flow by MRI. ''Danny JJ Wang'': [http://www.bri.ucla.edu/people/danny-jiong-jiong-wang-phd]===<br /> Most MRI images in use today are static maps, generally called &quot;structural&quot; images. However, the MRI signal is sensitive to a wide variety of dynamic phenomena. Practitioners typically use the term &quot;functional&quot; to describe images that pick up time-varying signals. The term ''functional MRI'', of course, has been usurped to describe brain activity mapping and, in most cases, refers to BOLD signal effects.<br /> <br /> Measurement of blood ''flow'' in particular is another window into brain activity. Several methods exist to create signal sensitive to blood flow, or blood flow changes. Some label the blood flow based on relaxation effects (Arterial Spin Labeling - ASL - being one important method) and others measure flow by velocity.<br /> <br /> In most cases, MRI is used as a semi quantitative modality. We get accurate spatial metrics but, in general, the image intensities are in arbitrary units. Blood flow is one particular feature that we can measure in native units.<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_perfusion-web.pdf‎ | Dr. Wang's Handout]]<br /> <br /> :*[http://www.cis.rit.edu/htbooks/mri/ These notes] by Joseph Hornak are highly professional and complete coverage of MRI.]<br /> :*[[media:M284B_perfusion-web.pdf‎ | Dr. Wang's Handout]]<br /> [[Image:HahnFig1.png]]<br /> ''above'': Figure 1 from Hahn, 1950<br /> <br /> ''Suggested Further Reading''<br /> :*[http://www.pnas.org/content/89/1/212.long Williams, PNAS 1992]<br /> :*[http://onlinelibrary.wiley.com/doi/10.1002/mrm.1910230106/abstract;jsessionid=E42CC3817A5B7B6BB12B6BA3562C1913.f03t04 Detre, MRM 1992]<br /> :*[http://onlinelibrary.wiley.com/doi/10.1002/mrm.21790/epdf Dai, MRM 2008]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/17969096 Wu, MRM 2007]<br /> :*[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3155947/ Zheng, Neuroimage 2012]<br /> <br /> <br /> <br /> =Week 8: Advanced MRI methods=<br /> ==''Monday 2/22/16'' ==<br /> [[Image:DIffusingSpheres.png | left]]<br /> ===- Diffusion Physics ''Speaker'': [http://radiology.ucla.edu/body.cfm?id=48&amp;action=detail&amp;ref=18 Ben Ellingson]===<br /> ''Required Readings''<br /> Slides should be up soon.<br /> :*[https://www.dropbox.com/s/0feqz7tlojbfgzl/1_DiffusionMRI_Methods_2015.key.pdf?dl=0 Handouts for class on 1/14/15] ''&lt;- New 1/26-2015''<br /> :*[[media:1_DiffusionMRI_Methods_v1 - 2014.pdf | Ben Ellingson Diffusion Slides]] uploaded 3/14/14<br /> ''Suggested Further Reading''<br /> :*[[media:DTIHageman.pdf | Hageman DTI Slides]]<br /> :*[[media:NICourse_DTILecture4Post_11-01-26.pdf | Hageman DTI Notes 2/26/12]]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/15037456?dopt=Citation Diffusion tensor imaging of cerebral white matter: a pictorial review of physics, fiber tract anatomy, and tumor imaging patterns AJNR Am J Neuroradiol. 2004 Mar;25(3):356-69]<br /> <br /> :Sadly, the library does not have a subscription for the journals below (Mark has copies on reserve in his office):<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/8661285?dopt=Citation Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J Magn Reson B. 1996 Jun;111(3):209-19]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/16950152?dopt=Citation Principles of diffusion tensor imaging and its applications to basic neuroscience research. Neuron. 2006 Sep 7;51(5):527-39]<br /> <br /> ==''Wednesday 2/25/16'' ==<br /> [[Image:Callosum.png | right]]<br /> ===- Brain Morphometry. ''Speaker'': [http://www.bmap.ucla.edu/about/peopledetails/roger_woods/ Roger Woods]: ===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_Woods.pdf‎ | Dr. Woods' slides]]<br /> <br /> ''Suggested Further Reading''<br /> :[http://www.ncbi.nlm.nih.gov/pubmed/12667854 *RP Woods, “Characterizing volume and surface deformations in an atlas framework: theory, applications, and implementation.” Neuroimage, 18(3): p. 769-788. 2003]<br /> <br /> =Week 9: MR Spectroscopy=<br /> ==''Monday 2/29/16'' ==<br /> No class<br /> <br /> ==''Wednesday 3/2/16'' ==<br /> === Spectroscopy fundamentals. ''Speaker'': [http://people.healthsciences.ucla.edu/institution/personnel?personnel_id=74852 Joseph O'Neill] ===<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:ONeill2016Spectro.pdf‎ | Dr. O'Neill's slides]]<br /> <br /> ===- Spectroscopic Methods. ''Speaker'': [http://radiology.ucla.edu/body.cfm?id=48&amp;action=detail&amp;ref=65 Albert Thomas] ===<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_AThomas030216.pdf‎ | Dr. Thomas' slides]]<br /> ''Suggested Further Reading''<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/23292856 SA Wijtenburg, LM Rowland, RA Edden and PB Barker, “Reproducibility of brain spectroscopy at 7T using conventional localization and spectral editing techniques.” J Magn Reson Imaging, 38(2): p. 460-467. 2013. PMCID: PMC3620961]<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/20574964 S Lipnick, G Verma, S Ramadan, J Furuyama and MA Thomas, “Echo planar correlated spectroscopic imaging: implementation and pilot evaluation in human calf in vivo.” Magn Reson Med, 64(4): p. 947-956. 2010. PMCID: PMC2946523]<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/22505247 JK Furuyama, NE Wilson, BL Burns, R Nagarajan, DJ Margolis and MA Thomas, “Application of compressed sensing to multidimensional spectroscopic imaging in human prostate.” Magn Reson Med, 67(6): p. 1499-1505. 2012.]<br /> <br /> =Week 10: Multimodal Methods / Course wrap up=<br /> ==''Monday 3/7/16'' ==<br /> [[Image:Kinship.png | right]]<br /> ===- Imaging Genetics. ''Speaker'': [http://www.semel.ucla.edu/bearden-lab Carrie Bearden]: ===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:Bearden_NITP2_2016-3-4.pdf | Dr. Bearden's slides]]<br /> <br /> ''Suggested Further Reading''<br /> :*[[media:Carter-Genomics.pdf | ''Enhancing the Informativeness and Replicability of Imaging Genomics Studies'' Carter et al.]]<br /> :*[[media:Flint_and_Geschwind.pdf | ''Genetics and genomics of psychiatric disease'' Flint and Geschwind]]<br /> <br /> <br /> --&gt;</div>

Alenartowicz https://www.ccn.ucla.edu/wiki/index.php?title=Principles_of_Neuroimaging_B_-_2017&diff=3237 2017-01-31T19:32:20Z

<p>Alenartowicz: </p> <hr /> <div>Principles of Neuroimaging B, Winter, 2017 - Class Schedule and Syllabus<br /> <br /> == This schedule is a draft only and ''will'' change!==<br /> <br /> :'''[[Principles_of_Neuroimaging_-_2016-2017 | Back to main course page for Principles of Neuroimaging]]'''<br /> <br /> =Course Reading=<br /> ===Required Reading===<br /> :'''Signal Processing for Neuroscientists''' by ''Wim van Drongelen''<br /> ::This can be found as a PDF on scribd.com, for a small fee of $8.99<br /> <br /> ===Supplemental Reading===<br /> :'''Matlab for Neuroscientists'''<br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/88212458/Matlab-Matlab-for-Neuroscientists<br /> <br /> :'''Cartoon Guide to Statistics''' <br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/148072668/Cartoon-Guide-to-Statistics<br /> <br /> :'''NOTE:''' if you subscribe for a Scribd account for a day, you can download as many documents as you like for one fee.<br /> <br /> =Week 1: Magnetic Resonance Imaging: Fundamentals=<br /> <br /> Magnetic Resonance Imaging (MRI) is probably the most influential and most flexible current means of imaging the human brain. It features a vast number of separable contrast mechanisms, and a near ideal combination of non-invasiveness, safety, resolution and metric accuracy. However, it is extraordinarily expensive and has limited temporal resolution, especially for functional studies<br /> <br /> <br /> OUTLINE<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> :*[http://www.cis.rit.edu/htbooks/mri/ These notes] by Joseph Hornak are highly professional and complete coverage of MRI.<br /> :*[http://www.imaios.com/en/e-Courses/e-MRI eMRI] is another excellent online MRI learning resource<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Hahn1950.pdf Erwin Hahn - Spin Echoes: ''Essential reading for the MRI community'']<br /> [[Image:HahnFig1.png]]<br /> ''above'': Figure 1 from Hahn, 1950<br /> <br /> ''Suggested Further Reading''<br /> [[image:PSatSeq.jpg|right]]<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> <br /> ==''Monday 1/9/17'' ==<br /> ===- MR Physics I. ''Speaker'': [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> ==''Wednesday 1/11/17'' ==<br /> ===- MR Physics II. ''Speaker'': [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> =Week 2: MRI Techniques=<br /> ==''Monday 1/16/17'' ==<br /> ===MLK Day No Class===<br /> <br /> ==''Wednesday 1/18/17'' ==<br /> ===- MR Spatial Encoding Q&amp;A. &quot;Speaker&quot; Cameron Rodriguez===<br /> <br /> =Week 3: MRI Applications =<br /> ==''Monday 1/23/17'' ==<br /> ===- MR Pulse Sequences and Parameters ''Speaker'' [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> ==''Wednesday 1/25/17'' ==<br /> ===- MR Applications (MR, fMRI, DTI, ASL, Spectroscopy). ''Speaker'' [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> =Week 4: Multimodal Imaging=<br /> ==''Monday 1/30/17 CLASS IN SEMEL 67-415''==<br /> ===- Multimodal I ''Speaker'' Cameron Rodriguez===<br /> <br /> ==''Wednesday 2/1/17'' ==<br /> ===- Multimodal II ''Speaker'' Agatha Lenartowicz===<br /> <br /> =Week 5: Review =<br /> ==''Monday 2/6/17''==<br /> ===- Q&amp;A Class ===<br /> <br /> ==''Wednesday 2/8/17''==<br /> ===- Midterm Review===<br /> <br /> =MIDTERM=<br /> <br /> =Week 6: Image Processing=<br /> ==''Monday 2/13/17'' ==<br /> ===-Image Processing I. ''Speaker'' Cameron Rodriguez===<br /> <br /> ==''Wednesday 2/15/17'' ==<br /> ===-Image Processing II. ''Speaker'' Agatha Lenartowicz===<br /> <br /> =Week 7: Image Analysis=<br /> ==''Monday 2/20/17'' ==<br /> ===President’s Day Holiday No Class===<br /> <br /> ==''Wednesday 2/22/17'' ==<br /> ===- Analysis I ''Speaker'' Agatha Lenartowicz===<br /> <br /> =Week 8: Image Analysis Cont.=<br /> ==''Monday 2/27/17 CLASS IN SEMEL 67-415'' ==<br /> ===Analysis II &quot;Speaker&quot; Agatha Lenartowicz===<br /> <br /> ==''Wednesday 3/1/17'' ==<br /> ===- TBD ''Speaker'' TBD===<br /> <br /> =Week 9: Brain Decoding=<br /> ==''Monday 3/6/17'' ==<br /> ===-ML I: Dimension Reduction, Machine Learning Fundamentals ''Speaker'' Wesley Kerr, Ph.D.===<br /> <br /> ==''Wednesday 3/8/17'' ==<br /> ===- MLII: Machine Learning cont. ''Speaker'' Wesley Kerr, Ph.D.===<br /> <br /> =Week 10: Review=<br /> ==''Monday 3/13/17'' ==<br /> ===-TBD ''Speaker'' TBD===<br /> <br /> ==''Wednesday 3/15/17'' ==<br /> ===-Final Exam Review. ''Speaker'' TBD===<br /> <br /> <br /> <br /> <br /> <br /> &lt;!--<br /> [[Image:ASL.png | right]]<br /> ===- Measurement of Blood Flow by MRI. ''Danny JJ Wang'': [http://www.bri.ucla.edu/people/danny-jiong-jiong-wang-phd]===<br /> Most MRI images in use today are static maps, generally called &quot;structural&quot; images. However, the MRI signal is sensitive to a wide variety of dynamic phenomena. Practitioners typically use the term &quot;functional&quot; to describe images that pick up time-varying signals. The term ''functional MRI'', of course, has been usurped to describe brain activity mapping and, in most cases, refers to BOLD signal effects.<br /> <br /> Measurement of blood ''flow'' in particular is another window into brain activity. Several methods exist to create signal sensitive to blood flow, or blood flow changes. Some label the blood flow based on relaxation effects (Arterial Spin Labeling - ASL - being one important method) and others measure flow by velocity.<br /> <br /> In most cases, MRI is used as a semi quantitative modality. We get accurate spatial metrics but, in general, the image intensities are in arbitrary units. Blood flow is one particular feature that we can measure in native units.<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_perfusion-web.pdf‎ | Dr. Wang's Handout]]<br /> <br /> :*[http://www.cis.rit.edu/htbooks/mri/ These notes] by Joseph Hornak are highly professional and complete coverage of MRI.]<br /> :*[[media:M284B_perfusion-web.pdf‎ | Dr. Wang's Handout]]<br /> [[Image:HahnFig1.png]]<br /> ''above'': Figure 1 from Hahn, 1950<br /> <br /> ''Suggested Further Reading''<br /> :*[http://www.pnas.org/content/89/1/212.long Williams, PNAS 1992]<br /> :*[http://onlinelibrary.wiley.com/doi/10.1002/mrm.1910230106/abstract;jsessionid=E42CC3817A5B7B6BB12B6BA3562C1913.f03t04 Detre, MRM 1992]<br /> :*[http://onlinelibrary.wiley.com/doi/10.1002/mrm.21790/epdf Dai, MRM 2008]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/17969096 Wu, MRM 2007]<br /> :*[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3155947/ Zheng, Neuroimage 2012]<br /> <br /> <br /> <br /> =Week 8: Advanced MRI methods=<br /> ==''Monday 2/22/16'' ==<br /> [[Image:DIffusingSpheres.png | left]]<br /> ===- Diffusion Physics ''Speaker'': [http://radiology.ucla.edu/body.cfm?id=48&amp;action=detail&amp;ref=18 Ben Ellingson]===<br /> ''Required Readings''<br /> Slides should be up soon.<br /> :*[https://www.dropbox.com/s/0feqz7tlojbfgzl/1_DiffusionMRI_Methods_2015.key.pdf?dl=0 Handouts for class on 1/14/15] ''&lt;- New 1/26-2015''<br /> :*[[media:1_DiffusionMRI_Methods_v1 - 2014.pdf | Ben Ellingson Diffusion Slides]] uploaded 3/14/14<br /> ''Suggested Further Reading''<br /> :*[[media:DTIHageman.pdf | Hageman DTI Slides]]<br /> :*[[media:NICourse_DTILecture4Post_11-01-26.pdf | Hageman DTI Notes 2/26/12]]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/15037456?dopt=Citation Diffusion tensor imaging of cerebral white matter: a pictorial review of physics, fiber tract anatomy, and tumor imaging patterns AJNR Am J Neuroradiol. 2004 Mar;25(3):356-69]<br /> <br /> :Sadly, the library does not have a subscription for the journals below (Mark has copies on reserve in his office):<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/8661285?dopt=Citation Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J Magn Reson B. 1996 Jun;111(3):209-19]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/16950152?dopt=Citation Principles of diffusion tensor imaging and its applications to basic neuroscience research. Neuron. 2006 Sep 7;51(5):527-39]<br /> <br /> ==''Wednesday 2/25/16'' ==<br /> [[Image:Callosum.png | right]]<br /> ===- Brain Morphometry. ''Speaker'': [http://www.bmap.ucla.edu/about/peopledetails/roger_woods/ Roger Woods]: ===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_Woods.pdf‎ | Dr. Woods' slides]]<br /> <br /> ''Suggested Further Reading''<br /> :[http://www.ncbi.nlm.nih.gov/pubmed/12667854 *RP Woods, “Characterizing volume and surface deformations in an atlas framework: theory, applications, and implementation.” Neuroimage, 18(3): p. 769-788. 2003]<br /> <br /> =Week 9: MR Spectroscopy=<br /> ==''Monday 2/29/16'' ==<br /> No class<br /> <br /> ==''Wednesday 3/2/16'' ==<br /> === Spectroscopy fundamentals. ''Speaker'': [http://people.healthsciences.ucla.edu/institution/personnel?personnel_id=74852 Joseph O'Neill] ===<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:ONeill2016Spectro.pdf‎ | Dr. O'Neill's slides]]<br /> <br /> ===- Spectroscopic Methods. ''Speaker'': [http://radiology.ucla.edu/body.cfm?id=48&amp;action=detail&amp;ref=65 Albert Thomas] ===<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_AThomas030216.pdf‎ | Dr. Thomas' slides]]<br /> ''Suggested Further Reading''<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/23292856 SA Wijtenburg, LM Rowland, RA Edden and PB Barker, “Reproducibility of brain spectroscopy at 7T using conventional localization and spectral editing techniques.” J Magn Reson Imaging, 38(2): p. 460-467. 2013. PMCID: PMC3620961]<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/20574964 S Lipnick, G Verma, S Ramadan, J Furuyama and MA Thomas, “Echo planar correlated spectroscopic imaging: implementation and pilot evaluation in human calf in vivo.” Magn Reson Med, 64(4): p. 947-956. 2010. PMCID: PMC2946523]<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/22505247 JK Furuyama, NE Wilson, BL Burns, R Nagarajan, DJ Margolis and MA Thomas, “Application of compressed sensing to multidimensional spectroscopic imaging in human prostate.” Magn Reson Med, 67(6): p. 1499-1505. 2012.]<br /> <br /> =Week 10: Multimodal Methods / Course wrap up=<br /> ==''Monday 3/7/16'' ==<br /> [[Image:Kinship.png | right]]<br /> ===- Imaging Genetics. ''Speaker'': [http://www.semel.ucla.edu/bearden-lab Carrie Bearden]: ===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:Bearden_NITP2_2016-3-4.pdf | Dr. Bearden's slides]]<br /> <br /> ''Suggested Further Reading''<br /> :*[[media:Carter-Genomics.pdf | ''Enhancing the Informativeness and Replicability of Imaging Genomics Studies'' Carter et al.]]<br /> :*[[media:Flint_and_Geschwind.pdf | ''Genetics and genomics of psychiatric disease'' Flint and Geschwind]]<br /> <br /> <br /> --&gt;</div>

Alenartowicz https://www.ccn.ucla.edu/wiki/index.php?title=Principles_of_Neuroimaging_B_-_2017&diff=3236 2017-01-31T19:31:36Z

<p>Alenartowicz: /* Image Processing I. &quot;Speaker&quot; Cameron Rodriguez */</p> <hr /> <div>Principles of Neuroimaging B, Winter, 2017 - Class Schedule and Syllabus<br /> <br /> == This schedule is a draft only and ''will'' change!==<br /> <br /> :'''[[Principles_of_Neuroimaging_-_2016-2017 | Back to main course page for Principles of Neuroimaging]]'''<br /> <br /> =Course Reading=<br /> ===Required Reading===<br /> :'''Signal Processing for Neuroscientists''' by ''Wim van Drongelen''<br /> ::This can be found as a PDF on scribd.com, for a small fee of $8.99<br /> <br /> ===Supplemental Reading===<br /> :'''Matlab for Neuroscientists'''<br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/88212458/Matlab-Matlab-for-Neuroscientists<br /> <br /> :'''Cartoon Guide to Statistics''' <br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/148072668/Cartoon-Guide-to-Statistics<br /> <br /> :'''NOTE:''' if you subscribe for a Scribd account for a day, you can download as many documents as you like for one fee.<br /> <br /> =Week 1: Magnetic Resonance Imaging: Fundamentals=<br /> <br /> Magnetic Resonance Imaging (MRI) is probably the most influential and most flexible current means of imaging the human brain. It features a vast number of separable contrast mechanisms, and a near ideal combination of non-invasiveness, safety, resolution and metric accuracy. However, it is extraordinarily expensive and has limited temporal resolution, especially for functional studies<br /> <br /> <br /> OUTLINE<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> :*[http://www.cis.rit.edu/htbooks/mri/ These notes] by Joseph Hornak are highly professional and complete coverage of MRI.<br /> :*[http://www.imaios.com/en/e-Courses/e-MRI eMRI] is another excellent online MRI learning resource<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Hahn1950.pdf Erwin Hahn - Spin Echoes: ''Essential reading for the MRI community'']<br /> [[Image:HahnFig1.png]]<br /> ''above'': Figure 1 from Hahn, 1950<br /> <br /> ''Suggested Further Reading''<br /> [[image:PSatSeq.jpg|right]]<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> <br /> ==''Monday 1/9/17'' ==<br /> ===- MR Physics I. ''Speaker'': [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> ==''Wednesday 1/11/17'' ==<br /> ===- MR Physics II. ''Speaker'': [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> =Week 2: MRI Techniques=<br /> ==''Monday 1/16/17'' ==<br /> ===MLK Day No Class===<br /> <br /> ==''Wednesday 1/18/17'' ==<br /> ===- MR Spatial Encoding Q&amp;A. &quot;Speaker&quot; Cameron Rodriguez===<br /> <br /> =Week 3: MRI Applications =<br /> ==''Monday 1/23/17'' ==<br /> ===- MR Pulse Sequences and Parameters ''Speaker'' [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> ==''Wednesday 1/25/17'' ==<br /> ===- MR Applications (MR, fMRI, DTI, ASL, Spectroscopy). ''Speaker'' [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> =Week 4: Multimodal Imaging=<br /> ==''Monday 1/30/17 CLASS IN SEMEL 67-415''==<br /> ===- Multimodal I ''Speaker'' Cameron Rodriguez===<br /> <br /> ==''Wednesday 2/1/17'' ==<br /> ===- Multimodal II ''Speaker'' Agatha Lenartowicz===<br /> <br /> <br /> =Week 5: Review =<br /> ==''Monday 2/6/17''==<br /> ===- Q&amp;A Class ===<br /> <br /> ==''Wednesday 2/8/17''==<br /> ===- Midterm Review===<br /> <br /> =MIDTERM=<br /> <br /> =Week 6: Image Processing=<br /> ==''Monday 2/13/17'' ==<br /> ===-Image Processing I. &quot;Speaker&quot; Cameron Rodriguez===<br /> <br /> ==''Wednesday 2/15/17'' ==<br /> ===-Image Processing II. ''Speaker'' Agatha Lenartowicz===<br /> <br /> =Week 7: Image Analysis=<br /> ==''Monday 2/20/17'' ==<br /> ===President’s Day Holiday No Class===<br /> <br /> ==''Wednesday 2/22/17'' ==<br /> ===- Analysis I ''Speaker'' Agatha Lenartowicz===<br /> <br /> =Week 8: Image Analysis Cont.=<br /> ==''Monday 2/27/17 CLASS IN SEMEL 67-415'' ==<br /> ===Analysis II &quot;Speaker&quot; Agatha Lenartowicz===<br /> <br /> ==''Wednesday 3/1/17'' ==<br /> ===- TBD ''Speaker'' TBD===<br /> <br /> =Week 9: Brain Decoding=<br /> ==''Monday 3/6/17'' ==<br /> ===-ML I: Dimension Reduction, Machine Learning Fundamentals ''Speaker'' Wesley Kerr, Ph.D.===<br /> <br /> ==''Wednesday 3/8/17'' ==<br /> ===- MLII: Machine Learning cont. ''Speaker'' Wesley Kerr, Ph.D.===<br /> <br /> =Week 10: Review=<br /> ==''Monday 3/13/17'' ==<br /> ===-TBD ''Speaker'' TBD===<br /> <br /> ==''Wednesday 3/15/17'' ==<br /> ===-Final Exam Review. ''Speaker'' TBD===<br /> <br /> <br /> <br /> <br /> <br /> &lt;!--<br /> [[Image:ASL.png | right]]<br /> ===- Measurement of Blood Flow by MRI. ''Danny JJ Wang'': [http://www.bri.ucla.edu/people/danny-jiong-jiong-wang-phd]===<br /> Most MRI images in use today are static maps, generally called &quot;structural&quot; images. However, the MRI signal is sensitive to a wide variety of dynamic phenomena. Practitioners typically use the term &quot;functional&quot; to describe images that pick up time-varying signals. The term ''functional MRI'', of course, has been usurped to describe brain activity mapping and, in most cases, refers to BOLD signal effects.<br /> <br /> Measurement of blood ''flow'' in particular is another window into brain activity. Several methods exist to create signal sensitive to blood flow, or blood flow changes. Some label the blood flow based on relaxation effects (Arterial Spin Labeling - ASL - being one important method) and others measure flow by velocity.<br /> <br /> In most cases, MRI is used as a semi quantitative modality. We get accurate spatial metrics but, in general, the image intensities are in arbitrary units. Blood flow is one particular feature that we can measure in native units.<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_perfusion-web.pdf‎ | Dr. Wang's Handout]]<br /> <br /> :*[http://www.cis.rit.edu/htbooks/mri/ These notes] by Joseph Hornak are highly professional and complete coverage of MRI.]<br /> :*[[media:M284B_perfusion-web.pdf‎ | Dr. Wang's Handout]]<br /> [[Image:HahnFig1.png]]<br /> ''above'': Figure 1 from Hahn, 1950<br /> <br /> ''Suggested Further Reading''<br /> :*[http://www.pnas.org/content/89/1/212.long Williams, PNAS 1992]<br /> :*[http://onlinelibrary.wiley.com/doi/10.1002/mrm.1910230106/abstract;jsessionid=E42CC3817A5B7B6BB12B6BA3562C1913.f03t04 Detre, MRM 1992]<br /> :*[http://onlinelibrary.wiley.com/doi/10.1002/mrm.21790/epdf Dai, MRM 2008]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/17969096 Wu, MRM 2007]<br /> :*[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3155947/ Zheng, Neuroimage 2012]<br /> <br /> <br /> <br /> =Week 8: Advanced MRI methods=<br /> ==''Monday 2/22/16'' ==<br /> [[Image:DIffusingSpheres.png | left]]<br /> ===- Diffusion Physics ''Speaker'': [http://radiology.ucla.edu/body.cfm?id=48&amp;action=detail&amp;ref=18 Ben Ellingson]===<br /> ''Required Readings''<br /> Slides should be up soon.<br /> :*[https://www.dropbox.com/s/0feqz7tlojbfgzl/1_DiffusionMRI_Methods_2015.key.pdf?dl=0 Handouts for class on 1/14/15] ''&lt;- New 1/26-2015''<br /> :*[[media:1_DiffusionMRI_Methods_v1 - 2014.pdf | Ben Ellingson Diffusion Slides]] uploaded 3/14/14<br /> ''Suggested Further Reading''<br /> :*[[media:DTIHageman.pdf | Hageman DTI Slides]]<br /> :*[[media:NICourse_DTILecture4Post_11-01-26.pdf | Hageman DTI Notes 2/26/12]]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/15037456?dopt=Citation Diffusion tensor imaging of cerebral white matter: a pictorial review of physics, fiber tract anatomy, and tumor imaging patterns AJNR Am J Neuroradiol. 2004 Mar;25(3):356-69]<br /> <br /> :Sadly, the library does not have a subscription for the journals below (Mark has copies on reserve in his office):<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/8661285?dopt=Citation Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J Magn Reson B. 1996 Jun;111(3):209-19]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/16950152?dopt=Citation Principles of diffusion tensor imaging and its applications to basic neuroscience research. Neuron. 2006 Sep 7;51(5):527-39]<br /> <br /> ==''Wednesday 2/25/16'' ==<br /> [[Image:Callosum.png | right]]<br /> ===- Brain Morphometry. ''Speaker'': [http://www.bmap.ucla.edu/about/peopledetails/roger_woods/ Roger Woods]: ===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_Woods.pdf‎ | Dr. Woods' slides]]<br /> <br /> ''Suggested Further Reading''<br /> :[http://www.ncbi.nlm.nih.gov/pubmed/12667854 *RP Woods, “Characterizing volume and surface deformations in an atlas framework: theory, applications, and implementation.” Neuroimage, 18(3): p. 769-788. 2003]<br /> <br /> =Week 9: MR Spectroscopy=<br /> ==''Monday 2/29/16'' ==<br /> No class<br /> <br /> ==''Wednesday 3/2/16'' ==<br /> === Spectroscopy fundamentals. ''Speaker'': [http://people.healthsciences.ucla.edu/institution/personnel?personnel_id=74852 Joseph O'Neill] ===<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:ONeill2016Spectro.pdf‎ | Dr. O'Neill's slides]]<br /> <br /> ===- Spectroscopic Methods. ''Speaker'': [http://radiology.ucla.edu/body.cfm?id=48&amp;action=detail&amp;ref=65 Albert Thomas] ===<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_AThomas030216.pdf‎ | Dr. Thomas' slides]]<br /> ''Suggested Further Reading''<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/23292856 SA Wijtenburg, LM Rowland, RA Edden and PB Barker, “Reproducibility of brain spectroscopy at 7T using conventional localization and spectral editing techniques.” J Magn Reson Imaging, 38(2): p. 460-467. 2013. PMCID: PMC3620961]<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/20574964 S Lipnick, G Verma, S Ramadan, J Furuyama and MA Thomas, “Echo planar correlated spectroscopic imaging: implementation and pilot evaluation in human calf in vivo.” Magn Reson Med, 64(4): p. 947-956. 2010. PMCID: PMC2946523]<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/22505247 JK Furuyama, NE Wilson, BL Burns, R Nagarajan, DJ Margolis and MA Thomas, “Application of compressed sensing to multidimensional spectroscopic imaging in human prostate.” Magn Reson Med, 67(6): p. 1499-1505. 2012.]<br /> <br /> =Week 10: Multimodal Methods / Course wrap up=<br /> ==''Monday 3/7/16'' ==<br /> [[Image:Kinship.png | right]]<br /> ===- Imaging Genetics. ''Speaker'': [http://www.semel.ucla.edu/bearden-lab Carrie Bearden]: ===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:Bearden_NITP2_2016-3-4.pdf | Dr. Bearden's slides]]<br /> <br /> ''Suggested Further Reading''<br /> :*[[media:Carter-Genomics.pdf | ''Enhancing the Informativeness and Replicability of Imaging Genomics Studies'' Carter et al.]]<br /> :*[[media:Flint_and_Geschwind.pdf | ''Genetics and genomics of psychiatric disease'' Flint and Geschwind]]<br /> <br /> <br /> --&gt;</div>

Alenartowicz https://www.ccn.ucla.edu/wiki/index.php?title=Principles_of_Neuroimaging_B_-_2017&diff=3235 2017-01-31T19:31:05Z

<p>Alenartowicz: </p> <hr /> <div>Principles of Neuroimaging B, Winter, 2017 - Class Schedule and Syllabus<br /> <br /> == This schedule is a draft only and ''will'' change!==<br /> <br /> :'''[[Principles_of_Neuroimaging_-_2016-2017 | Back to main course page for Principles of Neuroimaging]]'''<br /> <br /> =Course Reading=<br /> ===Required Reading===<br /> :'''Signal Processing for Neuroscientists''' by ''Wim van Drongelen''<br /> ::This can be found as a PDF on scribd.com, for a small fee of $8.99<br /> <br /> ===Supplemental Reading===<br /> :'''Matlab for Neuroscientists'''<br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/88212458/Matlab-Matlab-for-Neuroscientists<br /> <br /> :'''Cartoon Guide to Statistics''' <br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/148072668/Cartoon-Guide-to-Statistics<br /> <br /> :'''NOTE:''' if you subscribe for a Scribd account for a day, you can download as many documents as you like for one fee.<br /> <br /> =Week 1: Magnetic Resonance Imaging: Fundamentals=<br /> <br /> Magnetic Resonance Imaging (MRI) is probably the most influential and most flexible current means of imaging the human brain. It features a vast number of separable contrast mechanisms, and a near ideal combination of non-invasiveness, safety, resolution and metric accuracy. However, it is extraordinarily expensive and has limited temporal resolution, especially for functional studies<br /> <br /> <br /> OUTLINE<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> :*[http://www.cis.rit.edu/htbooks/mri/ These notes] by Joseph Hornak are highly professional and complete coverage of MRI.<br /> :*[http://www.imaios.com/en/e-Courses/e-MRI eMRI] is another excellent online MRI learning resource<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Hahn1950.pdf Erwin Hahn - Spin Echoes: ''Essential reading for the MRI community'']<br /> [[Image:HahnFig1.png]]<br /> ''above'': Figure 1 from Hahn, 1950<br /> <br /> ''Suggested Further Reading''<br /> [[image:PSatSeq.jpg|right]]<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> <br /> ==''Monday 1/9/17'' ==<br /> ===- MR Physics I. ''Speaker'': [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> ==''Wednesday 1/11/17'' ==<br /> ===- MR Physics II. ''Speaker'': [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> =Week 2: MRI Techniques=<br /> ==''Monday 1/16/17'' ==<br /> ===MLK Day No Class===<br /> <br /> ==''Wednesday 1/18/17'' ==<br /> ===- MR Spatial Encoding Q&amp;A. &quot;Speaker&quot; Cameron Rodriguez===<br /> <br /> =Week 3: MRI Applications =<br /> ==''Monday 1/23/17'' ==<br /> ===- MR Pulse Sequences and Parameters ''Speaker'' [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> ==''Wednesday 1/25/17'' ==<br /> ===- MR Applications (MR, fMRI, DTI, ASL, Spectroscopy). ''Speaker'' [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> =Week 4: Multimodal Imaging=<br /> ==''Monday 1/30/17 CLASS IN SEMEL 67-415''==<br /> ===- Multimodal I ''Speaker'' Cameron Rodriguez===<br /> <br /> ==''Wednesday 2/1/17'' ==<br /> ===- Multimodal II ''Speaker'' Agatha Lenartowicz===<br /> <br /> <br /> =Week 5: Review =<br /> ==''Monday 2/6/17''==<br /> ===- Q&amp;A Class ===<br /> <br /> ==''Wednesday 2/8/17''==<br /> ===- Midterm Review===<br /> <br /> =MIDTERM=<br /> <br /> =Week 6: Image Processing=<br /> ==''Monday 2/13/17'' ==<br /> ===Image Processing I. &quot;Speaker&quot; Cameron Rodriguez===<br /> <br /> ==''Wednesday 2/15/17'' ==<br /> ===-Image Processing II. ''Speaker'' Agatha Lenartowicz===<br /> <br /> =Week 7: Image Analysis=<br /> ==''Monday 2/20/17'' ==<br /> ===President’s Day Holiday No Class===<br /> <br /> ==''Wednesday 2/22/17'' ==<br /> ===- Analysis I ''Speaker'' Agatha Lenartowicz===<br /> <br /> =Week 8: Image Analysis Cont.=<br /> ==''Monday 2/27/17 CLASS IN SEMEL 67-415'' ==<br /> ===Analysis II &quot;Speaker&quot; Agatha Lenartowicz===<br /> <br /> ==''Wednesday 3/1/17'' ==<br /> ===- TBD ''Speaker'' TBD===<br /> <br /> =Week 9: Brain Decoding=<br /> ==''Monday 3/6/17'' ==<br /> ===-ML I: Dimension Reduction, Machine Learning Fundamentals ''Speaker'' Wesley Kerr, Ph.D.===<br /> <br /> ==''Wednesday 3/8/17'' ==<br /> ===- MLII: Machine Learning cont. ''Speaker'' Wesley Kerr, Ph.D.===<br /> <br /> =Week 10: Review=<br /> ==''Monday 3/13/17'' ==<br /> ===-TBD ''Speaker'' TBD===<br /> <br /> ==''Wednesday 3/15/17'' ==<br /> ===-Final Exam Review. ''Speaker'' TBD===<br /> <br /> <br /> <br /> <br /> <br /> &lt;!--<br /> [[Image:ASL.png | right]]<br /> ===- Measurement of Blood Flow by MRI. ''Danny JJ Wang'': [http://www.bri.ucla.edu/people/danny-jiong-jiong-wang-phd]===<br /> Most MRI images in use today are static maps, generally called &quot;structural&quot; images. However, the MRI signal is sensitive to a wide variety of dynamic phenomena. Practitioners typically use the term &quot;functional&quot; to describe images that pick up time-varying signals. The term ''functional MRI'', of course, has been usurped to describe brain activity mapping and, in most cases, refers to BOLD signal effects.<br /> <br /> Measurement of blood ''flow'' in particular is another window into brain activity. Several methods exist to create signal sensitive to blood flow, or blood flow changes. Some label the blood flow based on relaxation effects (Arterial Spin Labeling - ASL - being one important method) and others measure flow by velocity.<br /> <br /> In most cases, MRI is used as a semi quantitative modality. We get accurate spatial metrics but, in general, the image intensities are in arbitrary units. Blood flow is one particular feature that we can measure in native units.<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_perfusion-web.pdf‎ | Dr. Wang's Handout]]<br /> <br /> :*[http://www.cis.rit.edu/htbooks/mri/ These notes] by Joseph Hornak are highly professional and complete coverage of MRI.]<br /> :*[[media:M284B_perfusion-web.pdf‎ | Dr. Wang's Handout]]<br /> [[Image:HahnFig1.png]]<br /> ''above'': Figure 1 from Hahn, 1950<br /> <br /> ''Suggested Further Reading''<br /> :*[http://www.pnas.org/content/89/1/212.long Williams, PNAS 1992]<br /> :*[http://onlinelibrary.wiley.com/doi/10.1002/mrm.1910230106/abstract;jsessionid=E42CC3817A5B7B6BB12B6BA3562C1913.f03t04 Detre, MRM 1992]<br /> :*[http://onlinelibrary.wiley.com/doi/10.1002/mrm.21790/epdf Dai, MRM 2008]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/17969096 Wu, MRM 2007]<br /> :*[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3155947/ Zheng, Neuroimage 2012]<br /> <br /> <br /> <br /> =Week 8: Advanced MRI methods=<br /> ==''Monday 2/22/16'' ==<br /> [[Image:DIffusingSpheres.png | left]]<br /> ===- Diffusion Physics ''Speaker'': [http://radiology.ucla.edu/body.cfm?id=48&amp;action=detail&amp;ref=18 Ben Ellingson]===<br /> ''Required Readings''<br /> Slides should be up soon.<br /> :*[https://www.dropbox.com/s/0feqz7tlojbfgzl/1_DiffusionMRI_Methods_2015.key.pdf?dl=0 Handouts for class on 1/14/15] ''&lt;- New 1/26-2015''<br /> :*[[media:1_DiffusionMRI_Methods_v1 - 2014.pdf | Ben Ellingson Diffusion Slides]] uploaded 3/14/14<br /> ''Suggested Further Reading''<br /> :*[[media:DTIHageman.pdf | Hageman DTI Slides]]<br /> :*[[media:NICourse_DTILecture4Post_11-01-26.pdf | Hageman DTI Notes 2/26/12]]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/15037456?dopt=Citation Diffusion tensor imaging of cerebral white matter: a pictorial review of physics, fiber tract anatomy, and tumor imaging patterns AJNR Am J Neuroradiol. 2004 Mar;25(3):356-69]<br /> <br /> :Sadly, the library does not have a subscription for the journals below (Mark has copies on reserve in his office):<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/8661285?dopt=Citation Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J Magn Reson B. 1996 Jun;111(3):209-19]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/16950152?dopt=Citation Principles of diffusion tensor imaging and its applications to basic neuroscience research. Neuron. 2006 Sep 7;51(5):527-39]<br /> <br /> ==''Wednesday 2/25/16'' ==<br /> [[Image:Callosum.png | right]]<br /> ===- Brain Morphometry. ''Speaker'': [http://www.bmap.ucla.edu/about/peopledetails/roger_woods/ Roger Woods]: ===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_Woods.pdf‎ | Dr. Woods' slides]]<br /> <br /> ''Suggested Further Reading''<br /> :[http://www.ncbi.nlm.nih.gov/pubmed/12667854 *RP Woods, “Characterizing volume and surface deformations in an atlas framework: theory, applications, and implementation.” Neuroimage, 18(3): p. 769-788. 2003]<br /> <br /> =Week 9: MR Spectroscopy=<br /> ==''Monday 2/29/16'' ==<br /> No class<br /> <br /> ==''Wednesday 3/2/16'' ==<br /> === Spectroscopy fundamentals. ''Speaker'': [http://people.healthsciences.ucla.edu/institution/personnel?personnel_id=74852 Joseph O'Neill] ===<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:ONeill2016Spectro.pdf‎ | Dr. O'Neill's slides]]<br /> <br /> ===- Spectroscopic Methods. ''Speaker'': [http://radiology.ucla.edu/body.cfm?id=48&amp;action=detail&amp;ref=65 Albert Thomas] ===<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_AThomas030216.pdf‎ | Dr. Thomas' slides]]<br /> ''Suggested Further Reading''<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/23292856 SA Wijtenburg, LM Rowland, RA Edden and PB Barker, “Reproducibility of brain spectroscopy at 7T using conventional localization and spectral editing techniques.” J Magn Reson Imaging, 38(2): p. 460-467. 2013. PMCID: PMC3620961]<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/20574964 S Lipnick, G Verma, S Ramadan, J Furuyama and MA Thomas, “Echo planar correlated spectroscopic imaging: implementation and pilot evaluation in human calf in vivo.” Magn Reson Med, 64(4): p. 947-956. 2010. PMCID: PMC2946523]<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/22505247 JK Furuyama, NE Wilson, BL Burns, R Nagarajan, DJ Margolis and MA Thomas, “Application of compressed sensing to multidimensional spectroscopic imaging in human prostate.” Magn Reson Med, 67(6): p. 1499-1505. 2012.]<br /> <br /> =Week 10: Multimodal Methods / Course wrap up=<br /> ==''Monday 3/7/16'' ==<br /> [[Image:Kinship.png | right]]<br /> ===- Imaging Genetics. ''Speaker'': [http://www.semel.ucla.edu/bearden-lab Carrie Bearden]: ===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:Bearden_NITP2_2016-3-4.pdf | Dr. Bearden's slides]]<br /> <br /> ''Suggested Further Reading''<br /> :*[[media:Carter-Genomics.pdf | ''Enhancing the Informativeness and Replicability of Imaging Genomics Studies'' Carter et al.]]<br /> :*[[media:Flint_and_Geschwind.pdf | ''Genetics and genomics of psychiatric disease'' Flint and Geschwind]]<br /> <br /> <br /> --&gt;</div>

Alenartowicz https://www.ccn.ucla.edu/wiki/index.php?title=Principles_of_Neuroimaging_B_-_2017&diff=3234 2017-01-20T21:25:55Z

<p>Alenartowicz: </p> <hr /> <div>Principles of Neuroimaging B, Winter, 2017 - Class Schedule and Syllabus<br /> <br /> == This schedule is a draft only and ''will'' change!==<br /> <br /> :'''[[Principles_of_Neuroimaging_-_2016-2017 | Back to main course page for Principles of Neuroimaging]]'''<br /> <br /> =Course Reading=<br /> ===Required Reading===<br /> :'''Signal Processing for Neuroscientists''' by ''Wim van Drongelen''<br /> ::This can be found as a PDF on scribd.com, for a small fee of $8.99<br /> <br /> ===Supplemental Reading===<br /> :'''Matlab for Neuroscientists'''<br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/88212458/Matlab-Matlab-for-Neuroscientists<br /> <br /> :'''Cartoon Guide to Statistics''' <br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/148072668/Cartoon-Guide-to-Statistics<br /> <br /> :'''NOTE:''' if you subscribe for a Scribd account for a day, you can download as many documents as you like for one fee.<br /> <br /> =Week 1: Magnetic Resonance Imaging: Fundamentals=<br /> <br /> Magnetic Resonance Imaging (MRI) is probably the most influential and most flexible current means of imaging the human brain. It features a vast number of separable contrast mechanisms, and a near ideal combination of non-invasiveness, safety, resolution and metric accuracy. However, it is extraordinarily expensive and has limited temporal resolution, especially for functional studies<br /> <br /> <br /> OUTLINE<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> :*[http://www.cis.rit.edu/htbooks/mri/ These notes] by Joseph Hornak are highly professional and complete coverage of MRI.<br /> :*[http://www.imaios.com/en/e-Courses/e-MRI eMRI] is another excellent online MRI learning resource<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Hahn1950.pdf Erwin Hahn - Spin Echoes: ''Essential reading for the MRI community'']<br /> [[Image:HahnFig1.png]]<br /> ''above'': Figure 1 from Hahn, 1950<br /> <br /> ''Suggested Further Reading''<br /> [[image:PSatSeq.jpg|right]]<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> <br /> ==''Monday 1/9/17'' ==<br /> ===- MR Physics I. ''Speaker'': [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> ==''Wednesday 1/11/17'' ==<br /> ===- MR Physics II. ''Speaker'': [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> =Week 2: MRI Techniques=<br /> ==''Monday 1/16/17'' ==<br /> ===MLK Day No Class===<br /> <br /> ==''Wednesday 1/18/17'' ==<br /> ===- MR Spatial Encoding Q&amp;A. &quot;Speaker&quot; Cameron Rodriguez===<br /> <br /> =Week 3: MRI Applications =<br /> ==''Monday 1/23/17'' ==<br /> ===-MR Pulse Sequences and Parameters ''Speaker'' [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> ==''Wednesday 1/25/17'' ==<br /> ===-MR Applications (MR, fMRI, DTI, ASL, Spectroscopy). ''Speaker'' [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> [[Image:PETfounders.png | right]]<br /> [[Image:PET-ring.png | left]]<br /> <br /> =Week 4: Non-MR Imaging=<br /> ==''Monday 1/30/17 CLASS IN SEMEL 67-415'' ==<br /> ===- TBD ''Speaker'' TBD===<br /> <br /> ==''Wednesday 2/1/17'' ==<br /> ===- TBD. ''Speaker'' TBD===<br /> <br /> <br /> =Week 5: Multimodal Imaging =<br /> ==''Monday 2/6/17'' ==<br /> ===- Multimodal Imaging ''Speaker'' Cameron Rodriguez===<br /> <br /> ==''Wednesday 2/8/17'' ==<br /> ===- Midterm Review===<br /> <br /> =MIDTERM=<br /> <br /> =Week 6: Image Processing=<br /> ==''Monday 2/13/17'' ==<br /> ===Image Processing I. &quot;Speaker&quot; Cameron Rodriguez===<br /> <br /> ==''Wednesday 2/15/17'' ==<br /> ===-Image Processing II. ''Speaker'' Agatha Lenartowicz===<br /> <br /> =Week 7: TBD=<br /> ==''Monday 2/20/17'' ==<br /> ===President’s Day Holiday No Class===<br /> <br /> ==''Wednesday 2/22/17'' ==<br /> ===- TBD ''Speaker'' TBD===<br /> <br /> =Week 8: TBD=<br /> ==''Monday 2/27/17 CLASS IN SEMEL 67-415'' ==<br /> ===TBD &quot;Speaker&quot; TBD===<br /> <br /> ==''Wednesday 3/1/17'' ==<br /> ===- TBD ''Speaker'' TBD===<br /> <br /> 'Required Readings''<br /> :* [[media:TMSSafetyAndEthics-Rossi.pdf | TMS Safety and Ethics - Rossi, 2009]]<br /> :* [http://www.sciencedirect.com/science?_ob=ArticleURL&amp;_udi=B6T0J-50CV801-1&amp;_user=4423&amp;_coverDate=01%2F31%2F2011&amp;_rdoc=1&amp;_fmt=high&amp;_orig=search&amp;_origin=search&amp;_sort=d&amp;_docanchor=&amp;view=c&amp;_acct=C000059605&amp;_version=1&amp;_urlVersion=0&amp;_userid=4423&amp;md5=f9fa3c7d63942dfb3c74e047a1f848bc&amp;searchtype=a | M Sandrini, C Umilta and E Rusconi, “''The use of transcranial magnetic stimulation in cognitive neuroscience: a new synthesis of methodological issues.''” '''Neurosci Biobehav Rev''', '''35'''(3): p. 516-536. 2011]<br /> <br /> ''Suggested Further Reading''<br /> Once upon a time we demonstrated that this sort of magnetic stimulation can take place in the MRI machines:<br /> :*[http://onlinelibrary.wiley.com/doi/10.1002/mrm.1910140226/pdf MS Cohen, RM Weisskoff, RR Rzedzian and HL Kantor, “Sensory stimulation by time-varying magnetic fields.” Magnetic Resonance in Medicine, 14(2): p. 409-414. 1990]<br /> <br /> [[Image:Cetacean.png | right]]<br /> :*[[media:US_Imaging_Systems_1.pdf | Ultrasound Slides (2016)]]<br /> <br /> ''Suggested Further Reading''<br /> :* [[media:Culjat2010.pdf | A review of tissue substitutes for ultrasound imaging ]]<br /> <br /> =Week 9: Brain Decoding=<br /> ==''Monday 3/6/17'' ==<br /> ===-ML I: Dimension Reduction, Machine Learning Fundamentals ''Speaker'' Wesley Kerr, Ph.D.===<br /> <br /> ==''Wednesday 3/8/17'' ==<br /> ===- MLII: Machine Learning cont. ''Speaker'' Wesley Kerr, Ph.D.===<br /> <br /> =Week 10: Review=<br /> ==''Monday 3/13/17'' ==<br /> ===-TBD ''Speaker'' TBD===<br /> <br /> ==''Wednesday 3/15/17'' ==<br /> ===-Final Exam Review. ''Speaker'' TBD===<br /> <br /> <br /> <br /> <br /> <br /> &lt;!--<br /> [[Image:ASL.png | right]]<br /> ===- Measurement of Blood Flow by MRI. ''Danny JJ Wang'': [http://www.bri.ucla.edu/people/danny-jiong-jiong-wang-phd]===<br /> Most MRI images in use today are static maps, generally called &quot;structural&quot; images. However, the MRI signal is sensitive to a wide variety of dynamic phenomena. Practitioners typically use the term &quot;functional&quot; to describe images that pick up time-varying signals. The term ''functional MRI'', of course, has been usurped to describe brain activity mapping and, in most cases, refers to BOLD signal effects.<br /> <br /> Measurement of blood ''flow'' in particular is another window into brain activity. Several methods exist to create signal sensitive to blood flow, or blood flow changes. Some label the blood flow based on relaxation effects (Arterial Spin Labeling - ASL - being one important method) and others measure flow by velocity.<br /> <br /> In most cases, MRI is used as a semi quantitative modality. We get accurate spatial metrics but, in general, the image intensities are in arbitrary units. Blood flow is one particular feature that we can measure in native units.<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_perfusion-web.pdf‎ | Dr. Wang's Handout]]<br /> <br /> :*[http://www.cis.rit.edu/htbooks/mri/ These notes] by Joseph Hornak are highly professional and complete coverage of MRI.]<br /> :*[[media:M284B_perfusion-web.pdf‎ | Dr. Wang's Handout]]<br /> [[Image:HahnFig1.png]]<br /> ''above'': Figure 1 from Hahn, 1950<br /> <br /> ''Suggested Further Reading''<br /> :*[http://www.pnas.org/content/89/1/212.long Williams, PNAS 1992]<br /> :*[http://onlinelibrary.wiley.com/doi/10.1002/mrm.1910230106/abstract;jsessionid=E42CC3817A5B7B6BB12B6BA3562C1913.f03t04 Detre, MRM 1992]<br /> :*[http://onlinelibrary.wiley.com/doi/10.1002/mrm.21790/epdf Dai, MRM 2008]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/17969096 Wu, MRM 2007]<br /> :*[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3155947/ Zheng, Neuroimage 2012]<br /> <br /> <br /> <br /> =Week 8: Advanced MRI methods=<br /> ==''Monday 2/22/16'' ==<br /> [[Image:DIffusingSpheres.png | left]]<br /> ===- Diffusion Physics ''Speaker'': [http://radiology.ucla.edu/body.cfm?id=48&amp;action=detail&amp;ref=18 Ben Ellingson]===<br /> ''Required Readings''<br /> Slides should be up soon.<br /> :*[https://www.dropbox.com/s/0feqz7tlojbfgzl/1_DiffusionMRI_Methods_2015.key.pdf?dl=0 Handouts for class on 1/14/15] ''&lt;- New 1/26-2015''<br /> :*[[media:1_DiffusionMRI_Methods_v1 - 2014.pdf | Ben Ellingson Diffusion Slides]] uploaded 3/14/14<br /> ''Suggested Further Reading''<br /> :*[[media:DTIHageman.pdf | Hageman DTI Slides]]<br /> :*[[media:NICourse_DTILecture4Post_11-01-26.pdf | Hageman DTI Notes 2/26/12]]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/15037456?dopt=Citation Diffusion tensor imaging of cerebral white matter: a pictorial review of physics, fiber tract anatomy, and tumor imaging patterns AJNR Am J Neuroradiol. 2004 Mar;25(3):356-69]<br /> <br /> :Sadly, the library does not have a subscription for the journals below (Mark has copies on reserve in his office):<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/8661285?dopt=Citation Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J Magn Reson B. 1996 Jun;111(3):209-19]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/16950152?dopt=Citation Principles of diffusion tensor imaging and its applications to basic neuroscience research. Neuron. 2006 Sep 7;51(5):527-39]<br /> <br /> ==''Wednesday 2/25/16'' ==<br /> [[Image:Callosum.png | right]]<br /> ===- Brain Morphometry. ''Speaker'': [http://www.bmap.ucla.edu/about/peopledetails/roger_woods/ Roger Woods]: ===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_Woods.pdf‎ | Dr. Woods' slides]]<br /> <br /> ''Suggested Further Reading''<br /> :[http://www.ncbi.nlm.nih.gov/pubmed/12667854 *RP Woods, “Characterizing volume and surface deformations in an atlas framework: theory, applications, and implementation.” Neuroimage, 18(3): p. 769-788. 2003]<br /> <br /> =Week 9: MR Spectroscopy=<br /> ==''Monday 2/29/16'' ==<br /> No class<br /> <br /> ==''Wednesday 3/2/16'' ==<br /> === Spectroscopy fundamentals. ''Speaker'': [http://people.healthsciences.ucla.edu/institution/personnel?personnel_id=74852 Joseph O'Neill] ===<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:ONeill2016Spectro.pdf‎ | Dr. O'Neill's slides]]<br /> <br /> ===- Spectroscopic Methods. ''Speaker'': [http://radiology.ucla.edu/body.cfm?id=48&amp;action=detail&amp;ref=65 Albert Thomas] ===<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_AThomas030216.pdf‎ | Dr. Thomas' slides]]<br /> ''Suggested Further Reading''<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/23292856 SA Wijtenburg, LM Rowland, RA Edden and PB Barker, “Reproducibility of brain spectroscopy at 7T using conventional localization and spectral editing techniques.” J Magn Reson Imaging, 38(2): p. 460-467. 2013. PMCID: PMC3620961]<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/20574964 S Lipnick, G Verma, S Ramadan, J Furuyama and MA Thomas, “Echo planar correlated spectroscopic imaging: implementation and pilot evaluation in human calf in vivo.” Magn Reson Med, 64(4): p. 947-956. 2010. PMCID: PMC2946523]<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/22505247 JK Furuyama, NE Wilson, BL Burns, R Nagarajan, DJ Margolis and MA Thomas, “Application of compressed sensing to multidimensional spectroscopic imaging in human prostate.” Magn Reson Med, 67(6): p. 1499-1505. 2012.]<br /> <br /> =Week 10: Multimodal Methods / Course wrap up=<br /> ==''Monday 3/7/16'' ==<br /> [[Image:Kinship.png | right]]<br /> ===- Imaging Genetics. ''Speaker'': [http://www.semel.ucla.edu/bearden-lab Carrie Bearden]: ===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:Bearden_NITP2_2016-3-4.pdf | Dr. Bearden's slides]]<br /> <br /> ''Suggested Further Reading''<br /> :*[[media:Carter-Genomics.pdf | ''Enhancing the Informativeness and Replicability of Imaging Genomics Studies'' Carter et al.]]<br /> :*[[media:Flint_and_Geschwind.pdf | ''Genetics and genomics of psychiatric disease'' Flint and Geschwind]]<br /> <br /> <br /> --&gt;</div>

Alenartowicz https://www.ccn.ucla.edu/wiki/index.php?title=Principles_of_Neuroimaging_B_-_2017&diff=3233 2017-01-20T21:21:10Z

<p>Alenartowicz: </p> <hr /> <div>Principles of Neuroimaging B, Winter, 2017 - Class Schedule and Syllabus<br /> <br /> == This schedule is a draft only and ''will'' change!==<br /> <br /> :'''[[Principles_of_Neuroimaging_-_2016-2017 | Back to main course page for Principles of Neuroimaging]]'''<br /> <br /> =Course Reading=<br /> ===Required Reading===<br /> :'''Signal Processing for Neuroscientists''' by ''Wim van Drongelen''<br /> ::This can be found as a PDF on scribd.com, for a small fee of $8.99<br /> <br /> ===Supplemental Reading===<br /> :'''Matlab for Neuroscientists'''<br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/88212458/Matlab-Matlab-for-Neuroscientists<br /> <br /> :'''Cartoon Guide to Statistics''' <br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/148072668/Cartoon-Guide-to-Statistics<br /> <br /> :'''NOTE:''' if you subscribe for a Scribd account for a day, you can download as many documents as you like for one fee.<br /> <br /> =Week 1: Magnetic Resonance Imaging: Fundamentals=<br /> <br /> Magnetic Resonance Imaging (MRI) is probably the most influential and most flexible current means of imaging the human brain. It features a vast number of separable contrast mechanisms, and a near ideal combination of non-invasiveness, safety, resolution and metric accuracy. However, it is extraordinarily expensive and has limited temporal resolution, especially for functional studies<br /> <br /> <br /> OUTLINE<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> :*[http://www.cis.rit.edu/htbooks/mri/ These notes] by Joseph Hornak are highly professional and complete coverage of MRI.<br /> :*[http://www.imaios.com/en/e-Courses/e-MRI eMRI] is another excellent online MRI learning resource<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Hahn1950.pdf Erwin Hahn - Spin Echoes: ''Essential reading for the MRI community'']<br /> [[Image:HahnFig1.png]]<br /> ''above'': Figure 1 from Hahn, 1950<br /> <br /> ''Suggested Further Reading''<br /> [[image:PSatSeq.jpg|right]]<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> <br /> ==''Monday 1/9/17'' ==<br /> ===- MR Physics I. ''Speaker'': [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> ==''Wednesday 1/11/17'' ==<br /> ===- MR Physics II. ''Speaker'': [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> =Week 2: MRI Techniques=<br /> ==''Monday 1/16/17'' ==<br /> ===MLK Day No Class===<br /> <br /> ==''Wednesday 1/18/17'' ==<br /> ===- MR Spatial Encoding Q&amp;A. &quot;Speaker&quot; Cameron Rodriguez===<br /> <br /> =Week 3: MRI Applications =<br /> ==''Monday 1/23/17'' ==<br /> ===-MR Pulse Sequences and Parameters ''Speaker'' [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> ==''Wednesday 1/25/17'' ==<br /> ===-MR Applications (MR, fMRI, DTI, ASL, Spectroscopy). ''Speaker'' [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> [[Image:PETfounders.png | right]]<br /> [[Image:PET-ring.png | left]]<br /> <br /> =Week 4: Non-MR Imaging=<br /> ==''Monday 1/30/17 CLASS IN SEMEL 67-415'' ==<br /> ===- TBD ''Speaker'' TBD===<br /> <br /> ==''Wednesday 2/1/17'' ==<br /> ===- TBD. ''Speaker'' TBD===<br /> <br /> <br /> =Week 5: Multimodal Imaging =<br /> ==''Monday 2/6/17'' ==<br /> ===- Multimodal Imaging ''Speaker'' Cameron Rodriguez===<br /> <br /> ==''Wednesday 2/8/17'' ==<br /> ===- Midterm Review===<br /> <br /> =MIDTERM=<br /> <br /> =Week 6: Neuroimaging Data=<br /> ==''Monday 2/13/17'' ==<br /> ===Midterm Review===<br /> <br /> ==''Wednesday 2/15/17'' ==<br /> ===-Multivariate Analyses: Dimension Reduction. ''Speaker'' TBD===<br /> <br /> =Week 7: Neuroimaging Data=<br /> ==''Monday 2/20/17'' ==<br /> ===President’s Day Holiday No Class===<br /> <br /> ==''Wednesday 2/22/17'' ==<br /> ===- Multivariate Analyses: Brain Decoding ''Speaker'' TBD===<br /> <br /> =Week 8: Practical Brain Decoding &amp; Connectivity=<br /> ==''Monday 2/27/17 CLASS IN SEMEL 67-415'' ==<br /> ===Building a BCI System===<br /> <br /> ==''Wednesday 3/1/17'' ==<br /> ===- Connectivity Fundamentals ''Speaker'' TBD===<br /> <br /> =Week 9: Neuromodulation=<br /> ==''Monday 3/6/17'' ==<br /> ===-TMS. ''Speaker'' TBD===<br /> <br /> ==''Wednesday 3/8/17'' ==<br /> ===- Ultrasound. ''Speaker'' TBD===<br /> <br /> 'Required Readings''<br /> :* [[media:TMSSafetyAndEthics-Rossi.pdf | TMS Safety and Ethics - Rossi, 2009]]<br /> :* [http://www.sciencedirect.com/science?_ob=ArticleURL&amp;_udi=B6T0J-50CV801-1&amp;_user=4423&amp;_coverDate=01%2F31%2F2011&amp;_rdoc=1&amp;_fmt=high&amp;_orig=search&amp;_origin=search&amp;_sort=d&amp;_docanchor=&amp;view=c&amp;_acct=C000059605&amp;_version=1&amp;_urlVersion=0&amp;_userid=4423&amp;md5=f9fa3c7d63942dfb3c74e047a1f848bc&amp;searchtype=a | M Sandrini, C Umilta and E Rusconi, “''The use of transcranial magnetic stimulation in cognitive neuroscience: a new synthesis of methodological issues.''” '''Neurosci Biobehav Rev''', '''35'''(3): p. 516-536. 2011]<br /> <br /> ''Suggested Further Reading''<br /> Once upon a time we demonstrated that this sort of magnetic stimulation can take place in the MRI machines:<br /> :*[http://onlinelibrary.wiley.com/doi/10.1002/mrm.1910140226/pdf MS Cohen, RM Weisskoff, RR Rzedzian and HL Kantor, “Sensory stimulation by time-varying magnetic fields.” Magnetic Resonance in Medicine, 14(2): p. 409-414. 1990]<br /> <br /> [[Image:Cetacean.png | right]]<br /> :*[[media:US_Imaging_Systems_1.pdf | Ultrasound Slides (2016)]]<br /> <br /> ''Suggested Further Reading''<br /> :* [[media:Culjat2010.pdf | A review of tissue substitutes for ultrasound imaging ]]<br /> <br /> <br /> =Week 10: Review=<br /> ==''Monday 3/13/17'' ==<br /> ===-Final Exam Review. ''Speaker'' TBD===<br /> <br /> ==''Wednesday 3/15/17'' ==<br /> ===-TBD===<br /> <br /> <br /> <br /> <br /> <br /> &lt;!--<br /> [[Image:ASL.png | right]]<br /> ===- Measurement of Blood Flow by MRI. ''Danny JJ Wang'': [http://www.bri.ucla.edu/people/danny-jiong-jiong-wang-phd]===<br /> Most MRI images in use today are static maps, generally called &quot;structural&quot; images. However, the MRI signal is sensitive to a wide variety of dynamic phenomena. Practitioners typically use the term &quot;functional&quot; to describe images that pick up time-varying signals. The term ''functional MRI'', of course, has been usurped to describe brain activity mapping and, in most cases, refers to BOLD signal effects.<br /> <br /> Measurement of blood ''flow'' in particular is another window into brain activity. Several methods exist to create signal sensitive to blood flow, or blood flow changes. Some label the blood flow based on relaxation effects (Arterial Spin Labeling - ASL - being one important method) and others measure flow by velocity.<br /> <br /> In most cases, MRI is used as a semi quantitative modality. We get accurate spatial metrics but, in general, the image intensities are in arbitrary units. Blood flow is one particular feature that we can measure in native units.<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_perfusion-web.pdf‎ | Dr. Wang's Handout]]<br /> <br /> :*[http://www.cis.rit.edu/htbooks/mri/ These notes] by Joseph Hornak are highly professional and complete coverage of MRI.]<br /> :*[[media:M284B_perfusion-web.pdf‎ | Dr. Wang's Handout]]<br /> [[Image:HahnFig1.png]]<br /> ''above'': Figure 1 from Hahn, 1950<br /> <br /> ''Suggested Further Reading''<br /> :*[http://www.pnas.org/content/89/1/212.long Williams, PNAS 1992]<br /> :*[http://onlinelibrary.wiley.com/doi/10.1002/mrm.1910230106/abstract;jsessionid=E42CC3817A5B7B6BB12B6BA3562C1913.f03t04 Detre, MRM 1992]<br /> :*[http://onlinelibrary.wiley.com/doi/10.1002/mrm.21790/epdf Dai, MRM 2008]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/17969096 Wu, MRM 2007]<br /> :*[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3155947/ Zheng, Neuroimage 2012]<br /> <br /> <br /> <br /> =Week 8: Advanced MRI methods=<br /> ==''Monday 2/22/16'' ==<br /> [[Image:DIffusingSpheres.png | left]]<br /> ===- Diffusion Physics ''Speaker'': [http://radiology.ucla.edu/body.cfm?id=48&amp;action=detail&amp;ref=18 Ben Ellingson]===<br /> ''Required Readings''<br /> Slides should be up soon.<br /> :*[https://www.dropbox.com/s/0feqz7tlojbfgzl/1_DiffusionMRI_Methods_2015.key.pdf?dl=0 Handouts for class on 1/14/15] ''&lt;- New 1/26-2015''<br /> :*[[media:1_DiffusionMRI_Methods_v1 - 2014.pdf | Ben Ellingson Diffusion Slides]] uploaded 3/14/14<br /> ''Suggested Further Reading''<br /> :*[[media:DTIHageman.pdf | Hageman DTI Slides]]<br /> :*[[media:NICourse_DTILecture4Post_11-01-26.pdf | Hageman DTI Notes 2/26/12]]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/15037456?dopt=Citation Diffusion tensor imaging of cerebral white matter: a pictorial review of physics, fiber tract anatomy, and tumor imaging patterns AJNR Am J Neuroradiol. 2004 Mar;25(3):356-69]<br /> <br /> :Sadly, the library does not have a subscription for the journals below (Mark has copies on reserve in his office):<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/8661285?dopt=Citation Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J Magn Reson B. 1996 Jun;111(3):209-19]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/16950152?dopt=Citation Principles of diffusion tensor imaging and its applications to basic neuroscience research. Neuron. 2006 Sep 7;51(5):527-39]<br /> <br /> ==''Wednesday 2/25/16'' ==<br /> [[Image:Callosum.png | right]]<br /> ===- Brain Morphometry. ''Speaker'': [http://www.bmap.ucla.edu/about/peopledetails/roger_woods/ Roger Woods]: ===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_Woods.pdf‎ | Dr. Woods' slides]]<br /> <br /> ''Suggested Further Reading''<br /> :[http://www.ncbi.nlm.nih.gov/pubmed/12667854 *RP Woods, “Characterizing volume and surface deformations in an atlas framework: theory, applications, and implementation.” Neuroimage, 18(3): p. 769-788. 2003]<br /> <br /> =Week 9: MR Spectroscopy=<br /> ==''Monday 2/29/16'' ==<br /> No class<br /> <br /> ==''Wednesday 3/2/16'' ==<br /> === Spectroscopy fundamentals. ''Speaker'': [http://people.healthsciences.ucla.edu/institution/personnel?personnel_id=74852 Joseph O'Neill] ===<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:ONeill2016Spectro.pdf‎ | Dr. O'Neill's slides]]<br /> <br /> ===- Spectroscopic Methods. ''Speaker'': [http://radiology.ucla.edu/body.cfm?id=48&amp;action=detail&amp;ref=65 Albert Thomas] ===<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_AThomas030216.pdf‎ | Dr. Thomas' slides]]<br /> ''Suggested Further Reading''<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/23292856 SA Wijtenburg, LM Rowland, RA Edden and PB Barker, “Reproducibility of brain spectroscopy at 7T using conventional localization and spectral editing techniques.” J Magn Reson Imaging, 38(2): p. 460-467. 2013. PMCID: PMC3620961]<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/20574964 S Lipnick, G Verma, S Ramadan, J Furuyama and MA Thomas, “Echo planar correlated spectroscopic imaging: implementation and pilot evaluation in human calf in vivo.” Magn Reson Med, 64(4): p. 947-956. 2010. PMCID: PMC2946523]<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/22505247 JK Furuyama, NE Wilson, BL Burns, R Nagarajan, DJ Margolis and MA Thomas, “Application of compressed sensing to multidimensional spectroscopic imaging in human prostate.” Magn Reson Med, 67(6): p. 1499-1505. 2012.]<br /> <br /> =Week 10: Multimodal Methods / Course wrap up=<br /> ==''Monday 3/7/16'' ==<br /> [[Image:Kinship.png | right]]<br /> ===- Imaging Genetics. ''Speaker'': [http://www.semel.ucla.edu/bearden-lab Carrie Bearden]: ===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:Bearden_NITP2_2016-3-4.pdf | Dr. Bearden's slides]]<br /> <br /> ''Suggested Further Reading''<br /> :*[[media:Carter-Genomics.pdf | ''Enhancing the Informativeness and Replicability of Imaging Genomics Studies'' Carter et al.]]<br /> :*[[media:Flint_and_Geschwind.pdf | ''Genetics and genomics of psychiatric disease'' Flint and Geschwind]]<br /> <br /> <br /> --&gt;</div>

Alenartowicz https://www.ccn.ucla.edu/wiki/index.php?title=Principles_of_Neuroimaging_B_-_2017&diff=3232 2017-01-20T21:19:09Z

<p>Alenartowicz: </p> <hr /> <div>Principles of Neuroimaging B, Winter, 2017 - Class Schedule and Syllabus<br /> <br /> == This schedule is a draft only and ''will'' change!==<br /> <br /> :'''[[Principles_of_Neuroimaging_-_2016-2017 | Back to main course page for Principles of Neuroimaging]]'''<br /> <br /> =Course Reading=<br /> ===Required Reading===<br /> :'''Signal Processing for Neuroscientists''' by ''Wim van Drongelen''<br /> ::This can be found as a PDF on scribd.com, for a small fee of $8.99<br /> <br /> ===Supplemental Reading===<br /> :'''Matlab for Neuroscientists'''<br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/88212458/Matlab-Matlab-for-Neuroscientists<br /> <br /> :'''Cartoon Guide to Statistics''' <br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/148072668/Cartoon-Guide-to-Statistics<br /> <br /> :'''NOTE:''' if you subscribe for a Scribd account for a day, you can download as many documents as you like for one fee.<br /> <br /> =Week 1: Magnetic Resonance Imaging: Fundamentals=<br /> <br /> Magnetic Resonance Imaging (MRI) is probably the most influential and most flexible current means of imaging the human brain. It features a vast number of separable contrast mechanisms, and a near ideal combination of non-invasiveness, safety, resolution and metric accuracy. However, it is extraordinarily expensive and has limited temporal resolution, especially for functional studies<br /> <br /> <br /> OUTLINE<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> :*[http://www.cis.rit.edu/htbooks/mri/ These notes] by Joseph Hornak are highly professional and complete coverage of MRI.<br /> :*[http://www.imaios.com/en/e-Courses/e-MRI eMRI] is another excellent online MRI learning resource<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Hahn1950.pdf Erwin Hahn - Spin Echoes: ''Essential reading for the MRI community'']<br /> [[Image:HahnFig1.png]]<br /> ''above'': Figure 1 from Hahn, 1950<br /> <br /> ''Suggested Further Reading''<br /> [[image:PSatSeq.jpg|right]]<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> <br /> ==''Monday 1/9/17'' ==<br /> ===- MR Physics I. ''Speaker'': [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> ==''Wednesday 1/11/17'' ==<br /> ===- MR Physics II. ''Speaker'': [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> =Week 2: MRI Techniques=<br /> ==''Monday 1/16/17'' ==<br /> ===MLK Day No Class===<br /> <br /> ==''Wednesday 1/18/17'' ==<br /> ===- MR Spatial Encoding Q&amp;A. &quot;Speaker&quot; Cameron Rodriguez===<br /> <br /> =Week 3: MRI Applications =<br /> ==''Monday 1/23/17'' ==<br /> ===-MR Pulse Sequences and Parameters ''Speaker'' [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> ==''Wednesday 1/25/17'' ==<br /> ===-MR Applications (MR, fMRI, DTI, ASL, Spectroscopy). ''Speaker'' [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> [[Image:PETfounders.png | right]]<br /> [[Image:PET-ring.png | left]]<br /> <br /> =Week 4: Non-MR Imaging=<br /> ==''Monday 1/30/17'' ==<br /> ===- TBD ''Speaker'' TBD===<br /> <br /> ==''Wednesday 2/1/17'' ==<br /> ===- TBD. ''Speaker'' TBD===<br /> <br /> <br /> =Week 5: Multimodal Imaging &amp; Neuroimaging Data=<br /> ==''Monday 2/6/17'' ==<br /> ===- Multimodal Imaging ''Speaker'' Cameron Rodriguez===<br /> <br /> ==''Wednesday 2/8/17'' ==<br /> ===- Midterm Review===<br /> <br /> =MIDTERM=<br /> <br /> =Week 6: Neuroimaging Data=<br /> ==''Monday 2/13/17'' ==<br /> ===Midterm Review===<br /> <br /> ==''Wednesday 2/15/17'' ==<br /> ===-Multivariate Analyses: Dimension Reduction. ''Speaker'' TBD===<br /> <br /> =Week 7: Neuroimaging Data=<br /> ==''Monday 2/20/17'' ==<br /> ===President’s Day Holiday No Class===<br /> <br /> ==''Wednesday 2/22/17'' ==<br /> ===- Multivariate Analyses: Brain Decoding ''Speaker'' TBD===<br /> <br /> =Week 8: Practical Brain Decoding &amp; Connectivity=<br /> ==''Monday 2/27/17'' ==<br /> ===Building a BCI System===<br /> <br /> ==''Wednesday 3/1/17'' ==<br /> ===- Connectivity Fundamentals ''Speaker'' TBD===<br /> <br /> =Week 9: Neuromodulation=<br /> ==''Monday 3/6/17'' ==<br /> ===-TMS. ''Speaker'' TBD===<br /> <br /> ==''Wednesday 3/8/17'' ==<br /> ===- Ultrasound. ''Speaker'' TBD===<br /> <br /> 'Required Readings''<br /> :* [[media:TMSSafetyAndEthics-Rossi.pdf | TMS Safety and Ethics - Rossi, 2009]]<br /> :* [http://www.sciencedirect.com/science?_ob=ArticleURL&amp;_udi=B6T0J-50CV801-1&amp;_user=4423&amp;_coverDate=01%2F31%2F2011&amp;_rdoc=1&amp;_fmt=high&amp;_orig=search&amp;_origin=search&amp;_sort=d&amp;_docanchor=&amp;view=c&amp;_acct=C000059605&amp;_version=1&amp;_urlVersion=0&amp;_userid=4423&amp;md5=f9fa3c7d63942dfb3c74e047a1f848bc&amp;searchtype=a | M Sandrini, C Umilta and E Rusconi, “''The use of transcranial magnetic stimulation in cognitive neuroscience: a new synthesis of methodological issues.''” '''Neurosci Biobehav Rev''', '''35'''(3): p. 516-536. 2011]<br /> <br /> ''Suggested Further Reading''<br /> Once upon a time we demonstrated that this sort of magnetic stimulation can take place in the MRI machines:<br /> :*[http://onlinelibrary.wiley.com/doi/10.1002/mrm.1910140226/pdf MS Cohen, RM Weisskoff, RR Rzedzian and HL Kantor, “Sensory stimulation by time-varying magnetic fields.” Magnetic Resonance in Medicine, 14(2): p. 409-414. 1990]<br /> <br /> [[Image:Cetacean.png | right]]<br /> :*[[media:US_Imaging_Systems_1.pdf | Ultrasound Slides (2016)]]<br /> <br /> ''Suggested Further Reading''<br /> :* [[media:Culjat2010.pdf | A review of tissue substitutes for ultrasound imaging ]]<br /> <br /> <br /> =Week 10: Review=<br /> ==''Monday 3/13/17'' ==<br /> ===-Final Exam Review. ''Speaker'' TBD===<br /> <br /> ==''Wednesday 3/15/17'' ==<br /> ===-TBD===<br /> <br /> <br /> <br /> <br /> <br /> &lt;!--<br /> [[Image:ASL.png | right]]<br /> ===- Measurement of Blood Flow by MRI. ''Danny JJ Wang'': [http://www.bri.ucla.edu/people/danny-jiong-jiong-wang-phd]===<br /> Most MRI images in use today are static maps, generally called &quot;structural&quot; images. However, the MRI signal is sensitive to a wide variety of dynamic phenomena. Practitioners typically use the term &quot;functional&quot; to describe images that pick up time-varying signals. The term ''functional MRI'', of course, has been usurped to describe brain activity mapping and, in most cases, refers to BOLD signal effects.<br /> <br /> Measurement of blood ''flow'' in particular is another window into brain activity. Several methods exist to create signal sensitive to blood flow, or blood flow changes. Some label the blood flow based on relaxation effects (Arterial Spin Labeling - ASL - being one important method) and others measure flow by velocity.<br /> <br /> In most cases, MRI is used as a semi quantitative modality. We get accurate spatial metrics but, in general, the image intensities are in arbitrary units. Blood flow is one particular feature that we can measure in native units.<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_perfusion-web.pdf‎ | Dr. Wang's Handout]]<br /> <br /> :*[http://www.cis.rit.edu/htbooks/mri/ These notes] by Joseph Hornak are highly professional and complete coverage of MRI.]<br /> :*[[media:M284B_perfusion-web.pdf‎ | Dr. Wang's Handout]]<br /> [[Image:HahnFig1.png]]<br /> ''above'': Figure 1 from Hahn, 1950<br /> <br /> ''Suggested Further Reading''<br /> :*[http://www.pnas.org/content/89/1/212.long Williams, PNAS 1992]<br /> :*[http://onlinelibrary.wiley.com/doi/10.1002/mrm.1910230106/abstract;jsessionid=E42CC3817A5B7B6BB12B6BA3562C1913.f03t04 Detre, MRM 1992]<br /> :*[http://onlinelibrary.wiley.com/doi/10.1002/mrm.21790/epdf Dai, MRM 2008]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/17969096 Wu, MRM 2007]<br /> :*[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3155947/ Zheng, Neuroimage 2012]<br /> <br /> <br /> <br /> =Week 8: Advanced MRI methods=<br /> ==''Monday 2/22/16'' ==<br /> [[Image:DIffusingSpheres.png | left]]<br /> ===- Diffusion Physics ''Speaker'': [http://radiology.ucla.edu/body.cfm?id=48&amp;action=detail&amp;ref=18 Ben Ellingson]===<br /> ''Required Readings''<br /> Slides should be up soon.<br /> :*[https://www.dropbox.com/s/0feqz7tlojbfgzl/1_DiffusionMRI_Methods_2015.key.pdf?dl=0 Handouts for class on 1/14/15] ''&lt;- New 1/26-2015''<br /> :*[[media:1_DiffusionMRI_Methods_v1 - 2014.pdf | Ben Ellingson Diffusion Slides]] uploaded 3/14/14<br /> ''Suggested Further Reading''<br /> :*[[media:DTIHageman.pdf | Hageman DTI Slides]]<br /> :*[[media:NICourse_DTILecture4Post_11-01-26.pdf | Hageman DTI Notes 2/26/12]]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/15037456?dopt=Citation Diffusion tensor imaging of cerebral white matter: a pictorial review of physics, fiber tract anatomy, and tumor imaging patterns AJNR Am J Neuroradiol. 2004 Mar;25(3):356-69]<br /> <br /> :Sadly, the library does not have a subscription for the journals below (Mark has copies on reserve in his office):<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/8661285?dopt=Citation Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J Magn Reson B. 1996 Jun;111(3):209-19]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/16950152?dopt=Citation Principles of diffusion tensor imaging and its applications to basic neuroscience research. Neuron. 2006 Sep 7;51(5):527-39]<br /> <br /> ==''Wednesday 2/25/16'' ==<br /> [[Image:Callosum.png | right]]<br /> ===- Brain Morphometry. ''Speaker'': [http://www.bmap.ucla.edu/about/peopledetails/roger_woods/ Roger Woods]: ===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_Woods.pdf‎ | Dr. Woods' slides]]<br /> <br /> ''Suggested Further Reading''<br /> :[http://www.ncbi.nlm.nih.gov/pubmed/12667854 *RP Woods, “Characterizing volume and surface deformations in an atlas framework: theory, applications, and implementation.” Neuroimage, 18(3): p. 769-788. 2003]<br /> <br /> =Week 9: MR Spectroscopy=<br /> ==''Monday 2/29/16'' ==<br /> No class<br /> <br /> ==''Wednesday 3/2/16'' ==<br /> === Spectroscopy fundamentals. ''Speaker'': [http://people.healthsciences.ucla.edu/institution/personnel?personnel_id=74852 Joseph O'Neill] ===<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:ONeill2016Spectro.pdf‎ | Dr. O'Neill's slides]]<br /> <br /> ===- Spectroscopic Methods. ''Speaker'': [http://radiology.ucla.edu/body.cfm?id=48&amp;action=detail&amp;ref=65 Albert Thomas] ===<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_AThomas030216.pdf‎ | Dr. Thomas' slides]]<br /> ''Suggested Further Reading''<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/23292856 SA Wijtenburg, LM Rowland, RA Edden and PB Barker, “Reproducibility of brain spectroscopy at 7T using conventional localization and spectral editing techniques.” J Magn Reson Imaging, 38(2): p. 460-467. 2013. PMCID: PMC3620961]<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/20574964 S Lipnick, G Verma, S Ramadan, J Furuyama and MA Thomas, “Echo planar correlated spectroscopic imaging: implementation and pilot evaluation in human calf in vivo.” Magn Reson Med, 64(4): p. 947-956. 2010. PMCID: PMC2946523]<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/22505247 JK Furuyama, NE Wilson, BL Burns, R Nagarajan, DJ Margolis and MA Thomas, “Application of compressed sensing to multidimensional spectroscopic imaging in human prostate.” Magn Reson Med, 67(6): p. 1499-1505. 2012.]<br /> <br /> =Week 10: Multimodal Methods / Course wrap up=<br /> ==''Monday 3/7/16'' ==<br /> [[Image:Kinship.png | right]]<br /> ===- Imaging Genetics. ''Speaker'': [http://www.semel.ucla.edu/bearden-lab Carrie Bearden]: ===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:Bearden_NITP2_2016-3-4.pdf | Dr. Bearden's slides]]<br /> <br /> ''Suggested Further Reading''<br /> :*[[media:Carter-Genomics.pdf | ''Enhancing the Informativeness and Replicability of Imaging Genomics Studies'' Carter et al.]]<br /> :*[[media:Flint_and_Geschwind.pdf | ''Genetics and genomics of psychiatric disease'' Flint and Geschwind]]<br /> <br /> <br /> --&gt;</div>

Alenartowicz https://www.ccn.ucla.edu/wiki/index.php?title=Principles_of_Neuroimaging_B_-_2017&diff=3231 2017-01-20T21:15:47Z

<p>Alenartowicz: </p> <hr /> <div>Principles of Neuroimaging B, Winter, 2017 - Class Schedule and Syllabus<br /> <br /> == This schedule is a draft only and ''will'' change!==<br /> <br /> :'''[[Principles_of_Neuroimaging_-_2016-2017 | Back to main course page for Principles of Neuroimaging]]'''<br /> <br /> =Course Reading=<br /> ===Required Reading===<br /> :'''Signal Processing for Neuroscientists''' by ''Wim van Drongelen''<br /> ::This can be found as a PDF on scribd.com, for a small fee of $8.99<br /> <br /> ===Supplemental Reading===<br /> :'''Matlab for Neuroscientists'''<br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/88212458/Matlab-Matlab-for-Neuroscientists<br /> <br /> :'''Cartoon Guide to Statistics''' <br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/148072668/Cartoon-Guide-to-Statistics<br /> <br /> :'''NOTE:''' if you subscribe for a Scribd account for a day, you can download as many documents as you like for one fee.<br /> <br /> =Week 1: Magnetic Resonance Imaging: Fundamentals=<br /> <br /> Magnetic Resonance Imaging (MRI) is probably the most influential and most flexible current means of imaging the human brain. It features a vast number of separable contrast mechanisms, and a near ideal combination of non-invasiveness, safety, resolution and metric accuracy. However, it is extraordinarily expensive and has limited temporal resolution, especially for functional studies<br /> <br /> <br /> OUTLINE<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> :*[http://www.cis.rit.edu/htbooks/mri/ These notes] by Joseph Hornak are highly professional and complete coverage of MRI.<br /> :*[http://www.imaios.com/en/e-Courses/e-MRI eMRI] is another excellent online MRI learning resource<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Hahn1950.pdf Erwin Hahn - Spin Echoes: ''Essential reading for the MRI community'']<br /> [[Image:HahnFig1.png]]<br /> ''above'': Figure 1 from Hahn, 1950<br /> <br /> ''Suggested Further Reading''<br /> [[image:PSatSeq.jpg|right]]<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> <br /> ==''Monday 1/9/17'' ==<br /> ===- MR Physics I. ''Speaker'': [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> ==''Wednesday 1/11/17'' ==<br /> ===- MR Physics II. ''Speaker'': [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> =Week 2: MRI Techniques=<br /> ==''Monday 1/16/17'' ==<br /> ===MLK Day No Class===<br /> <br /> ==''Wednesday 1/18/17'' ==<br /> ===- MR Spatial Encoding Q&amp;A. &quot;Speaker&quot; Cameron Rodriguez===<br /> <br /> =Week 3: MRI Applications =<br /> ==''Monday 1/23/17'' ==<br /> ===-MR Pulse Sequences ''Speaker'' [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> ==''Wednesday 1/25/17'' ==<br /> ===-MR Applications (MR, fMRI, DTI, ASL, Spectroscopy). ''Speaker'' [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> [[Image:PETfounders.png | right]]<br /> [[Image:PET-ring.png | left]]<br /> <br /> =Week 4: Non-MR Imaging=<br /> ==''Monday 1/30/17'' ==<br /> ===- Optogenetics I. ''Speaker'' TBD===<br /> <br /> ==''Wednesday 2/1/17'' ==<br /> ===- Optogenetics II. ''Speaker'' TBD===<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:Golshani_NITP_2016.pdf | Dr. Golshani's Methods slides]]<br /> :*[[media:PeymanResearchTalk.pdf | Dr. Golshani's Research slides]]<br /> ''Suggested Further Reading''<br /> :*<br /> <br /> =Week 5: Multimodal Imaging &amp; Neuroimaging Data=<br /> ==''Monday 2/6/17'' ==<br /> ===- Multimodal Imaging ''Speaker'' TBD===<br /> <br /> ==''Wednesday 2/8/17'' ==<br /> ===- Neuroimaging Data: Fundamentals. ''Speaker'' TBD===<br /> <br /> =MIDTERM=<br /> <br /> =Week 6: Neuroimaging Data=<br /> ==''Monday 2/13/17'' ==<br /> ===Midterm Review===<br /> <br /> ==''Wednesday 2/15/17'' ==<br /> ===-Multivariate Analyses: Dimension Reduction. ''Speaker'' TBD===<br /> <br /> =Week 7: Neuroimaging Data=<br /> ==''Monday 2/20/17'' ==<br /> ===President’s Day Holiday No Class===<br /> <br /> ==''Wednesday 2/22/17'' ==<br /> ===- Multivariate Analyses: Brain Decoding ''Speaker'' TBD===<br /> <br /> =Week 8: Practical Brain Decoding &amp; Connectivity=<br /> ==''Monday 2/27/17'' ==<br /> ===Building a BCI System===<br /> <br /> ==''Wednesday 3/1/17'' ==<br /> ===- Connectivity Fundamentals ''Speaker'' TBD===<br /> <br /> =Week 9: Neuromodulation=<br /> ==''Monday 3/6/17'' ==<br /> ===-TMS. ''Speaker'' TBD===<br /> <br /> ==''Wednesday 3/8/17'' ==<br /> ===- Ultrasound. ''Speaker'' TBD===<br /> <br /> 'Required Readings''<br /> :* [[media:TMSSafetyAndEthics-Rossi.pdf | TMS Safety and Ethics - Rossi, 2009]]<br /> :* [http://www.sciencedirect.com/science?_ob=ArticleURL&amp;_udi=B6T0J-50CV801-1&amp;_user=4423&amp;_coverDate=01%2F31%2F2011&amp;_rdoc=1&amp;_fmt=high&amp;_orig=search&amp;_origin=search&amp;_sort=d&amp;_docanchor=&amp;view=c&amp;_acct=C000059605&amp;_version=1&amp;_urlVersion=0&amp;_userid=4423&amp;md5=f9fa3c7d63942dfb3c74e047a1f848bc&amp;searchtype=a | M Sandrini, C Umilta and E Rusconi, “''The use of transcranial magnetic stimulation in cognitive neuroscience: a new synthesis of methodological issues.''” '''Neurosci Biobehav Rev''', '''35'''(3): p. 516-536. 2011]<br /> <br /> ''Suggested Further Reading''<br /> Once upon a time we demonstrated that this sort of magnetic stimulation can take place in the MRI machines:<br /> :*[http://onlinelibrary.wiley.com/doi/10.1002/mrm.1910140226/pdf MS Cohen, RM Weisskoff, RR Rzedzian and HL Kantor, “Sensory stimulation by time-varying magnetic fields.” Magnetic Resonance in Medicine, 14(2): p. 409-414. 1990]<br /> <br /> [[Image:Cetacean.png | right]]<br /> :*[[media:US_Imaging_Systems_1.pdf | Ultrasound Slides (2016)]]<br /> <br /> ''Suggested Further Reading''<br /> :* [[media:Culjat2010.pdf | A review of tissue substitutes for ultrasound imaging ]]<br /> <br /> <br /> =Week 10: Review=<br /> ==''Monday 3/13/17'' ==<br /> ===-Final Exam Review. ''Speaker'' TBD===<br /> <br /> ==''Wednesday 3/15/17'' ==<br /> ===-TBD===<br /> <br /> <br /> <br /> <br /> <br /> &lt;!--<br /> [[Image:ASL.png | right]]<br /> ===- Measurement of Blood Flow by MRI. ''Danny JJ Wang'': [http://www.bri.ucla.edu/people/danny-jiong-jiong-wang-phd]===<br /> Most MRI images in use today are static maps, generally called &quot;structural&quot; images. However, the MRI signal is sensitive to a wide variety of dynamic phenomena. Practitioners typically use the term &quot;functional&quot; to describe images that pick up time-varying signals. The term ''functional MRI'', of course, has been usurped to describe brain activity mapping and, in most cases, refers to BOLD signal effects.<br /> <br /> Measurement of blood ''flow'' in particular is another window into brain activity. Several methods exist to create signal sensitive to blood flow, or blood flow changes. Some label the blood flow based on relaxation effects (Arterial Spin Labeling - ASL - being one important method) and others measure flow by velocity.<br /> <br /> In most cases, MRI is used as a semi quantitative modality. We get accurate spatial metrics but, in general, the image intensities are in arbitrary units. Blood flow is one particular feature that we can measure in native units.<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_perfusion-web.pdf‎ | Dr. Wang's Handout]]<br /> <br /> :*[http://www.cis.rit.edu/htbooks/mri/ These notes] by Joseph Hornak are highly professional and complete coverage of MRI.]<br /> :*[[media:M284B_perfusion-web.pdf‎ | Dr. Wang's Handout]]<br /> [[Image:HahnFig1.png]]<br /> ''above'': Figure 1 from Hahn, 1950<br /> <br /> ''Suggested Further Reading''<br /> :*[http://www.pnas.org/content/89/1/212.long Williams, PNAS 1992]<br /> :*[http://onlinelibrary.wiley.com/doi/10.1002/mrm.1910230106/abstract;jsessionid=E42CC3817A5B7B6BB12B6BA3562C1913.f03t04 Detre, MRM 1992]<br /> :*[http://onlinelibrary.wiley.com/doi/10.1002/mrm.21790/epdf Dai, MRM 2008]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/17969096 Wu, MRM 2007]<br /> :*[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3155947/ Zheng, Neuroimage 2012]<br /> <br /> <br /> <br /> =Week 8: Advanced MRI methods=<br /> ==''Monday 2/22/16'' ==<br /> [[Image:DIffusingSpheres.png | left]]<br /> ===- Diffusion Physics ''Speaker'': [http://radiology.ucla.edu/body.cfm?id=48&amp;action=detail&amp;ref=18 Ben Ellingson]===<br /> ''Required Readings''<br /> Slides should be up soon.<br /> :*[https://www.dropbox.com/s/0feqz7tlojbfgzl/1_DiffusionMRI_Methods_2015.key.pdf?dl=0 Handouts for class on 1/14/15] ''&lt;- New 1/26-2015''<br /> :*[[media:1_DiffusionMRI_Methods_v1 - 2014.pdf | Ben Ellingson Diffusion Slides]] uploaded 3/14/14<br /> ''Suggested Further Reading''<br /> :*[[media:DTIHageman.pdf | Hageman DTI Slides]]<br /> :*[[media:NICourse_DTILecture4Post_11-01-26.pdf | Hageman DTI Notes 2/26/12]]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/15037456?dopt=Citation Diffusion tensor imaging of cerebral white matter: a pictorial review of physics, fiber tract anatomy, and tumor imaging patterns AJNR Am J Neuroradiol. 2004 Mar;25(3):356-69]<br /> <br /> :Sadly, the library does not have a subscription for the journals below (Mark has copies on reserve in his office):<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/8661285?dopt=Citation Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J Magn Reson B. 1996 Jun;111(3):209-19]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/16950152?dopt=Citation Principles of diffusion tensor imaging and its applications to basic neuroscience research. Neuron. 2006 Sep 7;51(5):527-39]<br /> <br /> ==''Wednesday 2/25/16'' ==<br /> [[Image:Callosum.png | right]]<br /> ===- Brain Morphometry. ''Speaker'': [http://www.bmap.ucla.edu/about/peopledetails/roger_woods/ Roger Woods]: ===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_Woods.pdf‎ | Dr. Woods' slides]]<br /> <br /> ''Suggested Further Reading''<br /> :[http://www.ncbi.nlm.nih.gov/pubmed/12667854 *RP Woods, “Characterizing volume and surface deformations in an atlas framework: theory, applications, and implementation.” Neuroimage, 18(3): p. 769-788. 2003]<br /> <br /> =Week 9: MR Spectroscopy=<br /> ==''Monday 2/29/16'' ==<br /> No class<br /> <br /> ==''Wednesday 3/2/16'' ==<br /> === Spectroscopy fundamentals. ''Speaker'': [http://people.healthsciences.ucla.edu/institution/personnel?personnel_id=74852 Joseph O'Neill] ===<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:ONeill2016Spectro.pdf‎ | Dr. O'Neill's slides]]<br /> <br /> ===- Spectroscopic Methods. ''Speaker'': [http://radiology.ucla.edu/body.cfm?id=48&amp;action=detail&amp;ref=65 Albert Thomas] ===<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_AThomas030216.pdf‎ | Dr. Thomas' slides]]<br /> ''Suggested Further Reading''<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/23292856 SA Wijtenburg, LM Rowland, RA Edden and PB Barker, “Reproducibility of brain spectroscopy at 7T using conventional localization and spectral editing techniques.” J Magn Reson Imaging, 38(2): p. 460-467. 2013. PMCID: PMC3620961]<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/20574964 S Lipnick, G Verma, S Ramadan, J Furuyama and MA Thomas, “Echo planar correlated spectroscopic imaging: implementation and pilot evaluation in human calf in vivo.” Magn Reson Med, 64(4): p. 947-956. 2010. PMCID: PMC2946523]<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/22505247 JK Furuyama, NE Wilson, BL Burns, R Nagarajan, DJ Margolis and MA Thomas, “Application of compressed sensing to multidimensional spectroscopic imaging in human prostate.” Magn Reson Med, 67(6): p. 1499-1505. 2012.]<br /> <br /> =Week 10: Multimodal Methods / Course wrap up=<br /> ==''Monday 3/7/16'' ==<br /> [[Image:Kinship.png | right]]<br /> ===- Imaging Genetics. ''Speaker'': [http://www.semel.ucla.edu/bearden-lab Carrie Bearden]: ===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:Bearden_NITP2_2016-3-4.pdf | Dr. Bearden's slides]]<br /> <br /> ''Suggested Further Reading''<br /> :*[[media:Carter-Genomics.pdf | ''Enhancing the Informativeness and Replicability of Imaging Genomics Studies'' Carter et al.]]<br /> :*[[media:Flint_and_Geschwind.pdf | ''Genetics and genomics of psychiatric disease'' Flint and Geschwind]]<br /> <br /> <br /> --&gt;</div>

Alenartowicz https://www.ccn.ucla.edu/wiki/index.php?title=Principles_of_Neuroimaging_B_-_2017&diff=3230 2017-01-10T22:31:30Z

<p>Alenartowicz: </p> <hr /> <div>Principles of Neuroimaging B, Winter, 2017 - Class Schedule and Syllabus<br /> <br /> == This schedule is a draft only and ''will'' change!==<br /> <br /> :'''[[Principles_of_Neuroimaging_-_2016-2017 | Back to main course page for Principles of Neuroimaging]]'''<br /> <br /> =Course Reading=<br /> ===Required Reading===<br /> :'''Signal Processing for Neuroscientists''' by ''Wim van Drongelen''<br /> ::This can be found as a PDF on scribd.com, for a small fee of $8.99<br /> <br /> ===Supplemental Reading===<br /> :'''Matlab for Neuroscientists'''<br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/88212458/Matlab-Matlab-for-Neuroscientists<br /> <br /> :'''Cartoon Guide to Statistics''' <br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/148072668/Cartoon-Guide-to-Statistics<br /> <br /> :'''NOTE:''' if you subscribe for a Scribd account for a day, you can download as many documents as you like for one fee.<br /> <br /> =Week 1: Magnetic Resonance Imaging: Fundamentals=<br /> <br /> Magnetic Resonance Imaging (MRI) is probably the most influential and most flexible current means of imaging the human brain. It features a vast number of separable contrast mechanisms, and a near ideal combination of non-invasiveness, safety, resolution and metric accuracy. However, it is extraordinarily expensive and has limited temporal resolution, especially for functional studies<br /> <br /> <br /> OUTLINE<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> :*[http://www.cis.rit.edu/htbooks/mri/ These notes] by Joseph Hornak are highly professional and complete coverage of MRI.<br /> :*[http://www.imaios.com/en/e-Courses/e-MRI eMRI] is another excellent online MRI learning resource<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Hahn1950.pdf Erwin Hahn - Spin Echoes: ''Essential reading for the MRI community'']<br /> [[Image:HahnFig1.png]]<br /> ''above'': Figure 1 from Hahn, 1950<br /> <br /> ''Suggested Further Reading''<br /> [[image:PSatSeq.jpg|right]]<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> <br /> ==''Monday 1/9/17'' ==<br /> ===- MR Physics I. ''Speaker'': [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> ==''Wednesday 1/11/17'' ==<br /> ===- MR Physics II. ''Speaker'': [https://www.brainmapping.org/MarkCohen/aboutmark.html Mark Cohen]===<br /> <br /> =Week 2: MRI Techniques=<br /> ==''Monday 1/16/17'' ==<br /> ===MLK Day No Class===<br /> <br /> ==''Wednesday 1/18/17'' ==<br /> ===-MR Applications (MR, fMRI, DTI, ASL, Spectroscopy). ''Speaker'' TBD===<br /> <br /> [[Image:PET-ring.png | left]]<br /> <br /> =Week 3: Non-MR Imaging=<br /> ==''Monday 1/23/17'' ==<br /> ===- PET I. ''Speaker'' TBD===<br /> <br /> ==''Wednesday 1/25/17'' ==<br /> ===- PET II. ''Speaker'' TBD===<br /> <br /> [[Image:PETfounders.png | right]]<br /> <br /> =Week 4: Non-MR Imaging=<br /> ==''Monday 1/30/17'' ==<br /> ===- Optogenetics I. ''Speaker'' TBD===<br /> <br /> ==''Wednesday 2/1/17'' ==<br /> ===- Optogenetics II. ''Speaker'' TBD===<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:Golshani_NITP_2016.pdf | Dr. Golshani's Methods slides]]<br /> :*[[media:PeymanResearchTalk.pdf | Dr. Golshani's Research slides]]<br /> ''Suggested Further Reading''<br /> :*<br /> <br /> =Week 5: Multimodal Imaging &amp; Neuroimaging Data=<br /> ==''Monday 2/6/17'' ==<br /> ===- Multimodal Imaging ''Speaker'' TBD===<br /> <br /> ==''Wednesday 2/8/17'' ==<br /> ===- Neuroimaging Data: Fundamentals. ''Speaker'' TBD===<br /> <br /> =MIDTERM=<br /> <br /> =Week 6: Neuroimaging Data=<br /> ==''Monday 2/13/17'' ==<br /> ===Midterm Review===<br /> <br /> ==''Wednesday 2/15/17'' ==<br /> ===-Multivariate Analyses: Dimension Reduction. ''Speaker'' TBD===<br /> <br /> =Week 7: Neuroimaging Data=<br /> ==''Monday 2/20/17'' ==<br /> ===President’s Day Holiday No Class===<br /> <br /> ==''Wednesday 2/22/17'' ==<br /> ===- Multivariate Analyses: Brain Decoding ''Speaker'' TBD===<br /> <br /> =Week 8: Practical Brain Decoding &amp; Connectivity=<br /> ==''Monday 2/27/17'' ==<br /> ===Building a BCI System===<br /> <br /> ==''Wednesday 3/1/17'' ==<br /> ===- Connectivity Fundamentals ''Speaker'' TBD===<br /> <br /> =Week 9: Neuromodulation=<br /> ==''Monday 3/6/17'' ==<br /> ===-TMS. ''Speaker'' TBD===<br /> <br /> ==''Wednesday 3/8/17'' ==<br /> ===- Ultrasound. ''Speaker'' TBD===<br /> <br /> 'Required Readings''<br /> :* [[media:TMSSafetyAndEthics-Rossi.pdf | TMS Safety and Ethics - Rossi, 2009]]<br /> :* [http://www.sciencedirect.com/science?_ob=ArticleURL&amp;_udi=B6T0J-50CV801-1&amp;_user=4423&amp;_coverDate=01%2F31%2F2011&amp;_rdoc=1&amp;_fmt=high&amp;_orig=search&amp;_origin=search&amp;_sort=d&amp;_docanchor=&amp;view=c&amp;_acct=C000059605&amp;_version=1&amp;_urlVersion=0&amp;_userid=4423&amp;md5=f9fa3c7d63942dfb3c74e047a1f848bc&amp;searchtype=a | M Sandrini, C Umilta and E Rusconi, “''The use of transcranial magnetic stimulation in cognitive neuroscience: a new synthesis of methodological issues.''” '''Neurosci Biobehav Rev''', '''35'''(3): p. 516-536. 2011]<br /> <br /> ''Suggested Further Reading''<br /> Once upon a time we demonstrated that this sort of magnetic stimulation can take place in the MRI machines:<br /> :*[http://onlinelibrary.wiley.com/doi/10.1002/mrm.1910140226/pdf MS Cohen, RM Weisskoff, RR Rzedzian and HL Kantor, “Sensory stimulation by time-varying magnetic fields.” Magnetic Resonance in Medicine, 14(2): p. 409-414. 1990]<br /> <br /> [[Image:Cetacean.png | right]]<br /> :*[[media:US_Imaging_Systems_1.pdf | Ultrasound Slides (2016)]]<br /> <br /> ''Suggested Further Reading''<br /> :* [[media:Culjat2010.pdf | A review of tissue substitutes for ultrasound imaging ]]<br /> <br /> <br /> =Week 10: Review=<br /> ==''Monday 3/13/17'' ==<br /> ===-Final Exam Review. ''Speaker'' TBD===<br /> <br /> ==''Wednesday 3/15/17'' ==<br /> ===-TBD===<br /> <br /> <br /> <br /> <br /> <br /> &lt;!--<br /> [[Image:ASL.png | right]]<br /> ===- Measurement of Blood Flow by MRI. ''Danny JJ Wang'': [http://www.bri.ucla.edu/people/danny-jiong-jiong-wang-phd]===<br /> Most MRI images in use today are static maps, generally called &quot;structural&quot; images. However, the MRI signal is sensitive to a wide variety of dynamic phenomena. Practitioners typically use the term &quot;functional&quot; to describe images that pick up time-varying signals. The term ''functional MRI'', of course, has been usurped to describe brain activity mapping and, in most cases, refers to BOLD signal effects.<br /> <br /> Measurement of blood ''flow'' in particular is another window into brain activity. Several methods exist to create signal sensitive to blood flow, or blood flow changes. Some label the blood flow based on relaxation effects (Arterial Spin Labeling - ASL - being one important method) and others measure flow by velocity.<br /> <br /> In most cases, MRI is used as a semi quantitative modality. We get accurate spatial metrics but, in general, the image intensities are in arbitrary units. Blood flow is one particular feature that we can measure in native units.<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_perfusion-web.pdf‎ | Dr. Wang's Handout]]<br /> <br /> :*[http://www.cis.rit.edu/htbooks/mri/ These notes] by Joseph Hornak are highly professional and complete coverage of MRI.]<br /> :*[[media:M284B_perfusion-web.pdf‎ | Dr. Wang's Handout]]<br /> [[Image:HahnFig1.png]]<br /> ''above'': Figure 1 from Hahn, 1950<br /> <br /> ''Suggested Further Reading''<br /> :*[http://www.pnas.org/content/89/1/212.long Williams, PNAS 1992]<br /> :*[http://onlinelibrary.wiley.com/doi/10.1002/mrm.1910230106/abstract;jsessionid=E42CC3817A5B7B6BB12B6BA3562C1913.f03t04 Detre, MRM 1992]<br /> :*[http://onlinelibrary.wiley.com/doi/10.1002/mrm.21790/epdf Dai, MRM 2008]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/17969096 Wu, MRM 2007]<br /> :*[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3155947/ Zheng, Neuroimage 2012]<br /> <br /> <br /> <br /> =Week 8: Advanced MRI methods=<br /> ==''Monday 2/22/16'' ==<br /> [[Image:DIffusingSpheres.png | left]]<br /> ===- Diffusion Physics ''Speaker'': [http://radiology.ucla.edu/body.cfm?id=48&amp;action=detail&amp;ref=18 Ben Ellingson]===<br /> ''Required Readings''<br /> Slides should be up soon.<br /> :*[https://www.dropbox.com/s/0feqz7tlojbfgzl/1_DiffusionMRI_Methods_2015.key.pdf?dl=0 Handouts for class on 1/14/15] ''&lt;- New 1/26-2015''<br /> :*[[media:1_DiffusionMRI_Methods_v1 - 2014.pdf | Ben Ellingson Diffusion Slides]] uploaded 3/14/14<br /> ''Suggested Further Reading''<br /> :*[[media:DTIHageman.pdf | Hageman DTI Slides]]<br /> :*[[media:NICourse_DTILecture4Post_11-01-26.pdf | Hageman DTI Notes 2/26/12]]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/15037456?dopt=Citation Diffusion tensor imaging of cerebral white matter: a pictorial review of physics, fiber tract anatomy, and tumor imaging patterns AJNR Am J Neuroradiol. 2004 Mar;25(3):356-69]<br /> <br /> :Sadly, the library does not have a subscription for the journals below (Mark has copies on reserve in his office):<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/8661285?dopt=Citation Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J Magn Reson B. 1996 Jun;111(3):209-19]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/16950152?dopt=Citation Principles of diffusion tensor imaging and its applications to basic neuroscience research. Neuron. 2006 Sep 7;51(5):527-39]<br /> <br /> ==''Wednesday 2/25/16'' ==<br /> [[Image:Callosum.png | right]]<br /> ===- Brain Morphometry. ''Speaker'': [http://www.bmap.ucla.edu/about/peopledetails/roger_woods/ Roger Woods]: ===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_Woods.pdf‎ | Dr. Woods' slides]]<br /> <br /> ''Suggested Further Reading''<br /> :[http://www.ncbi.nlm.nih.gov/pubmed/12667854 *RP Woods, “Characterizing volume and surface deformations in an atlas framework: theory, applications, and implementation.” Neuroimage, 18(3): p. 769-788. 2003]<br /> <br /> =Week 9: MR Spectroscopy=<br /> ==''Monday 2/29/16'' ==<br /> No class<br /> <br /> ==''Wednesday 3/2/16'' ==<br /> === Spectroscopy fundamentals. ''Speaker'': [http://people.healthsciences.ucla.edu/institution/personnel?personnel_id=74852 Joseph O'Neill] ===<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:ONeill2016Spectro.pdf‎ | Dr. O'Neill's slides]]<br /> <br /> ===- Spectroscopic Methods. ''Speaker'': [http://radiology.ucla.edu/body.cfm?id=48&amp;action=detail&amp;ref=65 Albert Thomas] ===<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_AThomas030216.pdf‎ | Dr. Thomas' slides]]<br /> ''Suggested Further Reading''<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/23292856 SA Wijtenburg, LM Rowland, RA Edden and PB Barker, “Reproducibility of brain spectroscopy at 7T using conventional localization and spectral editing techniques.” J Magn Reson Imaging, 38(2): p. 460-467. 2013. PMCID: PMC3620961]<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/20574964 S Lipnick, G Verma, S Ramadan, J Furuyama and MA Thomas, “Echo planar correlated spectroscopic imaging: implementation and pilot evaluation in human calf in vivo.” Magn Reson Med, 64(4): p. 947-956. 2010. PMCID: PMC2946523]<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/22505247 JK Furuyama, NE Wilson, BL Burns, R Nagarajan, DJ Margolis and MA Thomas, “Application of compressed sensing to multidimensional spectroscopic imaging in human prostate.” Magn Reson Med, 67(6): p. 1499-1505. 2012.]<br /> <br /> =Week 10: Multimodal Methods / Course wrap up=<br /> ==''Monday 3/7/16'' ==<br /> [[Image:Kinship.png | right]]<br /> ===- Imaging Genetics. ''Speaker'': [http://www.semel.ucla.edu/bearden-lab Carrie Bearden]: ===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:Bearden_NITP2_2016-3-4.pdf | Dr. Bearden's slides]]<br /> <br /> ''Suggested Further Reading''<br /> :*[[media:Carter-Genomics.pdf | ''Enhancing the Informativeness and Replicability of Imaging Genomics Studies'' Carter et al.]]<br /> :*[[media:Flint_and_Geschwind.pdf | ''Genetics and genomics of psychiatric disease'' Flint and Geschwind]]<br /> <br /> <br /> --&gt;</div>

Alenartowicz https://www.ccn.ucla.edu/wiki/index.php?title=Principles_of_Neuroimaging_-_2016-2017&diff=3229 2017-01-10T22:27:00Z

<p>Alenartowicz: /* Mondays and Wednesdays at from 2-4 pm in Room 17-369 of the Semel Institute on the first floor of the NPI. */</p> <hr /> <div>[[File:NeuroImages.jpg | x203px]]<br /> =If you are a guest instructor, please read: [[Notes for Instructors]].=<br /> &lt;br /&gt;<br /> <br /> =Class Meetings=<br /> ===Mondays and Wednesdays at from 2-4 pm in '''[http://maps.ucla.edu/campus/?zlvl=10&amp;cpnt=-118.4441009215107,34.065875066286004 Room 17-364]''' of the Semel Institute on the first floor of the NPI.===<br /> &lt;br /&gt;<br /> <br /> =Course Schedule &amp; Syllabus=<br /> * [[Principles_of_Neuroimaging_A_-_2016 | '''Course Schedule &amp; Syllabus M284A (link)''']]<br /> * [[Principles_of_Neuroimaging_B_-_2017 | '''Course Schedule &amp; Syllabus M284B (link)''' ]]<br /> &lt;br /&gt;<br /> <br /> =General Information=<br /> This is a Wiki: You are encouraged to post comments and clarifications.<br /> ==Course Goals==<br /> The overall goal of this course, and of the NITP teaching program, is to give you a solid background in the concepts common to many types of neuroimaging, as well as a set of tools to think about and to analyze these images in the service of scientific hypothesis testing. There are ways of thinking about images that are shared across microscopy, positron emission tomography, EEG, X-ray, MRI and many others and that a good understanding of these will leave you prepared to take on not only the current armamentarium of imaging tools, but the newer methods that will arise during your careers.<br /> <br /> Extract information from images ''always'' implies the existence of a model for that information. Generally, we seek to remove extraneous content (by ''filtering''), and seek evidence in the images of data that conform to our model, usually by comparing what's in the image data to our model. This course concerns itself with themes in signal detection, statistical analysis, modeling, filtering, and evidence.<br /> <br /> Our eyes act as filters, our prior experiences as hypotheses, our entire perceptual system as models. Likewise, the devices themselves instantiate models of the world or of the data we hope to detect. A mission of this course is to make us more aware of the implicit expectations built in to all current imaging tools.<br /> <br /> This year, we will explore emerging concepts in imaging - new, and groundbreaking science.<br /> <br /> ===Teaching Philosophy===<br /> At the graduate level, IMHO the courses are not about grades, but about learning at a professional level. We do not emphasize exams and papers but:<br /> <br /> &lt;ol&gt;<br /> &lt;li&gt; This is a core course in several departments. Rigorous grading is required and, &lt;/li&gt;<br /> &lt;li&gt; Preparing for evaluations tends to force one to think and consolidate information.&lt;/li&gt;<br /> &lt;/ol&gt;<br /> Much more important, however, is your commitment to reading the material and participating in class. This means challenging the lecturers and students to be clear about concepts, and to place their work in the broadest context possible.<br /> <br /> Because the emphasis is on skills learning, as much as on content, we will prepare lectures and exercises on tools, including math, engineering and programming, that I hope will be useful to you for years into the future.<br /> <br /> MATLAB will be required for the course. While I had tried in prior classes to allow students to use a variety of programming languages, I found that this made things complicated for everybody. Usually, the example data will be made available through the course web site and, in many cases, there will be matlab code associated with it, so that you can open the files and read the data. You can purchase student copies of MATLAB for $99 (which is a bargain, BTW). If, for some reason, this is a hardship, please let me know and I will make arrangements on your behalf. I will provide some basic training in the software, but you should go through the tutorials on your own.<br /> <br /> Form time-to-time, we might collect some live example data during the course and will need volunteers willing to participate. If you would like to volunteer to have your brain studied, please contact me.<br /> <br /> ===Required Text===<br /> This year, as in the past, we will be ''informally'' using [http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description '''Signal Processing for Neuroscientists: An Introduction to the Analysis of Physiological Signals'''] by Wim can Drongelen - This comes with a CD containing Matlab code for the examples, all of which are based in neuroscience (e.g. EEG, spike trains, etc...) Intensive use of this book will start no sooner than week 3. You can obtain the book at ASUCLA bookstore.<br /> <br /> ===Further Reading===<br /> There are many links to reading materials on the [[Principles_of_Neuroimaging_A_-_2016 | '''Course Schedule &amp; Syllabus M284A''' ]] and [[Principles_of_Neuroimaging_B_-_2017 | '''Course Schedule &amp; Syllabus M284B''' ]] pages. If they are optional, it will say so.<br /> <br /> For the statistics sections, I STRONGLY recommend <br /> *[http://www.amazon.com/Cartoon-Guide-Statistics-Larry-Gonick/dp/0062731025 The Cartoon Guide to Statistics - Gonick $17.95 new]. This book is available at ASUCLA.<br /> Some other excellent resource texts include:<br /> *[http://www.amazon.com/MATLAB-Behavioral-Scientists-David-Rosenbaum/dp/0805863192/ref=ed_oe_p Matlab for Behavioral Scientists]<br /> *[http://www.amazon.com/Matlab-Neuroscientists-Introduction-Scientific-Computing/dp/0123745519/ref=sr_1_1?ie=UTF8&amp;s=books&amp;qid=1231366863&amp;sr=1-1 Matlab for Neuroscientists]<br /> *[http://www.amazon.com/Understanding-Digital-Signal-Processing-2nd/dp/0131089897/ref=sr_1_1?ie=UTF8&amp;s=books&amp;qid=1231979157&amp;sr=1-1 Understanding Digital Signal Processing] - An easy to understand explanation of digital sampling and [http://www.popularreview.com reviewing], various Fourier transforms, types of filtering, etc.<br /> *[http://www.elsevierdirect.com/v2/companion.jsp?ISBN=9780123708670 Signal Processing Matlab Files] - A Link to the Matlab file for the above book can be found here<br /> *[http://www.dspguide.com/pdfbook.htm The Scientist and Engineer's Guide to Digital Signal Processing] - '''FREE''' online DSP book with '''FREE''' downloads of each chapter in pdf format! I will occasionally post links on the [[syllabus page]] to chapters relevant current course lectures (kmc).<br /> <br /> ===Problem Sets===<br /> Problem sets will occur about once per week. Generally, you will have a week to work on them. These are often are kept simple and mechanical in order to learn the mechanics; some may be more challenging. You will use these skills in the midterm and final.<br /> <br /> Please send these to Cameron Rodriguez [mailto:cdrodriguez@g.ucla.edu (cdrodriguez@g.ucla.edu)]. If you do not get a response that your mail has been received, call or otherwise follow up with the instructors.<br /> <br /> Please Always Include This Title Line: '''M284 2016/17 Problem Set''', so that your mails are never lost.<br /> <br /> ==Instructor Information==<br /> Agatha Lenartowicz can be reached at [mailto:alenarto@g.ucla.edu alenarto@g.ucla.edu].<br /> Cameron Rodriguez can be reached at [mailto:cdrodriguez@g.ucla.edu cdrodriguez@g.ucla.edu].<br /> Office hours will be after class on Mondays and Wednesdays in room 17-369 of the NPI.<br /> <br /> ==Organizational notes==<br /> When sending mail about the course, please include the characters: '''NITP''' in the subject line somewhere, as that helps a great deal in file management. Thanks.<br /> <br /> While most of the classes will be in lecture format, there will also be lab work in computing, electronics and image collection. It may be necessary to schedule these outside of standard class hours to accommodate the availability of the equipment we need.<br /> <br /> ==Class List sign up==<br /> As soon as possible, please add yourself to the list of students in the class.<br /> [http://ccn.ucla.edu/mailman/listinfo/neuroimaging Class List].<br /> <br /> Please also send an email directly to [mailto:cdrodriguez@g.ucla.edu Agatha Lenartowicz] with your name, your best contact email and a subject line of &quot;'''M284 class signup - NITP'''&quot;<br /> <br /> ==Auditing==<br /> Auditing means ''taking the course'', though not for credit. Auditors are expected to attend all lectures, participate in discussions, and do ''all'' problem sets and tests. For my part, I will read an score the materials. If you do not intend to actually do the classwork, auditing is actively discouraged. If you miss several assignments you will be asked to drop the course.<br /> <br /> =Catalog Course Description=<br /> Factors common to neuroimaging in multiple modalities including: Physiological Contrast mechanisms and Biophysics; Signal and Image processing, including transform approaches, Statistical Modeling and Inference, Time-Series Statistics, Detection Theory, Contrast Agents, Experimental Design, Modeling and Inference, Electrical Detection methods, Electroencephalography, Optical Methods, Microscopy.<br /> <br /> =Pre-Requisites=<br /> Functional Neuroanatomy (M292) and competence in 1) Integral calculus 2) Statistics 3) Electricity and Magnetism and 4) Computer Programming (any language). Waiver of some requirements may be possible by consent of the instructor.<br /> <br /> The following are examples of the level of knowledge expected on entry. If you do not have this background please let Mark know as soon as possible. We will do our best to remediate any missing knowledge.<br /> <br /> '''In week 1 of class please ensure you fill out this online quiz to assess your skills, both for our and your benefit [[https://docs.google.com/forms/d/e/1FAIpQLSftU5YiXipM64fGcAM-guvfUBsT60r-jzISWc-6smBiTfUd9g/viewform]]'''<br /> <br /> ==Stats==<br /> A general philosophy of the course and of the NITP is that a sophisticated consumer of images uses these data as a test of a hypothesis. You will learn more about the instructor's feelings about truth by ''p''-values, but it is important to have a good intuitive understanding of random processes, noise, reliability, estimation, ''etc...'' For this reason, stats comfort is a must.&lt;br&gt;<br /> ----<br /> Here are a few questions that you should be easily able to find the answers to:&lt;br&gt;<br /> Given a sample of student heights at UCLA in inches:&lt;br&gt;<br /> : H(&quot;males&quot;) = [74, 71, 67, 69, 71, 70, 65, 67, 71, 68, 69, 66], and&lt;br&gt;<br /> : H(&quot;females&quot;) = [62, 66, 68, 62, 65, 62, 63, 64]&lt;br&gt;&lt;ul&gt;<br /> &lt;li&gt;What is the modal height of the males?&lt;/li&gt;<br /> &lt;li&gt;What is the difference in mean height between males and females?&lt;/li&gt;<br /> &lt;li&gt;Which of the following ''should'' be used to test if the average height of UCLA males and females differ significantly at &quot;p&quot;&lt;0.01?<br /> &lt;ol&gt;<br /> &lt;li&gt; Increase the number of females in the sample be eight, then perform a ''t''-test on the means&lt;/li&gt;<br /> &lt;li&gt; Continue collecting more data until the probability of a two-tailed ''t''-test statistic comparing males and females is less than 0.01.&lt;/li&gt;<br /> &lt;li&gt; Collect the heights of &quot;all&quot; males and females at UCLA and then calculate the ''t''--statistic to determine if the heights differ at the assigned probability level&lt;/li&gt;<br /> &lt;li&gt; Collect height data from an age-matched sample in the surrounding community.&lt;/li&gt;<br /> &lt;li&gt; Add to the sample until there are exactly 100 males and 100 females, and calculate if the heights differ by more than 1%.&lt;/li&gt;<br /> &lt;li&gt; None of the above.&lt;/li&gt;<br /> &lt;li&gt; All of the above&lt;/li&gt;<br /> &lt;/ol&gt;<br /> &lt;/ul&gt;<br /> <br /> ==Programming==<br /> Formally, students are required to have a background in at least some programming language. The fact of the matter is that Neuroimaging is computationally intensive; programming is a basic skill for this work. I intend to prepare problem sets that will require programming to solve.&lt;br&gt;<br /> This year, all of our programming will be done using MATLAB, purchase of which is a course requirement. The [http://i2w3.ais.ucla.edu/asucla/store.aspx?pg=macsoftware.pdf ASUCLA student store] has the licenses for students at an incredibly discounted price of $99. You will not regret owning this.<br /> <br /> ''Many'' MATLAB tutorials can be found online. Here is a good<br /> [http://www.mathworks.com/academia/student_center/tutorials/register.html interactive beginner tutorial] from MathWorks. It takes about 2 hours and you must register with MathWorks beforehand, but it covers many aspects of MATLAB in depth (e.g. the workspace, importing data, visualizing data, scripts, functions, &amp; loops).<br /> <br /> Another useful option is the ''demo'' feature that can be accessed within MATLAB by typing 'demo' at the command prompt.<br /> <br /> &gt;&gt; demo<br /> <br /> This will open a help window of all available demos. Here are a few demos I recommend (kmc):<br /> *Importing Data from Files<br /> *Using Basic Plotting Functions<br /> *Working with Arrays<br /> *Manipulating Multidimensional Arrays<br /> <br /> == Mathematics ==<br /> Can you solve for y or &lt;math&gt;\mathbf{Y}&lt;/math&gt; in these equations?&lt;br&gt;<br /> &lt;math&gt;y = \frac{d(e^x)}{dx}&lt;/math&gt;&lt;br&gt;<br /> &lt;math&gt;y = \int\sin x\,dx&lt;/math&gt;&lt;br&gt;<br /> <br /> &lt;math&gt;\mathbf{Y}=\left[\begin{array}{cc}<br /> 2 &amp; 4\\<br /> 5 &amp; 7\end{array}\right]^{-1}&lt;/math&gt;&lt;br&gt;<br /> If &lt;math&gt;y = 3x^2 + 6x + 2&lt;/math&gt;, what is &lt;math&gt;\frac{d(e^x)}{dx}&lt;/math&gt;?<br /> &lt;br&gt;<br /> If not, please let me know, and we will try to remedy things. In the meantime, there are a number of excellent online math tutorials. For matrices, may I suggest:<br /> &lt;ul&gt;<br /> &lt;li&gt;[http://www.math.hmc.edu/calculus/tutorials/ Harvey Mudd mathematics online tutorial]<br /> &lt;li&gt;[http://www.sosmath.com/matrix/matrix.html S.O.S. Mathematics]&lt;/li&gt;<br /> &lt;li&gt;[http://www.mathworks.com/access/helpdesk/help/techdoc/matlab.html MATLAB online tutorial]&lt;/li&gt;<br /> &lt;li&gt;[ftp://joshua.smcvt.edu/pub/hefferon/book/book.pdf Programmed text in Linear Algebra - ''Hefferon'']<br /> &lt;/ul&gt;<br /> These are all excellent free sources. Please feel free to suggest more.<br /> <br /> ==Functional Neuroanatomy==<br /> Again, there are many excellent resources online! Including:<br /> &lt;ul&gt;<br /> &lt;li&gt;[http://www9.biostr.washington.edu/da.html Washington Interactive Atlas]&lt;/li&gt;<br /> &lt;li&gt;[http://thebrain.mcgill.ca/ McGill Brain Tutorial]&lt;/li&gt;<br /> &lt;li&gt;[http://www.anatomie-amsterdam.nl/sub_sites/anatomie-zenuwwerking/123_neuro/start.htm Amsterdam Brain Atlas]&lt;/li&gt;<br /> &lt;/ul&gt;<br /> <br /> =Concepts and Teaching Plan=<br /> We will start looking at a few papers that use ''images of various kinds to address neuroscientific questions.'' Here, you should be paying especial attention to how the images are used in a theoretical context. Did the investigator pose the question first then collect the data? What is the role of ''a posteriori'' interpretation (reverse inference)? What is assumed about the ground truth of the phenomena exposed by neuroimaging?<br /> <br /> After this, we will begin to look at the properties of neurons that might make them visible to our neuroimaging tools. We will consider signaling in neurons, its energetic costs, and the changes in the cellular milieu that are associated. We will begin to consider the optical properties of neurons and their size scale, and the chemical changes that are associated with neuroal activity. As best possible, I will try to incorporate neurogenetics here to consider cell identification and labeling.<br /> <br /> At the same time, we will start the ''practical work in MATLAB.'' If you are already MATLAB proficient, consider your assignment to include bringing the rest of the class up to speed as quickly as possible so that we can move on. As noted above, MATLAB will be used for our quantitative examples, but it is also a strong standard for image and numerical analysis in the sciences and a relatively easy programming language to use, with a pretty quick startup.<br /> <br /> We will start also, on developing the mathematical tools we will need to carry forward. In the digital age, we are dealing always with very large numbers of data points and are forced to deal with large sample sizes (at the very least, a large number of pixels) and we need means of quantitative summary. Our initial steps will be in very ''basic statistical concepts'' in anticipation of doing more and deeper work later.<br /> <br /> This will be followed by work on ''analytic math'', building to ''transform theory.'' Depending on what I find out about your skills level in maths, we may start with some calculus review, or we may have to schedule one-on-one meetings to balance everyone’s background. The goal here is to develop a framework with which to understand what happens to the ground truth data we try to observe as it is filtered through our imaging tools. There are very powerful mathematical tools that can be applied here, particularly the field known as linear systems analysis that considers ''transfer functions'' and especially ''convolution.'' Each device we build or use can be analyzed, at least in part, within this framework. More importantly, for many classes of systems, the filtering they apply can be inverted – in some cases unblurring and recapturing much of the original data. ''Deconvolution'' is the general rubrick under which we will try to analyze this process.<br /> <br /> Mathematical transforms are, in general, ways to change the representation of equations into forms that are much easier to solve, or that offer additional insight into the underlying properties. We will look at a few transforms, particularly the ''LaPlace Transform'' and the ''Fourier Transform.'' The latter is simply a means of expressing and quantifying the frequencies contained in a signal. The maths for these includes a little bit of trigonometry and some basic calculus. By the time we start on these topics, you should make yourself responsible for knowing how to integrate sines and cosines, and reviewing properties of the natural logarithm, e. I will introduce, in class, the concepts and algebra of imaginary numbers, which we will need as well.<br /> <br /> [[File:Fourier-cat.jpg | x250px]]<br /> <br /> The essential results of the Fourier transform find their way into literally every means we have of neuroimaging, the statistical processing of images, concepts of noise and a host of other applications in neuroscience. I truly believe, that although you may find this material difficult, you will be happy about knowing it for the rest of your career as a scientist, making it well worth the effort.<br /> <br /> Our first direct application of the analytic tools will be in the analysis and then creation of electrical circuits. We do this for several reasons. Unlike many real-world devices, electrical circuit elements: resistors, batteries, capacitors, inductors and operational amplifiers, act very much like their idealized representations, storing and converting energy in very predictable ways. The tools that have grown to analyze such circuit elements are very mature and quite powerful, making prediction of their behavior straightforward. For this reason, many real-world physics and imaging problems are ''modeled'' using electrical circuit elements where we can predict their input-output properties.<br /> <br /> The second reason for looking at electrical circuits is that they are present in more or less every lab instrument you are likely to use. Towards the end of the first quarter, we will build, in class, an EEG system based on your understanding of these devices. This will also give us an entrée into the important study of noise, which is present in any experiments. We will look at the many sources of noise in neuroimaging and experiments, and consider ways in which modeling the noise can help us to reduce it. Conversely, we will discuss ways in which we can study the characteristics of the noise in order to better understand either our devices, or the actual features of our images.<br /> <br /> We will cover principles of optics, emphasizing the issues of resolution, optical spectrum (frequency ranges), distortion and digital imaging. One way to think about the effects of lenses is as convolution filters (''see above'') that ''color'' the signal. Color, as used here, is a rather broad concept. The process of ''whitening'' the signal can be considered a deconvolution. Undoing the lens convolution is a way of removing the blur or distortion produced by a lens. As we go on, we will see this theme of convolution blurring and deconvolution sharpening applied to the many modalities used in modern neuroimaging. Similarly, statistical variance or noise can be reduced or at least better understood in this context, sharpening our statistical inferences and improving detection power.<br /> <br /> Our next foray will be into electroencephalography (EEG), which is a simply a measure of the differences in electrical voltage from point to point on the scalp or brain. In addition to looking at the biological basis of the EEG, we will build and test an EEG system in class and we will look at some software approaches to interpreting the EEG both as spatially-resolved (''i.e., image'') data and as cognitive/physiological signals.<br /> <br /> =Grading=<br /> Your final grades will be determined by the problem sets, the midterm and final and by your class participation. Generally the rubrick is:<br /> :*Participation 10%<br /> :*Problem Sets 25%<br /> :*Midterm 30%<br /> :*Final 35%.<br /> As you can see, your participation in the class is of major import, as I believe that everyone learns from other people's questions and comments.<br /> <br /> As M284 is a required course for some students continuation in several Ph.D. programs, grading will necessarily be rigorous.</div>

Alenartowicz https://www.ccn.ucla.edu/wiki/index.php?title=Principles_of_Neuroimaging_A_-_2016&diff=3228 2016-11-29T04:04:56Z

<p>Alenartowicz: /* Week 10 - Data Processing */</p> <hr /> <div>Principles of Neuroimaging A, Fall, 2016 - Class Schedule and Syllabus<br /> <br /> <br /> ==='''Neuroimaging journal Club (''required for NITP certificate'')'''===<br /> Contact: Katherine Lawrence (katherine.E.Lawrence@ucla.edu) or Janelle Liu (janelle.j.liu@ucla.edu), Faculty Sponsor: Jamie Feusner (JFeusner@mednet.ucla.edu)<br /> <br /> ==This schedule ''will'' change!==<br /> <br /> :'''[[Principles_of_Neuroimaging_-_2016-2017 | Back to main course page for Principles of Neuroimaging]]'''<br /> <br /> :'''Coming up next!: [[Principles_of_Neuroimaging_B_-_2017 | M284B Principles of Neuroimaging B]]'''<br /> <br /> =Course Reading=<br /> ===Required Reading===<br /> :'''Signal Processing for Neuroscientists''' by ''Wim van Drongelen''<br /> ::This can be found as a PDF on scribd.com, for a small fee of $8.99<br /> <br /> ===Supplemental Reading===<br /> :'''Matlab for Neuroscientists'''<br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/88212458/Matlab-Matlab-for-Neuroscientists<br /> <br /> :'''Cartoon Guide to Statistics''' <br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/148072668/Cartoon-Guide-to-Statistics<br /> <br /> :'''NOTE:''' if you subscribe for a Scribd account for a day, you can download as many documents as you like for one fee.<br /> <br /> <br /> =Week 1: Orientation to Neuro-imaging, Neurons, Brains=<br /> <br /> ==''Monday 9/26/16'' ==<br /> ===- Images. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284AImages-intro.pdf | Lecture Slides Images]]<br /> <br /> ==''Wednesday 9/28/16''==<br /> ===- Neurons &amp; Signaling. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> In this first class we will review the basics of neurophysiology with an eye towards what signals of brain function might be visible to the neuroimager. We will discuss information coding, energetics, size and time scales.<br /> [[Image:Neurons.jpg|right]]<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:TheActiveBrain.pdf | The Active Brain]]<br /> :*[[media:NeuronFunctionM284-2016_SM.pdf | Neuron function slides shown in class]]<br /> :*[http://www.science.smith.edu/departments/neurosci/courses/bio330/squid.html Videos of giant squid experiments]<br /> ''Suggested Further Reading''<br /> :*[http://ccn.ucla.edu/wiki/images/5/5a/CAVEAT_LECTOR.pdf Caveat Lector - the misuse of neuroimaging]<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Protected/Kosslyn1999.pdf &quot;If Neuroimaging is the Answer, What is the Question?&quot; Kosslyn, 1999]<br /> :*[http://da.biostr.washington.edu:80/cgi-bin/DA/PageMaster?atlas:NeuroSyllabus+ffpathIndex/Splash^Page^Syllabus+2 Neuroanatomy Programmed Learning]<br /> :*[http://www.amazon.com/Fundamental-Neuroscience-Second-Larry-Squire/dp/0126603030 Squire, Fundamentals of Neuroscience]<br /> :*[http://www.amazon.com/Principles-Neural-Science-Eric-Kandel/dp/0838577016 Kandel, et al., &quot;Principles of Neural Science&quot;]<br /> :This paper, by Malhi, is a nice orientation in methods of neuroimaging. *[http://www.ccn.ucla.edu/wiki/images/f/f2/Malhi2007.pdf Making sense of neuroimaging in psychiatry]<br /> :*[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1359308/pdf/jphysiol01232-0142.pdf Replacement of the axoplasm of giant nerve fibres with artificial solutions]<br /> <br /> =Week 2: Linear Systems, Convolution, Fourier Transforms=<br /> ==''Wednesday 10/3/16''==<br /> <br /> ===- Linear Systems I. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ==''Wednesday 10/5/16''==<br /> ===- Linear Systems II. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> Why the emphasis on Linear Systems? Because they are actually ''easy'' (as compared to non-linear systems, which are not.) As we go through this course, we will see many ways in which linear systems theory is applied to:<br /> :Modeling of Neural Systems<br /> :Extraction of Signal from Noise<br /> :Design of Circuits<br /> :Image Enhancement<br /> :Understanding of Image artifacts, and others.<br /> <br /> Linear systems analysis is one of the great technologies of the 20th and 21st century. It is now the basis for virtually all electronics design, and its extension into the discrete (digital) domain is the basis for most of modern signal processing. <br /> <br /> In our specific case, we will use these few basic principles of linear systems to understand both the instruments we use and the neuroimaging signals we collect. When you have mastered this material, you should be in a much better position to model the systems that you study in order to develop an approach to studying them.<br /> <br /> Here is [http://www.brainmapping.org/NITP/PNA/Readings/ImaginaryNumbers.pdf A primer I wrote on imaginary numbers] that might be a helpful review.<br /> <br /> There is a nice [http://en.wikibooks.org/wiki/Calculus Wikibook on Calculus].<br /> <br /> ''Required Readings''<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapter 1<br /> :*[[media:MathematicalTools2016.pdf | Mathematical Tools]]<br /> <br /> ''Suggested Further Reading''<br /> :'''Introduction to matlab'''<br /> <br /> ''Slides shown in class''<br /> :[[media:LinearityM284A-2016.pdf | Linearity and the Fourier Transform]]<br /> <br /> Please see [http://www.brainmapping.org/NITP/PNA/html/Linearity.html MATLAB linearity demo]<br /> <br /> If you are the type who sees beauty in mathematics, the Euler identity may be one of the most beautiful pieces of math in the world.<br /> <br /> =Week 3: Math &amp; Circuits I=<br /> <br /> ==''Monday 10/10/16''==<br /> === - Circuits I. Speaker: Cameron Rodriguez ===<br /> :*[[media:Circuits_2016.pdf | Circuit slides shown in class]]<br /> <br /> ==''Wednesday 10/12/16''==<br /> === - Circuits II. ''Speaker'': Cameron Rodriguez ===<br /> <br /> Why circuits?<br /> :(Virtually) Every device you use in your research is electronic. You access your primary data only indirectly<br /> :The device you ''really'' want in your lab doesn't exist. You very well may have to make it.<br /> :There are electronic analogs to most of the linear systems that you have so far studied (and ''vice versa'' - the tools you now understand can be used to analyze and predict circuit behavior).<br /> <br /> :If you have not had any of this background, you might want to have a look at this handout, [[Media:Electricity.pdf|Electrical Circuits]], in advance. There are near infinite numbers of resources on the web that cover similar material (near enough to infinite that by the time you read all of them, there would be a whole new set.) I have recently come across a link to [http://www.allaboutcircuits.com/ Online Books: All About Circuits] ''IF'' you want practical hands-on knowledge about this material, my all-time favorite text is [http://www.google.com/products/catalog?hl=en&amp;client=safari&amp;rls=en-us&amp;ei=uVSPSfaxE5nMsAPf-tmSCQ&amp;resnum=1&amp;q=art+of+electronics&amp;um=1&amp;ie=UTF-8&amp;cid=8820839049329255765#ps-sellers &quot;Horowitz and Hill: ''The Art of Electronics.''&quot;] The latest edition, however, is dated 1989 and a new third edition is promised. I have therefore stopped short of recommending a purchase unless your need to make circuits is immediate. In this book, you will find an excellent education on the fundamental principles of electrical circuits and an incredible compendium of practical data, such as how to assemble circuit boards, how to make measurements, etc...) <br /> <br /> Readings:<br /> :*[http://www.ti.com/lit/an/sloa093/sloa093.pdf Filter Design in 30 Seconds]<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapter 2 and 10<br /> :*[https://www.circuitlab.com/ Circuit Lab ] A Free Circuit Web Base Simulator<br /> **You may or may not find this comprehensible without chapters 5 through 9.<br /> <br /> We will discuss:<br /> :*Passive Circuit Elements: Resistors, Capacitors, Inductors<br /> :*Gain<br /> :*Transformers<br /> :*Rectifiers<br /> :*Active Elements<br /> ::- ''Amplifiers''<br /> ::- ''Transistors''<br /> ::- ''Op Amps''<br /> :*Solutions with Matrices<br /> ''Suggested Further Reading''<br /> :*[http://video.google.com/videoplay?docid=5645396659673218353&amp;q=Physics+for+Future+Presidents+Electricity&amp;total=5&amp;start=0&amp;num=10&amp;so=0&amp;type=search&amp;plindex=0#0h20m30s Video intro lecture on charge, current and voltage].<br /> :*[[media:Electricity_Basics.pdf‎ | Well organized text on electrical concepts by Tony R. Kuphaldt]]<br /> :*[http://en.wikibooks.org/wiki/Circuit_Theory Circuit Theory - Wikibook]<br /> <br /> '''Suggested, Optional Readings from [http://www.dspguide.com DSPguide.com]:'''<br /> :*[http://www.dspguide.com/CH5.PDF Linear Systems]<br /> :*[http://www.dspguide.com/CH6.PDF Convolution]<br /> :*[http://www.dspguide.com/CH8.PDF Discrete Fourier Transform (DFT)]<br /> :''Note: These chapters are light on math and try to focus on a conceptual understanding''<br /> <br /> Time and Frequency / Spectral Filters<br /> <br /> ----<br /> Practice using the Fourier transform:<br /> :[http://www.brainmapping.org/NITP/PNA/ConvFThtml/ConvolutionWorksheet.pdf Fourier transform and Convolution Worksheet]. &lt;!-- [http://www.brainmapping.org/NITP/PNA/ConvFThtml/ConvFT.html (''Solutions'').] --&gt;<br /> :[http://www.brainmapping.org/NITP/PNA/ConvFThtml/Something.wav Sound file for worksheet above.]<br /> <br /> Other<br /> :*[http://www.youtube.com/watch?feature=player_embedded&amp;v=7uHfjpU3OH0#! Getting Started with Arduino]<br /> <br /> =Week 4 Signal Processing=<br /> <br /> ==''Monday 10/17/16''==<br /> === - Signal Processing. ''Speaker'': Cameron Rodriguez ===<br /> <br /> :*[[media:Signals1-PreProcessing1_2016.pdf | Signal Pre Processing Slide Set 1 shown in class]]<br /> <br /> <br /> ==''Wednesday 10/19/16''==<br /> === - Noise ''Speaker'': Cameron Rodriguez ===<br /> <br /> :*[[media:OneD_signal_Filter_Ex.pdf | Matlab Signal Tutorial printout]]<br /> <br /> It is what you ''don't'' want - usually - but things change in quantized systems<br /> :Additive noise<br /> :White Noise<br /> :Boltzmann noise<br /> :Colored Noise<br /> :Gaussian Noise<br /> :Coherent noise<br /> :Sampling Errors<br /> :Aliasing<br /> :Quantization noise<br /> :Spectral filtering<br /> <br /> Noise comes in all shapes and colors. It is present in every measurement we make, from an EEG voltage to an estimate of the effects of dopamine on forebrain signal. Our best weapons are an understanding of the statistical properties of noise, the sources of noise and the ways to control it. Noise in the discrete digital domain is special, as it is both ''created'' by digitization and amplified by sampling.<br /> <br /> Readings:<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapters 2 through 4<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Noise.pdf Noise Slides]<br /> <br /> =Week 5 - Information &amp; Statistical Theory =<br /> <br /> ==''Monday 10/24/16 - in C8-177''==<br /> === Noise Cont' &amp; Information Theory Speakers: Cameron Rodriguez &amp; John Villasenor===<br /> <br /> ==''Wednesday 10/26/16''==<br /> ===Statistical Theory ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> We will consider the general problems of statistical inference, with a concentration on developing an intuitive understanding of statistical concepts.<br /> [[Image:MeasureForMeasure.jpg|right]]<br /> <br /> Suggested reading<br /> :*[http://www.statsoft.com/textbook/stbasic.html Statsoft online text (''free'')]<br /> :*[http://www.amazon.com/Cartoon-Guide-Statistics-Larry-Gonick/dp/0062731025 The Cartoon Guide to Statistics - Gonick $17.95 new]<br /> :The latter teaches stats at what I feel to be the right level - developing intuitions about the kinds of questions that can be answered using stats and about the statistical tests and measures<br /> <br /> :'''Problem Set 3 - Statistics in matlab'''<br /> ::[[media: Problem_Set_1.doc|Problem set using stats and MATLAB]]<br /> ::[[media: Problem_Set_1B.doc|More practice with stats and MATLAB]]<br /> <br /> <br /> =MIDTERM=<br /> <br /> <br /> =Week 6 - Neurophysiology=<br /> ==''Monday 10/31/16''==<br /> === Electrophysiology I ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> We will examine our first imaging modality, EEG (and MEG).<br /> <br /> ::[[media: EEGNeurophys_161102.pdf|Slides for Neurophysiology Lectures]]<br /> <br /> ==''Wednesday 11/2/16''==<br /> === Electrophysiology II ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> Continuing with neurophysiology. ERPs &amp; Source Localization.<br /> <br /> =Week 7 - Neurophysiology =<br /> ==''Monday 11/7/16''==<br /> === Electrophysiology III ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> Continuing with neurophysiology. Connectivity &amp; Oscillations.<br /> <br /> ==''Wednesday 11/9/16''==<br /> ===Intracranial Recordings ''Speaker'': [http://neurosurgery.ucla.edu/neuromodulation-neuroimaging-lab Nanthia Suthana]===<br /> <br /> ::[[media: Suthana.pdf|Slides for Intracranial Lecture]]<br /> ::[[media: LachauxIntracranial.pdf|Lachaux et al Reading]]<br /> ::[[media: HarrisIntracranial.pdf|Harris et al Reading]]<br /> <br /> =Week 8 - Practical Neurophysiology=<br /> ==''Monday 11/14/16''==<br /> ===Building an EEG System - Circuit 1 - Going over circuit ''Speaker'': Cameron Rodriguez===<br /> <br /> :*[https://learn.sparkfun.com/tutorials/how-to-solder Through holesoldering].<br /> :*[https://learn.sparkfun.com/tutorials/pcb-basics Printed Circuit Board Basics].<br /> :*[https://learn.sparkfun.com/tutorials/how-to-install-and-setup-eagle Installing and Setting Up Eagle].<br /> :*[https://learn.sparkfun.com/tutorials/using-eagle-schematic Using Eagle Schematic].<br /> :*[https://learn.sparkfun.com/tutorials/using-eagle-board-layoutc Using Eagle Board Layout].<br /> <br /> :*[https://www.sparkfun.com/videos#pete/NJKZZArjdg8 PCB Layout 1 - According to Pete];<br /> :*[https://www.sparkfun.com/videos#pete/JANZsjRiM3w PCB Layout 2 - According to Pete];<br /> <br /> ==''Wednesday 11/16/16''==<br /> ===Data Processing Hands On ''Speaker'': Cameron Rodriguez===<br /> <br /> =Week 9 - Data Acquisition =<br /> ==''Monday 11/21/16 - in C8-177''==<br /> ===Data Acquisition ''Speaker'': Agatha Lenartowicz===<br /> Please meet in CCN suite.<br /> <br /> ==''Wednesday 11/23/16''==<br /> ===Thankgiving Wed - No Class!! ===<br /> Happy Thanksgiving!<br /> <br /> =Week 10 - Data Processing =<br /> <br /> *Please complete class evaluations via [https://my.ucla.edu/ '''MyUCLA''']. <br /> *Also we have an inhouse feedback form regarding the course syllabus: [https://docs.google.com/forms/d/e/1FAIpQLSd8xn340PxZRWb3-GQ__ZisYwFWoSgmYrcwyjvyBY5JV_mxnQ/viewform '''Feedback Form''']<br /> <br /> Your feedback is invaluable to us!!<br /> <br /> ==''Monday 11/28/16''==<br /> ===Midterm Review, Data Processing ''Speaker'': Agatha Lenartowicz===<br /> Hands on practice with EEG data, in Matlab using EEGLAB. Lab materials distributed by email 11/27/16.<br /> <br /> ==''Wednesady 11/30/16''==<br /> ===Data Processing ''Speaker'': Agatha Lenartowicz===<br /> <br /> Hands on practice with EEG data, in Matlab using EEGLAB.<br /> <br /> =Week 11 - Finals Week=<br /> Final will be distributed 11/2/16.<br /> <br /> ==''Monday 11/5/16''==<br /> ===Final Exam===<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!--<br /> SPARE CONTENT / PREVIOUS YEARS<br /> <br /> =Week 3: Stats begins!=<br /> <br /> ==''Monday 10/12/15''==<br /> <br /> ===- Statistical Fundamentals. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> :*[[media: CohenClassIntroStats11_25_13.pdf‎ | Slides used in class (set 1)]]<br /> <br /> ''Review of'':<br /> :*Descriptive Statistics: mean, mode, variance, standard deviation<br /> :*Statistical Inference. The Binomial and Normal Distribution<br /> :*Basic Tests: t-test, linear correlation<br /> :*Modeling and non-linear relations<br /> :*Bayes rule<br /> <br /> <br /> ==''Wednesday 10/14/15''==<br /> ===- Statistical Fundamentals II. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> =Week 4 - Stats and circuits=<br /> ==''Monday 10/19/15''==<br /> ===- Statistical Fundamentals III. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> ==''Wednesday 10/21/15''==<br /> ===Circuits continued: Active devices. [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ===Optics I. ''Speaker'': [mailto:zdeis@seas.ucla.edu Zachary Taylor]===<br /> [[Image:Reflection.jpg|right]]<br /> The overall goal of this lecture is to establish that:<br /> ''- Physical constants have tangible meanings''<br /> ''- Plane waves form a physically unrealizable but extremely good approximation to real systems''<br /> ''- Boundaries bend light''<br /> ''- Physical constants, plane wave mechanics, and boundaries can be used to describe the operation of a lens''<br /> ''- The PSF gives a good indication of the overall performance of an imaging system''<br /> ''- All of these concepts have analogues in other areas of engineering (ie circuits, mechanical vibrations, etc.)''''<br /> <br /> '''Outline:'''<br /> :* Constitutive parameters (ε, μ, η, n, etc.)<br /> :* Plane wave basics<br /> :* Plane waves at boundaries<br /> :* Lenses<br /> :* Advanced imaging properties of lenses<br /> :* Point spread function.<br /> <br /> ''Required Readings''<br /> Zach has very kindly agreed to post his [http://www.brainmapping.org/NITP/PNA/Readings/OpticsTaylor3-10-10.pdf Optics lecture notes].<br /> ''Suggested Further Reading''<br /> <br /> <br /> <br /> <br /> ==''Wednesday 11/19/14''== <br /> ===Optogenetics. ''Speaker'': [http://faculty.neuroscience.ucla.edu/institution/personnel?personnel_id=47031 Tom Otis]===<br /> <br /> <br /> =Week 8: =<br /> ==''Monday 12/1/14 ''==<br /> ===- Statistics for Imaging. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> [[image:BVTradeoff.jpg|right]]<br /> ''Required Readings''<br /> :*[[media: Mumford_stat_modeling.pdf | Statistical Modeling and Inference (pdf)]]<br /> :*[[media: CohenClassSlides12_2_13.pdf | Slides used in class 12/2/14 and 12/4/14]]<br /> <br /> :*The General Linear Model<br /> *Linear Algebra applied to Statistical Solutions<br /> :*Analysis of Variance<br /> <br /> ==''Wednesday 11/20/14 ...''==<br /> === - Statistics for Imaging II. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> *Fixed and Random Effects<br /> *Repeated measures<br /> :*Bonferroni and Other Corrections<br /> *Non-Parametric Methods<br /> *Autocorrelation<br /> *Unknown Distributions<br /> <br /> ''Required Readings''<br /> :*[[media: CohenClassSlides12_2_13.pdf‎ | same as 12/2/14]]<br /> <br /> ''Suggested Further Reading''<br /> <br /> :*[http://ccn.ucla.edu/media/PNI20121121.mp4 Lecture Video - To be Posted Shortly - Please Right Click and Save rather than Stream - Thank you Alan and Edward!]<br /> <br /> =Week 9: =<br /> ==''Monday 11/25/14''==<br /> === - TBD. [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> ==''Wednesday 11/27/14''==<br /> ===- The Organization of the Human Brain. ''Speaker'': [http://ccn.ucla.edu/bmcweb/bmc_bios/SusanBookheimer/ Susan Bookheimer]===<br /> <br /> We will discuss the general organization of the human brain, and the regional specialization of cortical areas. The emphasis will be on understanding principles of organization:<br /> *Phylogenetic Layering<br /> *Functional Specialization<br /> *Principles Divisions of the Brain<br /> *Brain Systems<br /> <br /> ''Required Readings''<br /> :*[http://da.biostr.washington.edu:80/cgi-bin/DA/PageMaster?atlas:NeuroSyllabus+ffpathIndex/Splash^Page^Syllabus+2 Neuroanatomy Programmed Learning]<br /> :*[[media:Cognitive_science_and_neuro_2012.pdf | Slides shown in Class UPDATED 2012/10/17]]<br /> ''Suggested Further Reading''<br /> <br /> :*[[media:PNIA_2012_PS1_Soln.pdf‎ | Solution to Problem Set 1]]<br /> ===- Human Electrophysiology ''Speakers'': [http://greenlab.npih.ucla.edu/ROSTER.html Agatha Lenartowicz], [http://dgsom.healthsciences.ucla.edu/institution/personnel?personnel_id=9140 John Stern]===<br /> <br /> ''Evoked Responses'' - Guest Lecturer: [http://greenlab.npih.ucla.edu/ROSTER.html Jonathan Wynn]<br /> *A look at real EEG data<br /> *Preprocessing:<br /> **filtering<br /> **artifact detection/removal<br /> *averaging<br /> *single events<br /> *interpretation<br /> <br /> ''Clinical EEG'' - Guest Lecturer: [http://dgsom.healthsciences.ucla.edu/institution/personnel?personnel_id=9140 John Stern]<br /> *Normal and Abnormal EEG<br /> *EEG as a marker for brain state<br /> **sleep staging<br /> **alpha and relaxation<br /> *Neurofeedback??? --&gt;</div>

Alenartowicz https://www.ccn.ucla.edu/wiki/index.php?title=Principles_of_Neuroimaging_A_-_2016&diff=3227 2016-11-29T04:03:31Z

<p>Alenartowicz: /* Data Processing Speaker: Cameron Rodriguez/Agatha Lenartowicz */</p> <hr /> <div>Principles of Neuroimaging A, Fall, 2016 - Class Schedule and Syllabus<br /> <br /> <br /> ==='''Neuroimaging journal Club (''required for NITP certificate'')'''===<br /> Contact: Katherine Lawrence (katherine.E.Lawrence@ucla.edu) or Janelle Liu (janelle.j.liu@ucla.edu), Faculty Sponsor: Jamie Feusner (JFeusner@mednet.ucla.edu)<br /> <br /> ==This schedule ''will'' change!==<br /> <br /> :'''[[Principles_of_Neuroimaging_-_2016-2017 | Back to main course page for Principles of Neuroimaging]]'''<br /> <br /> :'''Coming up next!: [[Principles_of_Neuroimaging_B_-_2017 | M284B Principles of Neuroimaging B]]'''<br /> <br /> =Course Reading=<br /> ===Required Reading===<br /> :'''Signal Processing for Neuroscientists''' by ''Wim van Drongelen''<br /> ::This can be found as a PDF on scribd.com, for a small fee of $8.99<br /> <br /> ===Supplemental Reading===<br /> :'''Matlab for Neuroscientists'''<br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/88212458/Matlab-Matlab-for-Neuroscientists<br /> <br /> :'''Cartoon Guide to Statistics''' <br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/148072668/Cartoon-Guide-to-Statistics<br /> <br /> :'''NOTE:''' if you subscribe for a Scribd account for a day, you can download as many documents as you like for one fee.<br /> <br /> <br /> =Week 1: Orientation to Neuro-imaging, Neurons, Brains=<br /> <br /> ==''Monday 9/26/16'' ==<br /> ===- Images. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284AImages-intro.pdf | Lecture Slides Images]]<br /> <br /> ==''Wednesday 9/28/16''==<br /> ===- Neurons &amp; Signaling. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> In this first class we will review the basics of neurophysiology with an eye towards what signals of brain function might be visible to the neuroimager. We will discuss information coding, energetics, size and time scales.<br /> [[Image:Neurons.jpg|right]]<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:TheActiveBrain.pdf | The Active Brain]]<br /> :*[[media:NeuronFunctionM284-2016_SM.pdf | Neuron function slides shown in class]]<br /> :*[http://www.science.smith.edu/departments/neurosci/courses/bio330/squid.html Videos of giant squid experiments]<br /> ''Suggested Further Reading''<br /> :*[http://ccn.ucla.edu/wiki/images/5/5a/CAVEAT_LECTOR.pdf Caveat Lector - the misuse of neuroimaging]<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Protected/Kosslyn1999.pdf &quot;If Neuroimaging is the Answer, What is the Question?&quot; Kosslyn, 1999]<br /> :*[http://da.biostr.washington.edu:80/cgi-bin/DA/PageMaster?atlas:NeuroSyllabus+ffpathIndex/Splash^Page^Syllabus+2 Neuroanatomy Programmed Learning]<br /> :*[http://www.amazon.com/Fundamental-Neuroscience-Second-Larry-Squire/dp/0126603030 Squire, Fundamentals of Neuroscience]<br /> :*[http://www.amazon.com/Principles-Neural-Science-Eric-Kandel/dp/0838577016 Kandel, et al., &quot;Principles of Neural Science&quot;]<br /> :This paper, by Malhi, is a nice orientation in methods of neuroimaging. *[http://www.ccn.ucla.edu/wiki/images/f/f2/Malhi2007.pdf Making sense of neuroimaging in psychiatry]<br /> :*[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1359308/pdf/jphysiol01232-0142.pdf Replacement of the axoplasm of giant nerve fibres with artificial solutions]<br /> <br /> =Week 2: Linear Systems, Convolution, Fourier Transforms=<br /> ==''Wednesday 10/3/16''==<br /> <br /> ===- Linear Systems I. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ==''Wednesday 10/5/16''==<br /> ===- Linear Systems II. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> Why the emphasis on Linear Systems? Because they are actually ''easy'' (as compared to non-linear systems, which are not.) As we go through this course, we will see many ways in which linear systems theory is applied to:<br /> :Modeling of Neural Systems<br /> :Extraction of Signal from Noise<br /> :Design of Circuits<br /> :Image Enhancement<br /> :Understanding of Image artifacts, and others.<br /> <br /> Linear systems analysis is one of the great technologies of the 20th and 21st century. It is now the basis for virtually all electronics design, and its extension into the discrete (digital) domain is the basis for most of modern signal processing. <br /> <br /> In our specific case, we will use these few basic principles of linear systems to understand both the instruments we use and the neuroimaging signals we collect. When you have mastered this material, you should be in a much better position to model the systems that you study in order to develop an approach to studying them.<br /> <br /> Here is [http://www.brainmapping.org/NITP/PNA/Readings/ImaginaryNumbers.pdf A primer I wrote on imaginary numbers] that might be a helpful review.<br /> <br /> There is a nice [http://en.wikibooks.org/wiki/Calculus Wikibook on Calculus].<br /> <br /> ''Required Readings''<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapter 1<br /> :*[[media:MathematicalTools2016.pdf | Mathematical Tools]]<br /> <br /> ''Suggested Further Reading''<br /> :'''Introduction to matlab'''<br /> <br /> ''Slides shown in class''<br /> :[[media:LinearityM284A-2016.pdf | Linearity and the Fourier Transform]]<br /> <br /> Please see [http://www.brainmapping.org/NITP/PNA/html/Linearity.html MATLAB linearity demo]<br /> <br /> If you are the type who sees beauty in mathematics, the Euler identity may be one of the most beautiful pieces of math in the world.<br /> <br /> =Week 3: Math &amp; Circuits I=<br /> <br /> ==''Monday 10/10/16''==<br /> === - Circuits I. Speaker: Cameron Rodriguez ===<br /> :*[[media:Circuits_2016.pdf | Circuit slides shown in class]]<br /> <br /> ==''Wednesday 10/12/16''==<br /> === - Circuits II. ''Speaker'': Cameron Rodriguez ===<br /> <br /> Why circuits?<br /> :(Virtually) Every device you use in your research is electronic. You access your primary data only indirectly<br /> :The device you ''really'' want in your lab doesn't exist. You very well may have to make it.<br /> :There are electronic analogs to most of the linear systems that you have so far studied (and ''vice versa'' - the tools you now understand can be used to analyze and predict circuit behavior).<br /> <br /> :If you have not had any of this background, you might want to have a look at this handout, [[Media:Electricity.pdf|Electrical Circuits]], in advance. There are near infinite numbers of resources on the web that cover similar material (near enough to infinite that by the time you read all of them, there would be a whole new set.) I have recently come across a link to [http://www.allaboutcircuits.com/ Online Books: All About Circuits] ''IF'' you want practical hands-on knowledge about this material, my all-time favorite text is [http://www.google.com/products/catalog?hl=en&amp;client=safari&amp;rls=en-us&amp;ei=uVSPSfaxE5nMsAPf-tmSCQ&amp;resnum=1&amp;q=art+of+electronics&amp;um=1&amp;ie=UTF-8&amp;cid=8820839049329255765#ps-sellers &quot;Horowitz and Hill: ''The Art of Electronics.''&quot;] The latest edition, however, is dated 1989 and a new third edition is promised. I have therefore stopped short of recommending a purchase unless your need to make circuits is immediate. In this book, you will find an excellent education on the fundamental principles of electrical circuits and an incredible compendium of practical data, such as how to assemble circuit boards, how to make measurements, etc...) <br /> <br /> Readings:<br /> :*[http://www.ti.com/lit/an/sloa093/sloa093.pdf Filter Design in 30 Seconds]<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapter 2 and 10<br /> :*[https://www.circuitlab.com/ Circuit Lab ] A Free Circuit Web Base Simulator<br /> **You may or may not find this comprehensible without chapters 5 through 9.<br /> <br /> We will discuss:<br /> :*Passive Circuit Elements: Resistors, Capacitors, Inductors<br /> :*Gain<br /> :*Transformers<br /> :*Rectifiers<br /> :*Active Elements<br /> ::- ''Amplifiers''<br /> ::- ''Transistors''<br /> ::- ''Op Amps''<br /> :*Solutions with Matrices<br /> ''Suggested Further Reading''<br /> :*[http://video.google.com/videoplay?docid=5645396659673218353&amp;q=Physics+for+Future+Presidents+Electricity&amp;total=5&amp;start=0&amp;num=10&amp;so=0&amp;type=search&amp;plindex=0#0h20m30s Video intro lecture on charge, current and voltage].<br /> :*[[media:Electricity_Basics.pdf‎ | Well organized text on electrical concepts by Tony R. Kuphaldt]]<br /> :*[http://en.wikibooks.org/wiki/Circuit_Theory Circuit Theory - Wikibook]<br /> <br /> '''Suggested, Optional Readings from [http://www.dspguide.com DSPguide.com]:'''<br /> :*[http://www.dspguide.com/CH5.PDF Linear Systems]<br /> :*[http://www.dspguide.com/CH6.PDF Convolution]<br /> :*[http://www.dspguide.com/CH8.PDF Discrete Fourier Transform (DFT)]<br /> :''Note: These chapters are light on math and try to focus on a conceptual understanding''<br /> <br /> Time and Frequency / Spectral Filters<br /> <br /> ----<br /> Practice using the Fourier transform:<br /> :[http://www.brainmapping.org/NITP/PNA/ConvFThtml/ConvolutionWorksheet.pdf Fourier transform and Convolution Worksheet]. &lt;!-- [http://www.brainmapping.org/NITP/PNA/ConvFThtml/ConvFT.html (''Solutions'').] --&gt;<br /> :[http://www.brainmapping.org/NITP/PNA/ConvFThtml/Something.wav Sound file for worksheet above.]<br /> <br /> Other<br /> :*[http://www.youtube.com/watch?feature=player_embedded&amp;v=7uHfjpU3OH0#! Getting Started with Arduino]<br /> <br /> =Week 4 Signal Processing=<br /> <br /> ==''Monday 10/17/16''==<br /> === - Signal Processing. ''Speaker'': Cameron Rodriguez ===<br /> <br /> :*[[media:Signals1-PreProcessing1_2016.pdf | Signal Pre Processing Slide Set 1 shown in class]]<br /> <br /> <br /> ==''Wednesday 10/19/16''==<br /> === - Noise ''Speaker'': Cameron Rodriguez ===<br /> <br /> :*[[media:OneD_signal_Filter_Ex.pdf | Matlab Signal Tutorial printout]]<br /> <br /> It is what you ''don't'' want - usually - but things change in quantized systems<br /> :Additive noise<br /> :White Noise<br /> :Boltzmann noise<br /> :Colored Noise<br /> :Gaussian Noise<br /> :Coherent noise<br /> :Sampling Errors<br /> :Aliasing<br /> :Quantization noise<br /> :Spectral filtering<br /> <br /> Noise comes in all shapes and colors. It is present in every measurement we make, from an EEG voltage to an estimate of the effects of dopamine on forebrain signal. Our best weapons are an understanding of the statistical properties of noise, the sources of noise and the ways to control it. Noise in the discrete digital domain is special, as it is both ''created'' by digitization and amplified by sampling.<br /> <br /> Readings:<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapters 2 through 4<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Noise.pdf Noise Slides]<br /> <br /> =Week 5 - Information &amp; Statistical Theory =<br /> <br /> ==''Monday 10/24/16 - in C8-177''==<br /> === Noise Cont' &amp; Information Theory Speakers: Cameron Rodriguez &amp; John Villasenor===<br /> <br /> ==''Wednesday 10/26/16''==<br /> ===Statistical Theory ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> We will consider the general problems of statistical inference, with a concentration on developing an intuitive understanding of statistical concepts.<br /> [[Image:MeasureForMeasure.jpg|right]]<br /> <br /> Suggested reading<br /> :*[http://www.statsoft.com/textbook/stbasic.html Statsoft online text (''free'')]<br /> :*[http://www.amazon.com/Cartoon-Guide-Statistics-Larry-Gonick/dp/0062731025 The Cartoon Guide to Statistics - Gonick $17.95 new]<br /> :The latter teaches stats at what I feel to be the right level - developing intuitions about the kinds of questions that can be answered using stats and about the statistical tests and measures<br /> <br /> :'''Problem Set 3 - Statistics in matlab'''<br /> ::[[media: Problem_Set_1.doc|Problem set using stats and MATLAB]]<br /> ::[[media: Problem_Set_1B.doc|More practice with stats and MATLAB]]<br /> <br /> <br /> =MIDTERM=<br /> <br /> <br /> =Week 6 - Neurophysiology=<br /> ==''Monday 10/31/16''==<br /> === Electrophysiology I ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> We will examine our first imaging modality, EEG (and MEG).<br /> <br /> ::[[media: EEGNeurophys_161102.pdf|Slides for Neurophysiology Lectures]]<br /> <br /> ==''Wednesday 11/2/16''==<br /> === Electrophysiology II ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> Continuing with neurophysiology. ERPs &amp; Source Localization.<br /> <br /> =Week 7 - Neurophysiology =<br /> ==''Monday 11/7/16''==<br /> === Electrophysiology III ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> Continuing with neurophysiology. Connectivity &amp; Oscillations.<br /> <br /> ==''Wednesday 11/9/16''==<br /> ===Intracranial Recordings ''Speaker'': [http://neurosurgery.ucla.edu/neuromodulation-neuroimaging-lab Nanthia Suthana]===<br /> <br /> ::[[media: Suthana.pdf|Slides for Intracranial Lecture]]<br /> ::[[media: LachauxIntracranial.pdf|Lachaux et al Reading]]<br /> ::[[media: HarrisIntracranial.pdf|Harris et al Reading]]<br /> <br /> =Week 8 - Practical Neurophysiology=<br /> ==''Monday 11/14/16''==<br /> ===Building an EEG System - Circuit 1 - Going over circuit ''Speaker'': Cameron Rodriguez===<br /> <br /> :*[https://learn.sparkfun.com/tutorials/how-to-solder Through holesoldering].<br /> :*[https://learn.sparkfun.com/tutorials/pcb-basics Printed Circuit Board Basics].<br /> :*[https://learn.sparkfun.com/tutorials/how-to-install-and-setup-eagle Installing and Setting Up Eagle].<br /> :*[https://learn.sparkfun.com/tutorials/using-eagle-schematic Using Eagle Schematic].<br /> :*[https://learn.sparkfun.com/tutorials/using-eagle-board-layoutc Using Eagle Board Layout].<br /> <br /> :*[https://www.sparkfun.com/videos#pete/NJKZZArjdg8 PCB Layout 1 - According to Pete];<br /> :*[https://www.sparkfun.com/videos#pete/JANZsjRiM3w PCB Layout 2 - According to Pete];<br /> <br /> ==''Wednesday 11/16/16''==<br /> ===Data Processing Hands On ''Speaker'': Cameron Rodriguez===<br /> <br /> =Week 9 - Data Acquisition =<br /> ==''Monday 11/21/16 - in C8-177''==<br /> ===Data Acquisition ''Speaker'': Agatha Lenartowicz===<br /> Please meet in CCN suite.<br /> <br /> ==''Wednesday 11/23/16''==<br /> ===Thankgiving Wed - No Class!! ===<br /> Happy Thanksgiving!<br /> <br /> =Week 10 - Data Processing =<br /> <br /> *Please complete class evaluations via myucla. <br /> *Also we have an inhouse feedback form regarding the course syllabus: [https://docs.google.com/forms/d/e/1FAIpQLSd8xn340PxZRWb3-GQ__ZisYwFWoSgmYrcwyjvyBY5JV_mxnQ/viewform Feedback Link]<br /> <br /> Your feedback is invaluable to us!!<br /> <br /> ==''Monday 11/28/16''==<br /> ===Midterm Review, Data Processing ''Speaker'': Agatha Lenartowicz===<br /> Hands on practice with EEG data, in Matlab using EEGLAB. Lab materials distributed by email 11/27/16.<br /> <br /> ==''Wednesady 11/30/16''==<br /> ===Data Processing ''Speaker'': Agatha Lenartowicz===<br /> <br /> Hands on practice with EEG data, in Matlab using EEGLAB.<br /> <br /> =Week 11 - Finals Week=<br /> Final will be distributed 11/2/16.<br /> <br /> ==''Monday 11/5/16''==<br /> ===Final Exam===<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!--<br /> SPARE CONTENT / PREVIOUS YEARS<br /> <br /> =Week 3: Stats begins!=<br /> <br /> ==''Monday 10/12/15''==<br /> <br /> ===- Statistical Fundamentals. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> :*[[media: CohenClassIntroStats11_25_13.pdf‎ | Slides used in class (set 1)]]<br /> <br /> ''Review of'':<br /> :*Descriptive Statistics: mean, mode, variance, standard deviation<br /> :*Statistical Inference. The Binomial and Normal Distribution<br /> :*Basic Tests: t-test, linear correlation<br /> :*Modeling and non-linear relations<br /> :*Bayes rule<br /> <br /> <br /> ==''Wednesday 10/14/15''==<br /> ===- Statistical Fundamentals II. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> =Week 4 - Stats and circuits=<br /> ==''Monday 10/19/15''==<br /> ===- Statistical Fundamentals III. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> ==''Wednesday 10/21/15''==<br /> ===Circuits continued: Active devices. [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ===Optics I. ''Speaker'': [mailto:zdeis@seas.ucla.edu Zachary Taylor]===<br /> [[Image:Reflection.jpg|right]]<br /> The overall goal of this lecture is to establish that:<br /> ''- Physical constants have tangible meanings''<br /> ''- Plane waves form a physically unrealizable but extremely good approximation to real systems''<br /> ''- Boundaries bend light''<br /> ''- Physical constants, plane wave mechanics, and boundaries can be used to describe the operation of a lens''<br /> ''- The PSF gives a good indication of the overall performance of an imaging system''<br /> ''- All of these concepts have analogues in other areas of engineering (ie circuits, mechanical vibrations, etc.)''''<br /> <br /> '''Outline:'''<br /> :* Constitutive parameters (ε, μ, η, n, etc.)<br /> :* Plane wave basics<br /> :* Plane waves at boundaries<br /> :* Lenses<br /> :* Advanced imaging properties of lenses<br /> :* Point spread function.<br /> <br /> ''Required Readings''<br /> Zach has very kindly agreed to post his [http://www.brainmapping.org/NITP/PNA/Readings/OpticsTaylor3-10-10.pdf Optics lecture notes].<br /> ''Suggested Further Reading''<br /> <br /> <br /> <br /> <br /> ==''Wednesday 11/19/14''== <br /> ===Optogenetics. ''Speaker'': [http://faculty.neuroscience.ucla.edu/institution/personnel?personnel_id=47031 Tom Otis]===<br /> <br /> <br /> =Week 8: =<br /> ==''Monday 12/1/14 ''==<br /> ===- Statistics for Imaging. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> [[image:BVTradeoff.jpg|right]]<br /> ''Required Readings''<br /> :*[[media: Mumford_stat_modeling.pdf | Statistical Modeling and Inference (pdf)]]<br /> :*[[media: CohenClassSlides12_2_13.pdf | Slides used in class 12/2/14 and 12/4/14]]<br /> <br /> :*The General Linear Model<br /> *Linear Algebra applied to Statistical Solutions<br /> :*Analysis of Variance<br /> <br /> ==''Wednesday 11/20/14 ...''==<br /> === - Statistics for Imaging II. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> *Fixed and Random Effects<br /> *Repeated measures<br /> :*Bonferroni and Other Corrections<br /> *Non-Parametric Methods<br /> *Autocorrelation<br /> *Unknown Distributions<br /> <br /> ''Required Readings''<br /> :*[[media: CohenClassSlides12_2_13.pdf‎ | same as 12/2/14]]<br /> <br /> ''Suggested Further Reading''<br /> <br /> :*[http://ccn.ucla.edu/media/PNI20121121.mp4 Lecture Video - To be Posted Shortly - Please Right Click and Save rather than Stream - Thank you Alan and Edward!]<br /> <br /> =Week 9: =<br /> ==''Monday 11/25/14''==<br /> === - TBD. [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> ==''Wednesday 11/27/14''==<br /> ===- The Organization of the Human Brain. ''Speaker'': [http://ccn.ucla.edu/bmcweb/bmc_bios/SusanBookheimer/ Susan Bookheimer]===<br /> <br /> We will discuss the general organization of the human brain, and the regional specialization of cortical areas. The emphasis will be on understanding principles of organization:<br /> *Phylogenetic Layering<br /> *Functional Specialization<br /> *Principles Divisions of the Brain<br /> *Brain Systems<br /> <br /> ''Required Readings''<br /> :*[http://da.biostr.washington.edu:80/cgi-bin/DA/PageMaster?atlas:NeuroSyllabus+ffpathIndex/Splash^Page^Syllabus+2 Neuroanatomy Programmed Learning]<br /> :*[[media:Cognitive_science_and_neuro_2012.pdf | Slides shown in Class UPDATED 2012/10/17]]<br /> ''Suggested Further Reading''<br /> <br /> :*[[media:PNIA_2012_PS1_Soln.pdf‎ | Solution to Problem Set 1]]<br /> ===- Human Electrophysiology ''Speakers'': [http://greenlab.npih.ucla.edu/ROSTER.html Agatha Lenartowicz], [http://dgsom.healthsciences.ucla.edu/institution/personnel?personnel_id=9140 John Stern]===<br /> <br /> ''Evoked Responses'' - Guest Lecturer: [http://greenlab.npih.ucla.edu/ROSTER.html Jonathan Wynn]<br /> *A look at real EEG data<br /> *Preprocessing:<br /> **filtering<br /> **artifact detection/removal<br /> *averaging<br /> *single events<br /> *interpretation<br /> <br /> ''Clinical EEG'' - Guest Lecturer: [http://dgsom.healthsciences.ucla.edu/institution/personnel?personnel_id=9140 John Stern]<br /> *Normal and Abnormal EEG<br /> *EEG as a marker for brain state<br /> **sleep staging<br /> **alpha and relaxation<br /> *Neurofeedback??? --&gt;</div>

Alenartowicz https://www.ccn.ucla.edu/wiki/index.php?title=Principles_of_Neuroimaging_A_-_2016&diff=3226 2016-11-29T04:03:22Z

<p>Alenartowicz: /* Midterm Review, Data Processing Speaker: Cameron Rodriguez/Agatha Lenartowicz */</p> <hr /> <div>Principles of Neuroimaging A, Fall, 2016 - Class Schedule and Syllabus<br /> <br /> <br /> ==='''Neuroimaging journal Club (''required for NITP certificate'')'''===<br /> Contact: Katherine Lawrence (katherine.E.Lawrence@ucla.edu) or Janelle Liu (janelle.j.liu@ucla.edu), Faculty Sponsor: Jamie Feusner (JFeusner@mednet.ucla.edu)<br /> <br /> ==This schedule ''will'' change!==<br /> <br /> :'''[[Principles_of_Neuroimaging_-_2016-2017 | Back to main course page for Principles of Neuroimaging]]'''<br /> <br /> :'''Coming up next!: [[Principles_of_Neuroimaging_B_-_2017 | M284B Principles of Neuroimaging B]]'''<br /> <br /> =Course Reading=<br /> ===Required Reading===<br /> :'''Signal Processing for Neuroscientists''' by ''Wim van Drongelen''<br /> ::This can be found as a PDF on scribd.com, for a small fee of $8.99<br /> <br /> ===Supplemental Reading===<br /> :'''Matlab for Neuroscientists'''<br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/88212458/Matlab-Matlab-for-Neuroscientists<br /> <br /> :'''Cartoon Guide to Statistics''' <br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/148072668/Cartoon-Guide-to-Statistics<br /> <br /> :'''NOTE:''' if you subscribe for a Scribd account for a day, you can download as many documents as you like for one fee.<br /> <br /> <br /> =Week 1: Orientation to Neuro-imaging, Neurons, Brains=<br /> <br /> ==''Monday 9/26/16'' ==<br /> ===- Images. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284AImages-intro.pdf | Lecture Slides Images]]<br /> <br /> ==''Wednesday 9/28/16''==<br /> ===- Neurons &amp; Signaling. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> In this first class we will review the basics of neurophysiology with an eye towards what signals of brain function might be visible to the neuroimager. We will discuss information coding, energetics, size and time scales.<br /> [[Image:Neurons.jpg|right]]<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:TheActiveBrain.pdf | The Active Brain]]<br /> :*[[media:NeuronFunctionM284-2016_SM.pdf | Neuron function slides shown in class]]<br /> :*[http://www.science.smith.edu/departments/neurosci/courses/bio330/squid.html Videos of giant squid experiments]<br /> ''Suggested Further Reading''<br /> :*[http://ccn.ucla.edu/wiki/images/5/5a/CAVEAT_LECTOR.pdf Caveat Lector - the misuse of neuroimaging]<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Protected/Kosslyn1999.pdf &quot;If Neuroimaging is the Answer, What is the Question?&quot; Kosslyn, 1999]<br /> :*[http://da.biostr.washington.edu:80/cgi-bin/DA/PageMaster?atlas:NeuroSyllabus+ffpathIndex/Splash^Page^Syllabus+2 Neuroanatomy Programmed Learning]<br /> :*[http://www.amazon.com/Fundamental-Neuroscience-Second-Larry-Squire/dp/0126603030 Squire, Fundamentals of Neuroscience]<br /> :*[http://www.amazon.com/Principles-Neural-Science-Eric-Kandel/dp/0838577016 Kandel, et al., &quot;Principles of Neural Science&quot;]<br /> :This paper, by Malhi, is a nice orientation in methods of neuroimaging. *[http://www.ccn.ucla.edu/wiki/images/f/f2/Malhi2007.pdf Making sense of neuroimaging in psychiatry]<br /> :*[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1359308/pdf/jphysiol01232-0142.pdf Replacement of the axoplasm of giant nerve fibres with artificial solutions]<br /> <br /> =Week 2: Linear Systems, Convolution, Fourier Transforms=<br /> ==''Wednesday 10/3/16''==<br /> <br /> ===- Linear Systems I. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ==''Wednesday 10/5/16''==<br /> ===- Linear Systems II. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> Why the emphasis on Linear Systems? Because they are actually ''easy'' (as compared to non-linear systems, which are not.) As we go through this course, we will see many ways in which linear systems theory is applied to:<br /> :Modeling of Neural Systems<br /> :Extraction of Signal from Noise<br /> :Design of Circuits<br /> :Image Enhancement<br /> :Understanding of Image artifacts, and others.<br /> <br /> Linear systems analysis is one of the great technologies of the 20th and 21st century. It is now the basis for virtually all electronics design, and its extension into the discrete (digital) domain is the basis for most of modern signal processing. <br /> <br /> In our specific case, we will use these few basic principles of linear systems to understand both the instruments we use and the neuroimaging signals we collect. When you have mastered this material, you should be in a much better position to model the systems that you study in order to develop an approach to studying them.<br /> <br /> Here is [http://www.brainmapping.org/NITP/PNA/Readings/ImaginaryNumbers.pdf A primer I wrote on imaginary numbers] that might be a helpful review.<br /> <br /> There is a nice [http://en.wikibooks.org/wiki/Calculus Wikibook on Calculus].<br /> <br /> ''Required Readings''<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapter 1<br /> :*[[media:MathematicalTools2016.pdf | Mathematical Tools]]<br /> <br /> ''Suggested Further Reading''<br /> :'''Introduction to matlab'''<br /> <br /> ''Slides shown in class''<br /> :[[media:LinearityM284A-2016.pdf | Linearity and the Fourier Transform]]<br /> <br /> Please see [http://www.brainmapping.org/NITP/PNA/html/Linearity.html MATLAB linearity demo]<br /> <br /> If you are the type who sees beauty in mathematics, the Euler identity may be one of the most beautiful pieces of math in the world.<br /> <br /> =Week 3: Math &amp; Circuits I=<br /> <br /> ==''Monday 10/10/16''==<br /> === - Circuits I. Speaker: Cameron Rodriguez ===<br /> :*[[media:Circuits_2016.pdf | Circuit slides shown in class]]<br /> <br /> ==''Wednesday 10/12/16''==<br /> === - Circuits II. ''Speaker'': Cameron Rodriguez ===<br /> <br /> Why circuits?<br /> :(Virtually) Every device you use in your research is electronic. You access your primary data only indirectly<br /> :The device you ''really'' want in your lab doesn't exist. You very well may have to make it.<br /> :There are electronic analogs to most of the linear systems that you have so far studied (and ''vice versa'' - the tools you now understand can be used to analyze and predict circuit behavior).<br /> <br /> :If you have not had any of this background, you might want to have a look at this handout, [[Media:Electricity.pdf|Electrical Circuits]], in advance. There are near infinite numbers of resources on the web that cover similar material (near enough to infinite that by the time you read all of them, there would be a whole new set.) I have recently come across a link to [http://www.allaboutcircuits.com/ Online Books: All About Circuits] ''IF'' you want practical hands-on knowledge about this material, my all-time favorite text is [http://www.google.com/products/catalog?hl=en&amp;client=safari&amp;rls=en-us&amp;ei=uVSPSfaxE5nMsAPf-tmSCQ&amp;resnum=1&amp;q=art+of+electronics&amp;um=1&amp;ie=UTF-8&amp;cid=8820839049329255765#ps-sellers &quot;Horowitz and Hill: ''The Art of Electronics.''&quot;] The latest edition, however, is dated 1989 and a new third edition is promised. I have therefore stopped short of recommending a purchase unless your need to make circuits is immediate. In this book, you will find an excellent education on the fundamental principles of electrical circuits and an incredible compendium of practical data, such as how to assemble circuit boards, how to make measurements, etc...) <br /> <br /> Readings:<br /> :*[http://www.ti.com/lit/an/sloa093/sloa093.pdf Filter Design in 30 Seconds]<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapter 2 and 10<br /> :*[https://www.circuitlab.com/ Circuit Lab ] A Free Circuit Web Base Simulator<br /> **You may or may not find this comprehensible without chapters 5 through 9.<br /> <br /> We will discuss:<br /> :*Passive Circuit Elements: Resistors, Capacitors, Inductors<br /> :*Gain<br /> :*Transformers<br /> :*Rectifiers<br /> :*Active Elements<br /> ::- ''Amplifiers''<br /> ::- ''Transistors''<br /> ::- ''Op Amps''<br /> :*Solutions with Matrices<br /> ''Suggested Further Reading''<br /> :*[http://video.google.com/videoplay?docid=5645396659673218353&amp;q=Physics+for+Future+Presidents+Electricity&amp;total=5&amp;start=0&amp;num=10&amp;so=0&amp;type=search&amp;plindex=0#0h20m30s Video intro lecture on charge, current and voltage].<br /> :*[[media:Electricity_Basics.pdf‎ | Well organized text on electrical concepts by Tony R. Kuphaldt]]<br /> :*[http://en.wikibooks.org/wiki/Circuit_Theory Circuit Theory - Wikibook]<br /> <br /> '''Suggested, Optional Readings from [http://www.dspguide.com DSPguide.com]:'''<br /> :*[http://www.dspguide.com/CH5.PDF Linear Systems]<br /> :*[http://www.dspguide.com/CH6.PDF Convolution]<br /> :*[http://www.dspguide.com/CH8.PDF Discrete Fourier Transform (DFT)]<br /> :''Note: These chapters are light on math and try to focus on a conceptual understanding''<br /> <br /> Time and Frequency / Spectral Filters<br /> <br /> ----<br /> Practice using the Fourier transform:<br /> :[http://www.brainmapping.org/NITP/PNA/ConvFThtml/ConvolutionWorksheet.pdf Fourier transform and Convolution Worksheet]. &lt;!-- [http://www.brainmapping.org/NITP/PNA/ConvFThtml/ConvFT.html (''Solutions'').] --&gt;<br /> :[http://www.brainmapping.org/NITP/PNA/ConvFThtml/Something.wav Sound file for worksheet above.]<br /> <br /> Other<br /> :*[http://www.youtube.com/watch?feature=player_embedded&amp;v=7uHfjpU3OH0#! Getting Started with Arduino]<br /> <br /> =Week 4 Signal Processing=<br /> <br /> ==''Monday 10/17/16''==<br /> === - Signal Processing. ''Speaker'': Cameron Rodriguez ===<br /> <br /> :*[[media:Signals1-PreProcessing1_2016.pdf | Signal Pre Processing Slide Set 1 shown in class]]<br /> <br /> <br /> ==''Wednesday 10/19/16''==<br /> === - Noise ''Speaker'': Cameron Rodriguez ===<br /> <br /> :*[[media:OneD_signal_Filter_Ex.pdf | Matlab Signal Tutorial printout]]<br /> <br /> It is what you ''don't'' want - usually - but things change in quantized systems<br /> :Additive noise<br /> :White Noise<br /> :Boltzmann noise<br /> :Colored Noise<br /> :Gaussian Noise<br /> :Coherent noise<br /> :Sampling Errors<br /> :Aliasing<br /> :Quantization noise<br /> :Spectral filtering<br /> <br /> Noise comes in all shapes and colors. It is present in every measurement we make, from an EEG voltage to an estimate of the effects of dopamine on forebrain signal. Our best weapons are an understanding of the statistical properties of noise, the sources of noise and the ways to control it. Noise in the discrete digital domain is special, as it is both ''created'' by digitization and amplified by sampling.<br /> <br /> Readings:<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapters 2 through 4<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Noise.pdf Noise Slides]<br /> <br /> =Week 5 - Information &amp; Statistical Theory =<br /> <br /> ==''Monday 10/24/16 - in C8-177''==<br /> === Noise Cont' &amp; Information Theory Speakers: Cameron Rodriguez &amp; John Villasenor===<br /> <br /> ==''Wednesday 10/26/16''==<br /> ===Statistical Theory ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> We will consider the general problems of statistical inference, with a concentration on developing an intuitive understanding of statistical concepts.<br /> [[Image:MeasureForMeasure.jpg|right]]<br /> <br /> Suggested reading<br /> :*[http://www.statsoft.com/textbook/stbasic.html Statsoft online text (''free'')]<br /> :*[http://www.amazon.com/Cartoon-Guide-Statistics-Larry-Gonick/dp/0062731025 The Cartoon Guide to Statistics - Gonick $17.95 new]<br /> :The latter teaches stats at what I feel to be the right level - developing intuitions about the kinds of questions that can be answered using stats and about the statistical tests and measures<br /> <br /> :'''Problem Set 3 - Statistics in matlab'''<br /> ::[[media: Problem_Set_1.doc|Problem set using stats and MATLAB]]<br /> ::[[media: Problem_Set_1B.doc|More practice with stats and MATLAB]]<br /> <br /> <br /> =MIDTERM=<br /> <br /> <br /> =Week 6 - Neurophysiology=<br /> ==''Monday 10/31/16''==<br /> === Electrophysiology I ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> We will examine our first imaging modality, EEG (and MEG).<br /> <br /> ::[[media: EEGNeurophys_161102.pdf|Slides for Neurophysiology Lectures]]<br /> <br /> ==''Wednesday 11/2/16''==<br /> === Electrophysiology II ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> Continuing with neurophysiology. ERPs &amp; Source Localization.<br /> <br /> =Week 7 - Neurophysiology =<br /> ==''Monday 11/7/16''==<br /> === Electrophysiology III ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> Continuing with neurophysiology. Connectivity &amp; Oscillations.<br /> <br /> ==''Wednesday 11/9/16''==<br /> ===Intracranial Recordings ''Speaker'': [http://neurosurgery.ucla.edu/neuromodulation-neuroimaging-lab Nanthia Suthana]===<br /> <br /> ::[[media: Suthana.pdf|Slides for Intracranial Lecture]]<br /> ::[[media: LachauxIntracranial.pdf|Lachaux et al Reading]]<br /> ::[[media: HarrisIntracranial.pdf|Harris et al Reading]]<br /> <br /> =Week 8 - Practical Neurophysiology=<br /> ==''Monday 11/14/16''==<br /> ===Building an EEG System - Circuit 1 - Going over circuit ''Speaker'': Cameron Rodriguez===<br /> <br /> :*[https://learn.sparkfun.com/tutorials/how-to-solder Through holesoldering].<br /> :*[https://learn.sparkfun.com/tutorials/pcb-basics Printed Circuit Board Basics].<br /> :*[https://learn.sparkfun.com/tutorials/how-to-install-and-setup-eagle Installing and Setting Up Eagle].<br /> :*[https://learn.sparkfun.com/tutorials/using-eagle-schematic Using Eagle Schematic].<br /> :*[https://learn.sparkfun.com/tutorials/using-eagle-board-layoutc Using Eagle Board Layout].<br /> <br /> :*[https://www.sparkfun.com/videos#pete/NJKZZArjdg8 PCB Layout 1 - According to Pete];<br /> :*[https://www.sparkfun.com/videos#pete/JANZsjRiM3w PCB Layout 2 - According to Pete];<br /> <br /> ==''Wednesday 11/16/16''==<br /> ===Data Processing Hands On ''Speaker'': Cameron Rodriguez===<br /> <br /> =Week 9 - Data Acquisition =<br /> ==''Monday 11/21/16 - in C8-177''==<br /> ===Data Acquisition ''Speaker'': Agatha Lenartowicz===<br /> Please meet in CCN suite.<br /> <br /> ==''Wednesday 11/23/16''==<br /> ===Thankgiving Wed - No Class!! ===<br /> Happy Thanksgiving!<br /> <br /> =Week 10 - Data Processing =<br /> <br /> *Please complete class evaluations via myucla. <br /> *Also we have an inhouse feedback form regarding the course syllabus: [https://docs.google.com/forms/d/e/1FAIpQLSd8xn340PxZRWb3-GQ__ZisYwFWoSgmYrcwyjvyBY5JV_mxnQ/viewform Feedback Link]<br /> <br /> Your feedback is invaluable to us!!<br /> <br /> ==''Monday 11/28/16''==<br /> ===Midterm Review, Data Processing ''Speaker'': Agatha Lenartowicz===<br /> Hands on practice with EEG data, in Matlab using EEGLAB. Lab materials distributed by email 11/27/16.<br /> <br /> ==''Wednesady 11/30/16''==<br /> ===Data Processing ''Speaker'': Cameron Rodriguez/Agatha Lenartowicz===<br /> <br /> Hands on practice with EEG data, in Matlab using EEGLAB.<br /> <br /> =Week 11 - Finals Week=<br /> Final will be distributed 11/2/16.<br /> <br /> ==''Monday 11/5/16''==<br /> ===Final Exam===<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!--<br /> SPARE CONTENT / PREVIOUS YEARS<br /> <br /> =Week 3: Stats begins!=<br /> <br /> ==''Monday 10/12/15''==<br /> <br /> ===- Statistical Fundamentals. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> :*[[media: CohenClassIntroStats11_25_13.pdf‎ | Slides used in class (set 1)]]<br /> <br /> ''Review of'':<br /> :*Descriptive Statistics: mean, mode, variance, standard deviation<br /> :*Statistical Inference. The Binomial and Normal Distribution<br /> :*Basic Tests: t-test, linear correlation<br /> :*Modeling and non-linear relations<br /> :*Bayes rule<br /> <br /> <br /> ==''Wednesday 10/14/15''==<br /> ===- Statistical Fundamentals II. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> =Week 4 - Stats and circuits=<br /> ==''Monday 10/19/15''==<br /> ===- Statistical Fundamentals III. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> ==''Wednesday 10/21/15''==<br /> ===Circuits continued: Active devices. [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ===Optics I. ''Speaker'': [mailto:zdeis@seas.ucla.edu Zachary Taylor]===<br /> [[Image:Reflection.jpg|right]]<br /> The overall goal of this lecture is to establish that:<br /> ''- Physical constants have tangible meanings''<br /> ''- Plane waves form a physically unrealizable but extremely good approximation to real systems''<br /> ''- Boundaries bend light''<br /> ''- Physical constants, plane wave mechanics, and boundaries can be used to describe the operation of a lens''<br /> ''- The PSF gives a good indication of the overall performance of an imaging system''<br /> ''- All of these concepts have analogues in other areas of engineering (ie circuits, mechanical vibrations, etc.)''''<br /> <br /> '''Outline:'''<br /> :* Constitutive parameters (ε, μ, η, n, etc.)<br /> :* Plane wave basics<br /> :* Plane waves at boundaries<br /> :* Lenses<br /> :* Advanced imaging properties of lenses<br /> :* Point spread function.<br /> <br /> ''Required Readings''<br /> Zach has very kindly agreed to post his [http://www.brainmapping.org/NITP/PNA/Readings/OpticsTaylor3-10-10.pdf Optics lecture notes].<br /> ''Suggested Further Reading''<br /> <br /> <br /> <br /> <br /> ==''Wednesday 11/19/14''== <br /> ===Optogenetics. ''Speaker'': [http://faculty.neuroscience.ucla.edu/institution/personnel?personnel_id=47031 Tom Otis]===<br /> <br /> <br /> =Week 8: =<br /> ==''Monday 12/1/14 ''==<br /> ===- Statistics for Imaging. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> [[image:BVTradeoff.jpg|right]]<br /> ''Required Readings''<br /> :*[[media: Mumford_stat_modeling.pdf | Statistical Modeling and Inference (pdf)]]<br /> :*[[media: CohenClassSlides12_2_13.pdf | Slides used in class 12/2/14 and 12/4/14]]<br /> <br /> :*The General Linear Model<br /> *Linear Algebra applied to Statistical Solutions<br /> :*Analysis of Variance<br /> <br /> ==''Wednesday 11/20/14 ...''==<br /> === - Statistics for Imaging II. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> *Fixed and Random Effects<br /> *Repeated measures<br /> :*Bonferroni and Other Corrections<br /> *Non-Parametric Methods<br /> *Autocorrelation<br /> *Unknown Distributions<br /> <br /> ''Required Readings''<br /> :*[[media: CohenClassSlides12_2_13.pdf‎ | same as 12/2/14]]<br /> <br /> ''Suggested Further Reading''<br /> <br /> :*[http://ccn.ucla.edu/media/PNI20121121.mp4 Lecture Video - To be Posted Shortly - Please Right Click and Save rather than Stream - Thank you Alan and Edward!]<br /> <br /> =Week 9: =<br /> ==''Monday 11/25/14''==<br /> === - TBD. [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> ==''Wednesday 11/27/14''==<br /> ===- The Organization of the Human Brain. ''Speaker'': [http://ccn.ucla.edu/bmcweb/bmc_bios/SusanBookheimer/ Susan Bookheimer]===<br /> <br /> We will discuss the general organization of the human brain, and the regional specialization of cortical areas. The emphasis will be on understanding principles of organization:<br /> *Phylogenetic Layering<br /> *Functional Specialization<br /> *Principles Divisions of the Brain<br /> *Brain Systems<br /> <br /> ''Required Readings''<br /> :*[http://da.biostr.washington.edu:80/cgi-bin/DA/PageMaster?atlas:NeuroSyllabus+ffpathIndex/Splash^Page^Syllabus+2 Neuroanatomy Programmed Learning]<br /> :*[[media:Cognitive_science_and_neuro_2012.pdf | Slides shown in Class UPDATED 2012/10/17]]<br /> ''Suggested Further Reading''<br /> <br /> :*[[media:PNIA_2012_PS1_Soln.pdf‎ | Solution to Problem Set 1]]<br /> ===- Human Electrophysiology ''Speakers'': [http://greenlab.npih.ucla.edu/ROSTER.html Agatha Lenartowicz], [http://dgsom.healthsciences.ucla.edu/institution/personnel?personnel_id=9140 John Stern]===<br /> <br /> ''Evoked Responses'' - Guest Lecturer: [http://greenlab.npih.ucla.edu/ROSTER.html Jonathan Wynn]<br /> *A look at real EEG data<br /> *Preprocessing:<br /> **filtering<br /> **artifact detection/removal<br /> *averaging<br /> *single events<br /> *interpretation<br /> <br /> ''Clinical EEG'' - Guest Lecturer: [http://dgsom.healthsciences.ucla.edu/institution/personnel?personnel_id=9140 John Stern]<br /> *Normal and Abnormal EEG<br /> *EEG as a marker for brain state<br /> **sleep staging<br /> **alpha and relaxation<br /> *Neurofeedback??? --&gt;</div>

Alenartowicz https://www.ccn.ucla.edu/wiki/index.php?title=Principles_of_Neuroimaging_A_-_2016&diff=3225 2016-11-29T04:03:03Z

<p>Alenartowicz: /* Data Processing &quot;Speaker&quot;: Cameron Rodriguez/Agatha Lenartowicz */</p> <hr /> <div>Principles of Neuroimaging A, Fall, 2016 - Class Schedule and Syllabus<br /> <br /> <br /> ==='''Neuroimaging journal Club (''required for NITP certificate'')'''===<br /> Contact: Katherine Lawrence (katherine.E.Lawrence@ucla.edu) or Janelle Liu (janelle.j.liu@ucla.edu), Faculty Sponsor: Jamie Feusner (JFeusner@mednet.ucla.edu)<br /> <br /> ==This schedule ''will'' change!==<br /> <br /> :'''[[Principles_of_Neuroimaging_-_2016-2017 | Back to main course page for Principles of Neuroimaging]]'''<br /> <br /> :'''Coming up next!: [[Principles_of_Neuroimaging_B_-_2017 | M284B Principles of Neuroimaging B]]'''<br /> <br /> =Course Reading=<br /> ===Required Reading===<br /> :'''Signal Processing for Neuroscientists''' by ''Wim van Drongelen''<br /> ::This can be found as a PDF on scribd.com, for a small fee of $8.99<br /> <br /> ===Supplemental Reading===<br /> :'''Matlab for Neuroscientists'''<br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/88212458/Matlab-Matlab-for-Neuroscientists<br /> <br /> :'''Cartoon Guide to Statistics''' <br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/148072668/Cartoon-Guide-to-Statistics<br /> <br /> :'''NOTE:''' if you subscribe for a Scribd account for a day, you can download as many documents as you like for one fee.<br /> <br /> <br /> =Week 1: Orientation to Neuro-imaging, Neurons, Brains=<br /> <br /> ==''Monday 9/26/16'' ==<br /> ===- Images. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284AImages-intro.pdf | Lecture Slides Images]]<br /> <br /> ==''Wednesday 9/28/16''==<br /> ===- Neurons &amp; Signaling. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> In this first class we will review the basics of neurophysiology with an eye towards what signals of brain function might be visible to the neuroimager. We will discuss information coding, energetics, size and time scales.<br /> [[Image:Neurons.jpg|right]]<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:TheActiveBrain.pdf | The Active Brain]]<br /> :*[[media:NeuronFunctionM284-2016_SM.pdf | Neuron function slides shown in class]]<br /> :*[http://www.science.smith.edu/departments/neurosci/courses/bio330/squid.html Videos of giant squid experiments]<br /> ''Suggested Further Reading''<br /> :*[http://ccn.ucla.edu/wiki/images/5/5a/CAVEAT_LECTOR.pdf Caveat Lector - the misuse of neuroimaging]<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Protected/Kosslyn1999.pdf &quot;If Neuroimaging is the Answer, What is the Question?&quot; Kosslyn, 1999]<br /> :*[http://da.biostr.washington.edu:80/cgi-bin/DA/PageMaster?atlas:NeuroSyllabus+ffpathIndex/Splash^Page^Syllabus+2 Neuroanatomy Programmed Learning]<br /> :*[http://www.amazon.com/Fundamental-Neuroscience-Second-Larry-Squire/dp/0126603030 Squire, Fundamentals of Neuroscience]<br /> :*[http://www.amazon.com/Principles-Neural-Science-Eric-Kandel/dp/0838577016 Kandel, et al., &quot;Principles of Neural Science&quot;]<br /> :This paper, by Malhi, is a nice orientation in methods of neuroimaging. *[http://www.ccn.ucla.edu/wiki/images/f/f2/Malhi2007.pdf Making sense of neuroimaging in psychiatry]<br /> :*[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1359308/pdf/jphysiol01232-0142.pdf Replacement of the axoplasm of giant nerve fibres with artificial solutions]<br /> <br /> =Week 2: Linear Systems, Convolution, Fourier Transforms=<br /> ==''Wednesday 10/3/16''==<br /> <br /> ===- Linear Systems I. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ==''Wednesday 10/5/16''==<br /> ===- Linear Systems II. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> Why the emphasis on Linear Systems? Because they are actually ''easy'' (as compared to non-linear systems, which are not.) As we go through this course, we will see many ways in which linear systems theory is applied to:<br /> :Modeling of Neural Systems<br /> :Extraction of Signal from Noise<br /> :Design of Circuits<br /> :Image Enhancement<br /> :Understanding of Image artifacts, and others.<br /> <br /> Linear systems analysis is one of the great technologies of the 20th and 21st century. It is now the basis for virtually all electronics design, and its extension into the discrete (digital) domain is the basis for most of modern signal processing. <br /> <br /> In our specific case, we will use these few basic principles of linear systems to understand both the instruments we use and the neuroimaging signals we collect. When you have mastered this material, you should be in a much better position to model the systems that you study in order to develop an approach to studying them.<br /> <br /> Here is [http://www.brainmapping.org/NITP/PNA/Readings/ImaginaryNumbers.pdf A primer I wrote on imaginary numbers] that might be a helpful review.<br /> <br /> There is a nice [http://en.wikibooks.org/wiki/Calculus Wikibook on Calculus].<br /> <br /> ''Required Readings''<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapter 1<br /> :*[[media:MathematicalTools2016.pdf | Mathematical Tools]]<br /> <br /> ''Suggested Further Reading''<br /> :'''Introduction to matlab'''<br /> <br /> ''Slides shown in class''<br /> :[[media:LinearityM284A-2016.pdf | Linearity and the Fourier Transform]]<br /> <br /> Please see [http://www.brainmapping.org/NITP/PNA/html/Linearity.html MATLAB linearity demo]<br /> <br /> If you are the type who sees beauty in mathematics, the Euler identity may be one of the most beautiful pieces of math in the world.<br /> <br /> =Week 3: Math &amp; Circuits I=<br /> <br /> ==''Monday 10/10/16''==<br /> === - Circuits I. Speaker: Cameron Rodriguez ===<br /> :*[[media:Circuits_2016.pdf | Circuit slides shown in class]]<br /> <br /> ==''Wednesday 10/12/16''==<br /> === - Circuits II. ''Speaker'': Cameron Rodriguez ===<br /> <br /> Why circuits?<br /> :(Virtually) Every device you use in your research is electronic. You access your primary data only indirectly<br /> :The device you ''really'' want in your lab doesn't exist. You very well may have to make it.<br /> :There are electronic analogs to most of the linear systems that you have so far studied (and ''vice versa'' - the tools you now understand can be used to analyze and predict circuit behavior).<br /> <br /> :If you have not had any of this background, you might want to have a look at this handout, [[Media:Electricity.pdf|Electrical Circuits]], in advance. There are near infinite numbers of resources on the web that cover similar material (near enough to infinite that by the time you read all of them, there would be a whole new set.) I have recently come across a link to [http://www.allaboutcircuits.com/ Online Books: All About Circuits] ''IF'' you want practical hands-on knowledge about this material, my all-time favorite text is [http://www.google.com/products/catalog?hl=en&amp;client=safari&amp;rls=en-us&amp;ei=uVSPSfaxE5nMsAPf-tmSCQ&amp;resnum=1&amp;q=art+of+electronics&amp;um=1&amp;ie=UTF-8&amp;cid=8820839049329255765#ps-sellers &quot;Horowitz and Hill: ''The Art of Electronics.''&quot;] The latest edition, however, is dated 1989 and a new third edition is promised. I have therefore stopped short of recommending a purchase unless your need to make circuits is immediate. In this book, you will find an excellent education on the fundamental principles of electrical circuits and an incredible compendium of practical data, such as how to assemble circuit boards, how to make measurements, etc...) <br /> <br /> Readings:<br /> :*[http://www.ti.com/lit/an/sloa093/sloa093.pdf Filter Design in 30 Seconds]<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapter 2 and 10<br /> :*[https://www.circuitlab.com/ Circuit Lab ] A Free Circuit Web Base Simulator<br /> **You may or may not find this comprehensible without chapters 5 through 9.<br /> <br /> We will discuss:<br /> :*Passive Circuit Elements: Resistors, Capacitors, Inductors<br /> :*Gain<br /> :*Transformers<br /> :*Rectifiers<br /> :*Active Elements<br /> ::- ''Amplifiers''<br /> ::- ''Transistors''<br /> ::- ''Op Amps''<br /> :*Solutions with Matrices<br /> ''Suggested Further Reading''<br /> :*[http://video.google.com/videoplay?docid=5645396659673218353&amp;q=Physics+for+Future+Presidents+Electricity&amp;total=5&amp;start=0&amp;num=10&amp;so=0&amp;type=search&amp;plindex=0#0h20m30s Video intro lecture on charge, current and voltage].<br /> :*[[media:Electricity_Basics.pdf‎ | Well organized text on electrical concepts by Tony R. Kuphaldt]]<br /> :*[http://en.wikibooks.org/wiki/Circuit_Theory Circuit Theory - Wikibook]<br /> <br /> '''Suggested, Optional Readings from [http://www.dspguide.com DSPguide.com]:'''<br /> :*[http://www.dspguide.com/CH5.PDF Linear Systems]<br /> :*[http://www.dspguide.com/CH6.PDF Convolution]<br /> :*[http://www.dspguide.com/CH8.PDF Discrete Fourier Transform (DFT)]<br /> :''Note: These chapters are light on math and try to focus on a conceptual understanding''<br /> <br /> Time and Frequency / Spectral Filters<br /> <br /> ----<br /> Practice using the Fourier transform:<br /> :[http://www.brainmapping.org/NITP/PNA/ConvFThtml/ConvolutionWorksheet.pdf Fourier transform and Convolution Worksheet]. &lt;!-- [http://www.brainmapping.org/NITP/PNA/ConvFThtml/ConvFT.html (''Solutions'').] --&gt;<br /> :[http://www.brainmapping.org/NITP/PNA/ConvFThtml/Something.wav Sound file for worksheet above.]<br /> <br /> Other<br /> :*[http://www.youtube.com/watch?feature=player_embedded&amp;v=7uHfjpU3OH0#! Getting Started with Arduino]<br /> <br /> =Week 4 Signal Processing=<br /> <br /> ==''Monday 10/17/16''==<br /> === - Signal Processing. ''Speaker'': Cameron Rodriguez ===<br /> <br /> :*[[media:Signals1-PreProcessing1_2016.pdf | Signal Pre Processing Slide Set 1 shown in class]]<br /> <br /> <br /> ==''Wednesday 10/19/16''==<br /> === - Noise ''Speaker'': Cameron Rodriguez ===<br /> <br /> :*[[media:OneD_signal_Filter_Ex.pdf | Matlab Signal Tutorial printout]]<br /> <br /> It is what you ''don't'' want - usually - but things change in quantized systems<br /> :Additive noise<br /> :White Noise<br /> :Boltzmann noise<br /> :Colored Noise<br /> :Gaussian Noise<br /> :Coherent noise<br /> :Sampling Errors<br /> :Aliasing<br /> :Quantization noise<br /> :Spectral filtering<br /> <br /> Noise comes in all shapes and colors. It is present in every measurement we make, from an EEG voltage to an estimate of the effects of dopamine on forebrain signal. Our best weapons are an understanding of the statistical properties of noise, the sources of noise and the ways to control it. Noise in the discrete digital domain is special, as it is both ''created'' by digitization and amplified by sampling.<br /> <br /> Readings:<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapters 2 through 4<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Noise.pdf Noise Slides]<br /> <br /> =Week 5 - Information &amp; Statistical Theory =<br /> <br /> ==''Monday 10/24/16 - in C8-177''==<br /> === Noise Cont' &amp; Information Theory Speakers: Cameron Rodriguez &amp; John Villasenor===<br /> <br /> ==''Wednesday 10/26/16''==<br /> ===Statistical Theory ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> We will consider the general problems of statistical inference, with a concentration on developing an intuitive understanding of statistical concepts.<br /> [[Image:MeasureForMeasure.jpg|right]]<br /> <br /> Suggested reading<br /> :*[http://www.statsoft.com/textbook/stbasic.html Statsoft online text (''free'')]<br /> :*[http://www.amazon.com/Cartoon-Guide-Statistics-Larry-Gonick/dp/0062731025 The Cartoon Guide to Statistics - Gonick $17.95 new]<br /> :The latter teaches stats at what I feel to be the right level - developing intuitions about the kinds of questions that can be answered using stats and about the statistical tests and measures<br /> <br /> :'''Problem Set 3 - Statistics in matlab'''<br /> ::[[media: Problem_Set_1.doc|Problem set using stats and MATLAB]]<br /> ::[[media: Problem_Set_1B.doc|More practice with stats and MATLAB]]<br /> <br /> <br /> =MIDTERM=<br /> <br /> <br /> =Week 6 - Neurophysiology=<br /> ==''Monday 10/31/16''==<br /> === Electrophysiology I ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> We will examine our first imaging modality, EEG (and MEG).<br /> <br /> ::[[media: EEGNeurophys_161102.pdf|Slides for Neurophysiology Lectures]]<br /> <br /> ==''Wednesday 11/2/16''==<br /> === Electrophysiology II ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> Continuing with neurophysiology. ERPs &amp; Source Localization.<br /> <br /> =Week 7 - Neurophysiology =<br /> ==''Monday 11/7/16''==<br /> === Electrophysiology III ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> Continuing with neurophysiology. Connectivity &amp; Oscillations.<br /> <br /> ==''Wednesday 11/9/16''==<br /> ===Intracranial Recordings ''Speaker'': [http://neurosurgery.ucla.edu/neuromodulation-neuroimaging-lab Nanthia Suthana]===<br /> <br /> ::[[media: Suthana.pdf|Slides for Intracranial Lecture]]<br /> ::[[media: LachauxIntracranial.pdf|Lachaux et al Reading]]<br /> ::[[media: HarrisIntracranial.pdf|Harris et al Reading]]<br /> <br /> =Week 8 - Practical Neurophysiology=<br /> ==''Monday 11/14/16''==<br /> ===Building an EEG System - Circuit 1 - Going over circuit ''Speaker'': Cameron Rodriguez===<br /> <br /> :*[https://learn.sparkfun.com/tutorials/how-to-solder Through holesoldering].<br /> :*[https://learn.sparkfun.com/tutorials/pcb-basics Printed Circuit Board Basics].<br /> :*[https://learn.sparkfun.com/tutorials/how-to-install-and-setup-eagle Installing and Setting Up Eagle].<br /> :*[https://learn.sparkfun.com/tutorials/using-eagle-schematic Using Eagle Schematic].<br /> :*[https://learn.sparkfun.com/tutorials/using-eagle-board-layoutc Using Eagle Board Layout].<br /> <br /> :*[https://www.sparkfun.com/videos#pete/NJKZZArjdg8 PCB Layout 1 - According to Pete];<br /> :*[https://www.sparkfun.com/videos#pete/JANZsjRiM3w PCB Layout 2 - According to Pete];<br /> <br /> ==''Wednesday 11/16/16''==<br /> ===Data Processing Hands On ''Speaker'': Cameron Rodriguez===<br /> <br /> =Week 9 - Data Acquisition =<br /> ==''Monday 11/21/16 - in C8-177''==<br /> ===Data Acquisition ''Speaker'': Agatha Lenartowicz===<br /> Please meet in CCN suite.<br /> <br /> ==''Wednesday 11/23/16''==<br /> ===Thankgiving Wed - No Class!! ===<br /> Happy Thanksgiving!<br /> <br /> =Week 10 - Data Processing =<br /> <br /> *Please complete class evaluations via myucla. <br /> *Also we have an inhouse feedback form regarding the course syllabus: [https://docs.google.com/forms/d/e/1FAIpQLSd8xn340PxZRWb3-GQ__ZisYwFWoSgmYrcwyjvyBY5JV_mxnQ/viewform Feedback Link]<br /> <br /> Your feedback is invaluable to us!!<br /> <br /> ==''Monday 11/28/16''==<br /> ===Midterm Review, Data Processing ''Speaker'': Cameron Rodriguez/Agatha Lenartowicz===<br /> Hands on practice with EEG data, in Matlab using EEGLAB. Lab materials distributed by email 11/27/16.<br /> <br /> ==''Wednesady 11/30/16''==<br /> ===Data Processing ''Speaker'': Cameron Rodriguez/Agatha Lenartowicz===<br /> <br /> Hands on practice with EEG data, in Matlab using EEGLAB.<br /> <br /> =Week 11 - Finals Week=<br /> Final will be distributed 11/2/16.<br /> <br /> ==''Monday 11/5/16''==<br /> ===Final Exam===<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!--<br /> SPARE CONTENT / PREVIOUS YEARS<br /> <br /> =Week 3: Stats begins!=<br /> <br /> ==''Monday 10/12/15''==<br /> <br /> ===- Statistical Fundamentals. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> :*[[media: CohenClassIntroStats11_25_13.pdf‎ | Slides used in class (set 1)]]<br /> <br /> ''Review of'':<br /> :*Descriptive Statistics: mean, mode, variance, standard deviation<br /> :*Statistical Inference. The Binomial and Normal Distribution<br /> :*Basic Tests: t-test, linear correlation<br /> :*Modeling and non-linear relations<br /> :*Bayes rule<br /> <br /> <br /> ==''Wednesday 10/14/15''==<br /> ===- Statistical Fundamentals II. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> =Week 4 - Stats and circuits=<br /> ==''Monday 10/19/15''==<br /> ===- Statistical Fundamentals III. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> ==''Wednesday 10/21/15''==<br /> ===Circuits continued: Active devices. [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ===Optics I. ''Speaker'': [mailto:zdeis@seas.ucla.edu Zachary Taylor]===<br /> [[Image:Reflection.jpg|right]]<br /> The overall goal of this lecture is to establish that:<br /> ''- Physical constants have tangible meanings''<br /> ''- Plane waves form a physically unrealizable but extremely good approximation to real systems''<br /> ''- Boundaries bend light''<br /> ''- Physical constants, plane wave mechanics, and boundaries can be used to describe the operation of a lens''<br /> ''- The PSF gives a good indication of the overall performance of an imaging system''<br /> ''- All of these concepts have analogues in other areas of engineering (ie circuits, mechanical vibrations, etc.)''''<br /> <br /> '''Outline:'''<br /> :* Constitutive parameters (ε, μ, η, n, etc.)<br /> :* Plane wave basics<br /> :* Plane waves at boundaries<br /> :* Lenses<br /> :* Advanced imaging properties of lenses<br /> :* Point spread function.<br /> <br /> ''Required Readings''<br /> Zach has very kindly agreed to post his [http://www.brainmapping.org/NITP/PNA/Readings/OpticsTaylor3-10-10.pdf Optics lecture notes].<br /> ''Suggested Further Reading''<br /> <br /> <br /> <br /> <br /> ==''Wednesday 11/19/14''== <br /> ===Optogenetics. ''Speaker'': [http://faculty.neuroscience.ucla.edu/institution/personnel?personnel_id=47031 Tom Otis]===<br /> <br /> <br /> =Week 8: =<br /> ==''Monday 12/1/14 ''==<br /> ===- Statistics for Imaging. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> [[image:BVTradeoff.jpg|right]]<br /> ''Required Readings''<br /> :*[[media: Mumford_stat_modeling.pdf | Statistical Modeling and Inference (pdf)]]<br /> :*[[media: CohenClassSlides12_2_13.pdf | Slides used in class 12/2/14 and 12/4/14]]<br /> <br /> :*The General Linear Model<br /> *Linear Algebra applied to Statistical Solutions<br /> :*Analysis of Variance<br /> <br /> ==''Wednesday 11/20/14 ...''==<br /> === - Statistics for Imaging II. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> *Fixed and Random Effects<br /> *Repeated measures<br /> :*Bonferroni and Other Corrections<br /> *Non-Parametric Methods<br /> *Autocorrelation<br /> *Unknown Distributions<br /> <br /> ''Required Readings''<br /> :*[[media: CohenClassSlides12_2_13.pdf‎ | same as 12/2/14]]<br /> <br /> ''Suggested Further Reading''<br /> <br /> :*[http://ccn.ucla.edu/media/PNI20121121.mp4 Lecture Video - To be Posted Shortly - Please Right Click and Save rather than Stream - Thank you Alan and Edward!]<br /> <br /> =Week 9: =<br /> ==''Monday 11/25/14''==<br /> === - TBD. [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> ==''Wednesday 11/27/14''==<br /> ===- The Organization of the Human Brain. ''Speaker'': [http://ccn.ucla.edu/bmcweb/bmc_bios/SusanBookheimer/ Susan Bookheimer]===<br /> <br /> We will discuss the general organization of the human brain, and the regional specialization of cortical areas. The emphasis will be on understanding principles of organization:<br /> *Phylogenetic Layering<br /> *Functional Specialization<br /> *Principles Divisions of the Brain<br /> *Brain Systems<br /> <br /> ''Required Readings''<br /> :*[http://da.biostr.washington.edu:80/cgi-bin/DA/PageMaster?atlas:NeuroSyllabus+ffpathIndex/Splash^Page^Syllabus+2 Neuroanatomy Programmed Learning]<br /> :*[[media:Cognitive_science_and_neuro_2012.pdf | Slides shown in Class UPDATED 2012/10/17]]<br /> ''Suggested Further Reading''<br /> <br /> :*[[media:PNIA_2012_PS1_Soln.pdf‎ | Solution to Problem Set 1]]<br /> ===- Human Electrophysiology ''Speakers'': [http://greenlab.npih.ucla.edu/ROSTER.html Agatha Lenartowicz], [http://dgsom.healthsciences.ucla.edu/institution/personnel?personnel_id=9140 John Stern]===<br /> <br /> ''Evoked Responses'' - Guest Lecturer: [http://greenlab.npih.ucla.edu/ROSTER.html Jonathan Wynn]<br /> *A look at real EEG data<br /> *Preprocessing:<br /> **filtering<br /> **artifact detection/removal<br /> *averaging<br /> *single events<br /> *interpretation<br /> <br /> ''Clinical EEG'' - Guest Lecturer: [http://dgsom.healthsciences.ucla.edu/institution/personnel?personnel_id=9140 John Stern]<br /> *Normal and Abnormal EEG<br /> *EEG as a marker for brain state<br /> **sleep staging<br /> **alpha and relaxation<br /> *Neurofeedback??? --&gt;</div>

Alenartowicz https://www.ccn.ucla.edu/wiki/index.php?title=Principles_of_Neuroimaging_A_-_2016&diff=3224 2016-11-29T04:01:54Z

<p>Alenartowicz: /* Thankgiving Wed - No Class */</p> <hr /> <div>Principles of Neuroimaging A, Fall, 2016 - Class Schedule and Syllabus<br /> <br /> <br /> ==='''Neuroimaging journal Club (''required for NITP certificate'')'''===<br /> Contact: Katherine Lawrence (katherine.E.Lawrence@ucla.edu) or Janelle Liu (janelle.j.liu@ucla.edu), Faculty Sponsor: Jamie Feusner (JFeusner@mednet.ucla.edu)<br /> <br /> ==This schedule ''will'' change!==<br /> <br /> :'''[[Principles_of_Neuroimaging_-_2016-2017 | Back to main course page for Principles of Neuroimaging]]'''<br /> <br /> :'''Coming up next!: [[Principles_of_Neuroimaging_B_-_2017 | M284B Principles of Neuroimaging B]]'''<br /> <br /> =Course Reading=<br /> ===Required Reading===<br /> :'''Signal Processing for Neuroscientists''' by ''Wim van Drongelen''<br /> ::This can be found as a PDF on scribd.com, for a small fee of $8.99<br /> <br /> ===Supplemental Reading===<br /> :'''Matlab for Neuroscientists'''<br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/88212458/Matlab-Matlab-for-Neuroscientists<br /> <br /> :'''Cartoon Guide to Statistics''' <br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/148072668/Cartoon-Guide-to-Statistics<br /> <br /> :'''NOTE:''' if you subscribe for a Scribd account for a day, you can download as many documents as you like for one fee.<br /> <br /> <br /> =Week 1: Orientation to Neuro-imaging, Neurons, Brains=<br /> <br /> ==''Monday 9/26/16'' ==<br /> ===- Images. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284AImages-intro.pdf | Lecture Slides Images]]<br /> <br /> ==''Wednesday 9/28/16''==<br /> ===- Neurons &amp; Signaling. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> In this first class we will review the basics of neurophysiology with an eye towards what signals of brain function might be visible to the neuroimager. We will discuss information coding, energetics, size and time scales.<br /> [[Image:Neurons.jpg|right]]<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:TheActiveBrain.pdf | The Active Brain]]<br /> :*[[media:NeuronFunctionM284-2016_SM.pdf | Neuron function slides shown in class]]<br /> :*[http://www.science.smith.edu/departments/neurosci/courses/bio330/squid.html Videos of giant squid experiments]<br /> ''Suggested Further Reading''<br /> :*[http://ccn.ucla.edu/wiki/images/5/5a/CAVEAT_LECTOR.pdf Caveat Lector - the misuse of neuroimaging]<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Protected/Kosslyn1999.pdf &quot;If Neuroimaging is the Answer, What is the Question?&quot; Kosslyn, 1999]<br /> :*[http://da.biostr.washington.edu:80/cgi-bin/DA/PageMaster?atlas:NeuroSyllabus+ffpathIndex/Splash^Page^Syllabus+2 Neuroanatomy Programmed Learning]<br /> :*[http://www.amazon.com/Fundamental-Neuroscience-Second-Larry-Squire/dp/0126603030 Squire, Fundamentals of Neuroscience]<br /> :*[http://www.amazon.com/Principles-Neural-Science-Eric-Kandel/dp/0838577016 Kandel, et al., &quot;Principles of Neural Science&quot;]<br /> :This paper, by Malhi, is a nice orientation in methods of neuroimaging. *[http://www.ccn.ucla.edu/wiki/images/f/f2/Malhi2007.pdf Making sense of neuroimaging in psychiatry]<br /> :*[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1359308/pdf/jphysiol01232-0142.pdf Replacement of the axoplasm of giant nerve fibres with artificial solutions]<br /> <br /> =Week 2: Linear Systems, Convolution, Fourier Transforms=<br /> ==''Wednesday 10/3/16''==<br /> <br /> ===- Linear Systems I. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ==''Wednesday 10/5/16''==<br /> ===- Linear Systems II. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> Why the emphasis on Linear Systems? Because they are actually ''easy'' (as compared to non-linear systems, which are not.) As we go through this course, we will see many ways in which linear systems theory is applied to:<br /> :Modeling of Neural Systems<br /> :Extraction of Signal from Noise<br /> :Design of Circuits<br /> :Image Enhancement<br /> :Understanding of Image artifacts, and others.<br /> <br /> Linear systems analysis is one of the great technologies of the 20th and 21st century. It is now the basis for virtually all electronics design, and its extension into the discrete (digital) domain is the basis for most of modern signal processing. <br /> <br /> In our specific case, we will use these few basic principles of linear systems to understand both the instruments we use and the neuroimaging signals we collect. When you have mastered this material, you should be in a much better position to model the systems that you study in order to develop an approach to studying them.<br /> <br /> Here is [http://www.brainmapping.org/NITP/PNA/Readings/ImaginaryNumbers.pdf A primer I wrote on imaginary numbers] that might be a helpful review.<br /> <br /> There is a nice [http://en.wikibooks.org/wiki/Calculus Wikibook on Calculus].<br /> <br /> ''Required Readings''<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapter 1<br /> :*[[media:MathematicalTools2016.pdf | Mathematical Tools]]<br /> <br /> ''Suggested Further Reading''<br /> :'''Introduction to matlab'''<br /> <br /> ''Slides shown in class''<br /> :[[media:LinearityM284A-2016.pdf | Linearity and the Fourier Transform]]<br /> <br /> Please see [http://www.brainmapping.org/NITP/PNA/html/Linearity.html MATLAB linearity demo]<br /> <br /> If you are the type who sees beauty in mathematics, the Euler identity may be one of the most beautiful pieces of math in the world.<br /> <br /> =Week 3: Math &amp; Circuits I=<br /> <br /> ==''Monday 10/10/16''==<br /> === - Circuits I. Speaker: Cameron Rodriguez ===<br /> :*[[media:Circuits_2016.pdf | Circuit slides shown in class]]<br /> <br /> ==''Wednesday 10/12/16''==<br /> === - Circuits II. ''Speaker'': Cameron Rodriguez ===<br /> <br /> Why circuits?<br /> :(Virtually) Every device you use in your research is electronic. You access your primary data only indirectly<br /> :The device you ''really'' want in your lab doesn't exist. You very well may have to make it.<br /> :There are electronic analogs to most of the linear systems that you have so far studied (and ''vice versa'' - the tools you now understand can be used to analyze and predict circuit behavior).<br /> <br /> :If you have not had any of this background, you might want to have a look at this handout, [[Media:Electricity.pdf|Electrical Circuits]], in advance. There are near infinite numbers of resources on the web that cover similar material (near enough to infinite that by the time you read all of them, there would be a whole new set.) I have recently come across a link to [http://www.allaboutcircuits.com/ Online Books: All About Circuits] ''IF'' you want practical hands-on knowledge about this material, my all-time favorite text is [http://www.google.com/products/catalog?hl=en&amp;client=safari&amp;rls=en-us&amp;ei=uVSPSfaxE5nMsAPf-tmSCQ&amp;resnum=1&amp;q=art+of+electronics&amp;um=1&amp;ie=UTF-8&amp;cid=8820839049329255765#ps-sellers &quot;Horowitz and Hill: ''The Art of Electronics.''&quot;] The latest edition, however, is dated 1989 and a new third edition is promised. I have therefore stopped short of recommending a purchase unless your need to make circuits is immediate. In this book, you will find an excellent education on the fundamental principles of electrical circuits and an incredible compendium of practical data, such as how to assemble circuit boards, how to make measurements, etc...) <br /> <br /> Readings:<br /> :*[http://www.ti.com/lit/an/sloa093/sloa093.pdf Filter Design in 30 Seconds]<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapter 2 and 10<br /> :*[https://www.circuitlab.com/ Circuit Lab ] A Free Circuit Web Base Simulator<br /> **You may or may not find this comprehensible without chapters 5 through 9.<br /> <br /> We will discuss:<br /> :*Passive Circuit Elements: Resistors, Capacitors, Inductors<br /> :*Gain<br /> :*Transformers<br /> :*Rectifiers<br /> :*Active Elements<br /> ::- ''Amplifiers''<br /> ::- ''Transistors''<br /> ::- ''Op Amps''<br /> :*Solutions with Matrices<br /> ''Suggested Further Reading''<br /> :*[http://video.google.com/videoplay?docid=5645396659673218353&amp;q=Physics+for+Future+Presidents+Electricity&amp;total=5&amp;start=0&amp;num=10&amp;so=0&amp;type=search&amp;plindex=0#0h20m30s Video intro lecture on charge, current and voltage].<br /> :*[[media:Electricity_Basics.pdf‎ | Well organized text on electrical concepts by Tony R. Kuphaldt]]<br /> :*[http://en.wikibooks.org/wiki/Circuit_Theory Circuit Theory - Wikibook]<br /> <br /> '''Suggested, Optional Readings from [http://www.dspguide.com DSPguide.com]:'''<br /> :*[http://www.dspguide.com/CH5.PDF Linear Systems]<br /> :*[http://www.dspguide.com/CH6.PDF Convolution]<br /> :*[http://www.dspguide.com/CH8.PDF Discrete Fourier Transform (DFT)]<br /> :''Note: These chapters are light on math and try to focus on a conceptual understanding''<br /> <br /> Time and Frequency / Spectral Filters<br /> <br /> ----<br /> Practice using the Fourier transform:<br /> :[http://www.brainmapping.org/NITP/PNA/ConvFThtml/ConvolutionWorksheet.pdf Fourier transform and Convolution Worksheet]. &lt;!-- [http://www.brainmapping.org/NITP/PNA/ConvFThtml/ConvFT.html (''Solutions'').] --&gt;<br /> :[http://www.brainmapping.org/NITP/PNA/ConvFThtml/Something.wav Sound file for worksheet above.]<br /> <br /> Other<br /> :*[http://www.youtube.com/watch?feature=player_embedded&amp;v=7uHfjpU3OH0#! Getting Started with Arduino]<br /> <br /> =Week 4 Signal Processing=<br /> <br /> ==''Monday 10/17/16''==<br /> === - Signal Processing. ''Speaker'': Cameron Rodriguez ===<br /> <br /> :*[[media:Signals1-PreProcessing1_2016.pdf | Signal Pre Processing Slide Set 1 shown in class]]<br /> <br /> <br /> ==''Wednesday 10/19/16''==<br /> === - Noise ''Speaker'': Cameron Rodriguez ===<br /> <br /> :*[[media:OneD_signal_Filter_Ex.pdf | Matlab Signal Tutorial printout]]<br /> <br /> It is what you ''don't'' want - usually - but things change in quantized systems<br /> :Additive noise<br /> :White Noise<br /> :Boltzmann noise<br /> :Colored Noise<br /> :Gaussian Noise<br /> :Coherent noise<br /> :Sampling Errors<br /> :Aliasing<br /> :Quantization noise<br /> :Spectral filtering<br /> <br /> Noise comes in all shapes and colors. It is present in every measurement we make, from an EEG voltage to an estimate of the effects of dopamine on forebrain signal. Our best weapons are an understanding of the statistical properties of noise, the sources of noise and the ways to control it. Noise in the discrete digital domain is special, as it is both ''created'' by digitization and amplified by sampling.<br /> <br /> Readings:<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapters 2 through 4<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Noise.pdf Noise Slides]<br /> <br /> =Week 5 - Information &amp; Statistical Theory =<br /> <br /> ==''Monday 10/24/16 - in C8-177''==<br /> === Noise Cont' &amp; Information Theory Speakers: Cameron Rodriguez &amp; John Villasenor===<br /> <br /> ==''Wednesday 10/26/16''==<br /> ===Statistical Theory ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> We will consider the general problems of statistical inference, with a concentration on developing an intuitive understanding of statistical concepts.<br /> [[Image:MeasureForMeasure.jpg|right]]<br /> <br /> Suggested reading<br /> :*[http://www.statsoft.com/textbook/stbasic.html Statsoft online text (''free'')]<br /> :*[http://www.amazon.com/Cartoon-Guide-Statistics-Larry-Gonick/dp/0062731025 The Cartoon Guide to Statistics - Gonick $17.95 new]<br /> :The latter teaches stats at what I feel to be the right level - developing intuitions about the kinds of questions that can be answered using stats and about the statistical tests and measures<br /> <br /> :'''Problem Set 3 - Statistics in matlab'''<br /> ::[[media: Problem_Set_1.doc|Problem set using stats and MATLAB]]<br /> ::[[media: Problem_Set_1B.doc|More practice with stats and MATLAB]]<br /> <br /> <br /> =MIDTERM=<br /> <br /> <br /> =Week 6 - Neurophysiology=<br /> ==''Monday 10/31/16''==<br /> === Electrophysiology I ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> We will examine our first imaging modality, EEG (and MEG).<br /> <br /> ::[[media: EEGNeurophys_161102.pdf|Slides for Neurophysiology Lectures]]<br /> <br /> ==''Wednesday 11/2/16''==<br /> === Electrophysiology II ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> Continuing with neurophysiology. ERPs &amp; Source Localization.<br /> <br /> =Week 7 - Neurophysiology =<br /> ==''Monday 11/7/16''==<br /> === Electrophysiology III ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> Continuing with neurophysiology. Connectivity &amp; Oscillations.<br /> <br /> ==''Wednesday 11/9/16''==<br /> ===Intracranial Recordings ''Speaker'': [http://neurosurgery.ucla.edu/neuromodulation-neuroimaging-lab Nanthia Suthana]===<br /> <br /> ::[[media: Suthana.pdf|Slides for Intracranial Lecture]]<br /> ::[[media: LachauxIntracranial.pdf|Lachaux et al Reading]]<br /> ::[[media: HarrisIntracranial.pdf|Harris et al Reading]]<br /> <br /> =Week 8 - Practical Neurophysiology=<br /> ==''Monday 11/14/16''==<br /> ===Building an EEG System - Circuit 1 - Going over circuit ''Speaker'': Cameron Rodriguez===<br /> <br /> :*[https://learn.sparkfun.com/tutorials/how-to-solder Through holesoldering].<br /> :*[https://learn.sparkfun.com/tutorials/pcb-basics Printed Circuit Board Basics].<br /> :*[https://learn.sparkfun.com/tutorials/how-to-install-and-setup-eagle Installing and Setting Up Eagle].<br /> :*[https://learn.sparkfun.com/tutorials/using-eagle-schematic Using Eagle Schematic].<br /> :*[https://learn.sparkfun.com/tutorials/using-eagle-board-layoutc Using Eagle Board Layout].<br /> <br /> :*[https://www.sparkfun.com/videos#pete/NJKZZArjdg8 PCB Layout 1 - According to Pete];<br /> :*[https://www.sparkfun.com/videos#pete/JANZsjRiM3w PCB Layout 2 - According to Pete];<br /> <br /> ==''Wednesday 11/16/16''==<br /> ===Data Processing Hands On ''Speaker'': Cameron Rodriguez===<br /> <br /> =Week 9 - Data Acquisition =<br /> ==''Monday 11/21/16 - in C8-177''==<br /> ===Data Acquisition ''Speaker'': Agatha Lenartowicz===<br /> Please meet in CCN suite.<br /> <br /> ==''Wednesday 11/23/16''==<br /> ===Thankgiving Wed - No Class!! ===<br /> Happy Thanksgiving!<br /> <br /> =Week 10 - Data Processing =<br /> <br /> *Please complete class evaluations via myucla. <br /> *Also we have an inhouse feedback form regarding the course syllabus: [https://docs.google.com/forms/d/e/1FAIpQLSd8xn340PxZRWb3-GQ__ZisYwFWoSgmYrcwyjvyBY5JV_mxnQ/viewform Feedback Link]<br /> <br /> Your feedback is invaluable to us!!<br /> <br /> ==''Monday 11/28/16''==<br /> ===Midterm Review, Data Processing ''Speaker'': Cameron Rodriguez/Agatha Lenartowicz===<br /> Hands on practice with EEG data, in Matlab using EEGLAB. Lab materials distributed by email 11/27/16.<br /> <br /> ==''Wednesady 11/30/16''==<br /> ===Data Processing &quot;Speaker&quot;: Cameron Rodriguez/Agatha Lenartowicz===<br /> Hands on practice with EEG data, in Matlab using EEGLAB.<br /> <br /> =Week 11 - Finals Week=<br /> Final will be distributed 11/2/16.<br /> <br /> ==''Monday 11/5/16''==<br /> ===Final Exam===<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!--<br /> SPARE CONTENT / PREVIOUS YEARS<br /> <br /> =Week 3: Stats begins!=<br /> <br /> ==''Monday 10/12/15''==<br /> <br /> ===- Statistical Fundamentals. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> :*[[media: CohenClassIntroStats11_25_13.pdf‎ | Slides used in class (set 1)]]<br /> <br /> ''Review of'':<br /> :*Descriptive Statistics: mean, mode, variance, standard deviation<br /> :*Statistical Inference. The Binomial and Normal Distribution<br /> :*Basic Tests: t-test, linear correlation<br /> :*Modeling and non-linear relations<br /> :*Bayes rule<br /> <br /> <br /> ==''Wednesday 10/14/15''==<br /> ===- Statistical Fundamentals II. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> =Week 4 - Stats and circuits=<br /> ==''Monday 10/19/15''==<br /> ===- Statistical Fundamentals III. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> ==''Wednesday 10/21/15''==<br /> ===Circuits continued: Active devices. [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ===Optics I. ''Speaker'': [mailto:zdeis@seas.ucla.edu Zachary Taylor]===<br /> [[Image:Reflection.jpg|right]]<br /> The overall goal of this lecture is to establish that:<br /> ''- Physical constants have tangible meanings''<br /> ''- Plane waves form a physically unrealizable but extremely good approximation to real systems''<br /> ''- Boundaries bend light''<br /> ''- Physical constants, plane wave mechanics, and boundaries can be used to describe the operation of a lens''<br /> ''- The PSF gives a good indication of the overall performance of an imaging system''<br /> ''- All of these concepts have analogues in other areas of engineering (ie circuits, mechanical vibrations, etc.)''''<br /> <br /> '''Outline:'''<br /> :* Constitutive parameters (ε, μ, η, n, etc.)<br /> :* Plane wave basics<br /> :* Plane waves at boundaries<br /> :* Lenses<br /> :* Advanced imaging properties of lenses<br /> :* Point spread function.<br /> <br /> ''Required Readings''<br /> Zach has very kindly agreed to post his [http://www.brainmapping.org/NITP/PNA/Readings/OpticsTaylor3-10-10.pdf Optics lecture notes].<br /> ''Suggested Further Reading''<br /> <br /> <br /> <br /> <br /> ==''Wednesday 11/19/14''== <br /> ===Optogenetics. ''Speaker'': [http://faculty.neuroscience.ucla.edu/institution/personnel?personnel_id=47031 Tom Otis]===<br /> <br /> <br /> =Week 8: =<br /> ==''Monday 12/1/14 ''==<br /> ===- Statistics for Imaging. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> [[image:BVTradeoff.jpg|right]]<br /> ''Required Readings''<br /> :*[[media: Mumford_stat_modeling.pdf | Statistical Modeling and Inference (pdf)]]<br /> :*[[media: CohenClassSlides12_2_13.pdf | Slides used in class 12/2/14 and 12/4/14]]<br /> <br /> :*The General Linear Model<br /> *Linear Algebra applied to Statistical Solutions<br /> :*Analysis of Variance<br /> <br /> ==''Wednesday 11/20/14 ...''==<br /> === - Statistics for Imaging II. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> *Fixed and Random Effects<br /> *Repeated measures<br /> :*Bonferroni and Other Corrections<br /> *Non-Parametric Methods<br /> *Autocorrelation<br /> *Unknown Distributions<br /> <br /> ''Required Readings''<br /> :*[[media: CohenClassSlides12_2_13.pdf‎ | same as 12/2/14]]<br /> <br /> ''Suggested Further Reading''<br /> <br /> :*[http://ccn.ucla.edu/media/PNI20121121.mp4 Lecture Video - To be Posted Shortly - Please Right Click and Save rather than Stream - Thank you Alan and Edward!]<br /> <br /> =Week 9: =<br /> ==''Monday 11/25/14''==<br /> === - TBD. [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> ==''Wednesday 11/27/14''==<br /> ===- The Organization of the Human Brain. ''Speaker'': [http://ccn.ucla.edu/bmcweb/bmc_bios/SusanBookheimer/ Susan Bookheimer]===<br /> <br /> We will discuss the general organization of the human brain, and the regional specialization of cortical areas. The emphasis will be on understanding principles of organization:<br /> *Phylogenetic Layering<br /> *Functional Specialization<br /> *Principles Divisions of the Brain<br /> *Brain Systems<br /> <br /> ''Required Readings''<br /> :*[http://da.biostr.washington.edu:80/cgi-bin/DA/PageMaster?atlas:NeuroSyllabus+ffpathIndex/Splash^Page^Syllabus+2 Neuroanatomy Programmed Learning]<br /> :*[[media:Cognitive_science_and_neuro_2012.pdf | Slides shown in Class UPDATED 2012/10/17]]<br /> ''Suggested Further Reading''<br /> <br /> :*[[media:PNIA_2012_PS1_Soln.pdf‎ | Solution to Problem Set 1]]<br /> ===- Human Electrophysiology ''Speakers'': [http://greenlab.npih.ucla.edu/ROSTER.html Agatha Lenartowicz], [http://dgsom.healthsciences.ucla.edu/institution/personnel?personnel_id=9140 John Stern]===<br /> <br /> ''Evoked Responses'' - Guest Lecturer: [http://greenlab.npih.ucla.edu/ROSTER.html Jonathan Wynn]<br /> *A look at real EEG data<br /> *Preprocessing:<br /> **filtering<br /> **artifact detection/removal<br /> *averaging<br /> *single events<br /> *interpretation<br /> <br /> ''Clinical EEG'' - Guest Lecturer: [http://dgsom.healthsciences.ucla.edu/institution/personnel?personnel_id=9140 John Stern]<br /> *Normal and Abnormal EEG<br /> *EEG as a marker for brain state<br /> **sleep staging<br /> **alpha and relaxation<br /> *Neurofeedback??? --&gt;</div>

Alenartowicz https://www.ccn.ucla.edu/wiki/index.php?title=Principles_of_Neuroimaging_A_-_2016&diff=3223 2016-11-29T04:01:23Z

<p>Alenartowicz: </p> <hr /> <div>Principles of Neuroimaging A, Fall, 2016 - Class Schedule and Syllabus<br /> <br /> <br /> ==='''Neuroimaging journal Club (''required for NITP certificate'')'''===<br /> Contact: Katherine Lawrence (katherine.E.Lawrence@ucla.edu) or Janelle Liu (janelle.j.liu@ucla.edu), Faculty Sponsor: Jamie Feusner (JFeusner@mednet.ucla.edu)<br /> <br /> ==This schedule ''will'' change!==<br /> <br /> :'''[[Principles_of_Neuroimaging_-_2016-2017 | Back to main course page for Principles of Neuroimaging]]'''<br /> <br /> :'''Coming up next!: [[Principles_of_Neuroimaging_B_-_2017 | M284B Principles of Neuroimaging B]]'''<br /> <br /> =Course Reading=<br /> ===Required Reading===<br /> :'''Signal Processing for Neuroscientists''' by ''Wim van Drongelen''<br /> ::This can be found as a PDF on scribd.com, for a small fee of $8.99<br /> <br /> ===Supplemental Reading===<br /> :'''Matlab for Neuroscientists'''<br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/88212458/Matlab-Matlab-for-Neuroscientists<br /> <br /> :'''Cartoon Guide to Statistics''' <br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/148072668/Cartoon-Guide-to-Statistics<br /> <br /> :'''NOTE:''' if you subscribe for a Scribd account for a day, you can download as many documents as you like for one fee.<br /> <br /> <br /> =Week 1: Orientation to Neuro-imaging, Neurons, Brains=<br /> <br /> ==''Monday 9/26/16'' ==<br /> ===- Images. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284AImages-intro.pdf | Lecture Slides Images]]<br /> <br /> ==''Wednesday 9/28/16''==<br /> ===- Neurons &amp; Signaling. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> In this first class we will review the basics of neurophysiology with an eye towards what signals of brain function might be visible to the neuroimager. We will discuss information coding, energetics, size and time scales.<br /> [[Image:Neurons.jpg|right]]<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:TheActiveBrain.pdf | The Active Brain]]<br /> :*[[media:NeuronFunctionM284-2016_SM.pdf | Neuron function slides shown in class]]<br /> :*[http://www.science.smith.edu/departments/neurosci/courses/bio330/squid.html Videos of giant squid experiments]<br /> ''Suggested Further Reading''<br /> :*[http://ccn.ucla.edu/wiki/images/5/5a/CAVEAT_LECTOR.pdf Caveat Lector - the misuse of neuroimaging]<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Protected/Kosslyn1999.pdf &quot;If Neuroimaging is the Answer, What is the Question?&quot; Kosslyn, 1999]<br /> :*[http://da.biostr.washington.edu:80/cgi-bin/DA/PageMaster?atlas:NeuroSyllabus+ffpathIndex/Splash^Page^Syllabus+2 Neuroanatomy Programmed Learning]<br /> :*[http://www.amazon.com/Fundamental-Neuroscience-Second-Larry-Squire/dp/0126603030 Squire, Fundamentals of Neuroscience]<br /> :*[http://www.amazon.com/Principles-Neural-Science-Eric-Kandel/dp/0838577016 Kandel, et al., &quot;Principles of Neural Science&quot;]<br /> :This paper, by Malhi, is a nice orientation in methods of neuroimaging. *[http://www.ccn.ucla.edu/wiki/images/f/f2/Malhi2007.pdf Making sense of neuroimaging in psychiatry]<br /> :*[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1359308/pdf/jphysiol01232-0142.pdf Replacement of the axoplasm of giant nerve fibres with artificial solutions]<br /> <br /> =Week 2: Linear Systems, Convolution, Fourier Transforms=<br /> ==''Wednesday 10/3/16''==<br /> <br /> ===- Linear Systems I. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ==''Wednesday 10/5/16''==<br /> ===- Linear Systems II. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> Why the emphasis on Linear Systems? Because they are actually ''easy'' (as compared to non-linear systems, which are not.) As we go through this course, we will see many ways in which linear systems theory is applied to:<br /> :Modeling of Neural Systems<br /> :Extraction of Signal from Noise<br /> :Design of Circuits<br /> :Image Enhancement<br /> :Understanding of Image artifacts, and others.<br /> <br /> Linear systems analysis is one of the great technologies of the 20th and 21st century. It is now the basis for virtually all electronics design, and its extension into the discrete (digital) domain is the basis for most of modern signal processing. <br /> <br /> In our specific case, we will use these few basic principles of linear systems to understand both the instruments we use and the neuroimaging signals we collect. When you have mastered this material, you should be in a much better position to model the systems that you study in order to develop an approach to studying them.<br /> <br /> Here is [http://www.brainmapping.org/NITP/PNA/Readings/ImaginaryNumbers.pdf A primer I wrote on imaginary numbers] that might be a helpful review.<br /> <br /> There is a nice [http://en.wikibooks.org/wiki/Calculus Wikibook on Calculus].<br /> <br /> ''Required Readings''<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapter 1<br /> :*[[media:MathematicalTools2016.pdf | Mathematical Tools]]<br /> <br /> ''Suggested Further Reading''<br /> :'''Introduction to matlab'''<br /> <br /> ''Slides shown in class''<br /> :[[media:LinearityM284A-2016.pdf | Linearity and the Fourier Transform]]<br /> <br /> Please see [http://www.brainmapping.org/NITP/PNA/html/Linearity.html MATLAB linearity demo]<br /> <br /> If you are the type who sees beauty in mathematics, the Euler identity may be one of the most beautiful pieces of math in the world.<br /> <br /> =Week 3: Math &amp; Circuits I=<br /> <br /> ==''Monday 10/10/16''==<br /> === - Circuits I. Speaker: Cameron Rodriguez ===<br /> :*[[media:Circuits_2016.pdf | Circuit slides shown in class]]<br /> <br /> ==''Wednesday 10/12/16''==<br /> === - Circuits II. ''Speaker'': Cameron Rodriguez ===<br /> <br /> Why circuits?<br /> :(Virtually) Every device you use in your research is electronic. You access your primary data only indirectly<br /> :The device you ''really'' want in your lab doesn't exist. You very well may have to make it.<br /> :There are electronic analogs to most of the linear systems that you have so far studied (and ''vice versa'' - the tools you now understand can be used to analyze and predict circuit behavior).<br /> <br /> :If you have not had any of this background, you might want to have a look at this handout, [[Media:Electricity.pdf|Electrical Circuits]], in advance. There are near infinite numbers of resources on the web that cover similar material (near enough to infinite that by the time you read all of them, there would be a whole new set.) I have recently come across a link to [http://www.allaboutcircuits.com/ Online Books: All About Circuits] ''IF'' you want practical hands-on knowledge about this material, my all-time favorite text is [http://www.google.com/products/catalog?hl=en&amp;client=safari&amp;rls=en-us&amp;ei=uVSPSfaxE5nMsAPf-tmSCQ&amp;resnum=1&amp;q=art+of+electronics&amp;um=1&amp;ie=UTF-8&amp;cid=8820839049329255765#ps-sellers &quot;Horowitz and Hill: ''The Art of Electronics.''&quot;] The latest edition, however, is dated 1989 and a new third edition is promised. I have therefore stopped short of recommending a purchase unless your need to make circuits is immediate. In this book, you will find an excellent education on the fundamental principles of electrical circuits and an incredible compendium of practical data, such as how to assemble circuit boards, how to make measurements, etc...) <br /> <br /> Readings:<br /> :*[http://www.ti.com/lit/an/sloa093/sloa093.pdf Filter Design in 30 Seconds]<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapter 2 and 10<br /> :*[https://www.circuitlab.com/ Circuit Lab ] A Free Circuit Web Base Simulator<br /> **You may or may not find this comprehensible without chapters 5 through 9.<br /> <br /> We will discuss:<br /> :*Passive Circuit Elements: Resistors, Capacitors, Inductors<br /> :*Gain<br /> :*Transformers<br /> :*Rectifiers<br /> :*Active Elements<br /> ::- ''Amplifiers''<br /> ::- ''Transistors''<br /> ::- ''Op Amps''<br /> :*Solutions with Matrices<br /> ''Suggested Further Reading''<br /> :*[http://video.google.com/videoplay?docid=5645396659673218353&amp;q=Physics+for+Future+Presidents+Electricity&amp;total=5&amp;start=0&amp;num=10&amp;so=0&amp;type=search&amp;plindex=0#0h20m30s Video intro lecture on charge, current and voltage].<br /> :*[[media:Electricity_Basics.pdf‎ | Well organized text on electrical concepts by Tony R. Kuphaldt]]<br /> :*[http://en.wikibooks.org/wiki/Circuit_Theory Circuit Theory - Wikibook]<br /> <br /> '''Suggested, Optional Readings from [http://www.dspguide.com DSPguide.com]:'''<br /> :*[http://www.dspguide.com/CH5.PDF Linear Systems]<br /> :*[http://www.dspguide.com/CH6.PDF Convolution]<br /> :*[http://www.dspguide.com/CH8.PDF Discrete Fourier Transform (DFT)]<br /> :''Note: These chapters are light on math and try to focus on a conceptual understanding''<br /> <br /> Time and Frequency / Spectral Filters<br /> <br /> ----<br /> Practice using the Fourier transform:<br /> :[http://www.brainmapping.org/NITP/PNA/ConvFThtml/ConvolutionWorksheet.pdf Fourier transform and Convolution Worksheet]. &lt;!-- [http://www.brainmapping.org/NITP/PNA/ConvFThtml/ConvFT.html (''Solutions'').] --&gt;<br /> :[http://www.brainmapping.org/NITP/PNA/ConvFThtml/Something.wav Sound file for worksheet above.]<br /> <br /> Other<br /> :*[http://www.youtube.com/watch?feature=player_embedded&amp;v=7uHfjpU3OH0#! Getting Started with Arduino]<br /> <br /> =Week 4 Signal Processing=<br /> <br /> ==''Monday 10/17/16''==<br /> === - Signal Processing. ''Speaker'': Cameron Rodriguez ===<br /> <br /> :*[[media:Signals1-PreProcessing1_2016.pdf | Signal Pre Processing Slide Set 1 shown in class]]<br /> <br /> <br /> ==''Wednesday 10/19/16''==<br /> === - Noise ''Speaker'': Cameron Rodriguez ===<br /> <br /> :*[[media:OneD_signal_Filter_Ex.pdf | Matlab Signal Tutorial printout]]<br /> <br /> It is what you ''don't'' want - usually - but things change in quantized systems<br /> :Additive noise<br /> :White Noise<br /> :Boltzmann noise<br /> :Colored Noise<br /> :Gaussian Noise<br /> :Coherent noise<br /> :Sampling Errors<br /> :Aliasing<br /> :Quantization noise<br /> :Spectral filtering<br /> <br /> Noise comes in all shapes and colors. It is present in every measurement we make, from an EEG voltage to an estimate of the effects of dopamine on forebrain signal. Our best weapons are an understanding of the statistical properties of noise, the sources of noise and the ways to control it. Noise in the discrete digital domain is special, as it is both ''created'' by digitization and amplified by sampling.<br /> <br /> Readings:<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapters 2 through 4<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Noise.pdf Noise Slides]<br /> <br /> =Week 5 - Information &amp; Statistical Theory =<br /> <br /> ==''Monday 10/24/16 - in C8-177''==<br /> === Noise Cont' &amp; Information Theory Speakers: Cameron Rodriguez &amp; John Villasenor===<br /> <br /> ==''Wednesday 10/26/16''==<br /> ===Statistical Theory ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> We will consider the general problems of statistical inference, with a concentration on developing an intuitive understanding of statistical concepts.<br /> [[Image:MeasureForMeasure.jpg|right]]<br /> <br /> Suggested reading<br /> :*[http://www.statsoft.com/textbook/stbasic.html Statsoft online text (''free'')]<br /> :*[http://www.amazon.com/Cartoon-Guide-Statistics-Larry-Gonick/dp/0062731025 The Cartoon Guide to Statistics - Gonick $17.95 new]<br /> :The latter teaches stats at what I feel to be the right level - developing intuitions about the kinds of questions that can be answered using stats and about the statistical tests and measures<br /> <br /> :'''Problem Set 3 - Statistics in matlab'''<br /> ::[[media: Problem_Set_1.doc|Problem set using stats and MATLAB]]<br /> ::[[media: Problem_Set_1B.doc|More practice with stats and MATLAB]]<br /> <br /> <br /> =MIDTERM=<br /> <br /> <br /> =Week 6 - Neurophysiology=<br /> ==''Monday 10/31/16''==<br /> === Electrophysiology I ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> We will examine our first imaging modality, EEG (and MEG).<br /> <br /> ::[[media: EEGNeurophys_161102.pdf|Slides for Neurophysiology Lectures]]<br /> <br /> ==''Wednesday 11/2/16''==<br /> === Electrophysiology II ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> Continuing with neurophysiology. ERPs &amp; Source Localization.<br /> <br /> =Week 7 - Neurophysiology =<br /> ==''Monday 11/7/16''==<br /> === Electrophysiology III ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> Continuing with neurophysiology. Connectivity &amp; Oscillations.<br /> <br /> ==''Wednesday 11/9/16''==<br /> ===Intracranial Recordings ''Speaker'': [http://neurosurgery.ucla.edu/neuromodulation-neuroimaging-lab Nanthia Suthana]===<br /> <br /> ::[[media: Suthana.pdf|Slides for Intracranial Lecture]]<br /> ::[[media: LachauxIntracranial.pdf|Lachaux et al Reading]]<br /> ::[[media: HarrisIntracranial.pdf|Harris et al Reading]]<br /> <br /> =Week 8 - Practical Neurophysiology=<br /> ==''Monday 11/14/16''==<br /> ===Building an EEG System - Circuit 1 - Going over circuit ''Speaker'': Cameron Rodriguez===<br /> <br /> :*[https://learn.sparkfun.com/tutorials/how-to-solder Through holesoldering].<br /> :*[https://learn.sparkfun.com/tutorials/pcb-basics Printed Circuit Board Basics].<br /> :*[https://learn.sparkfun.com/tutorials/how-to-install-and-setup-eagle Installing and Setting Up Eagle].<br /> :*[https://learn.sparkfun.com/tutorials/using-eagle-schematic Using Eagle Schematic].<br /> :*[https://learn.sparkfun.com/tutorials/using-eagle-board-layoutc Using Eagle Board Layout].<br /> <br /> :*[https://www.sparkfun.com/videos#pete/NJKZZArjdg8 PCB Layout 1 - According to Pete];<br /> :*[https://www.sparkfun.com/videos#pete/JANZsjRiM3w PCB Layout 2 - According to Pete];<br /> <br /> ==''Wednesday 11/16/16''==<br /> ===Data Processing Hands On ''Speaker'': Cameron Rodriguez===<br /> <br /> =Week 9 - Data Acquisition =<br /> ==''Monday 11/21/16 - in C8-177''==<br /> ===Data Acquisition ''Speaker'': Agatha Lenartowicz===<br /> Please meet in CCN suite.<br /> <br /> ==''Wednesday 11/23/16''==<br /> ===Thankgiving Wed - No Class ===<br /> <br /> =Week 10 - Data Processing =<br /> <br /> *Please complete class evaluations via myucla. <br /> *Also we have an inhouse feedback form regarding the course syllabus: [https://docs.google.com/forms/d/e/1FAIpQLSd8xn340PxZRWb3-GQ__ZisYwFWoSgmYrcwyjvyBY5JV_mxnQ/viewform Feedback Link]<br /> <br /> Your feedback is invaluable to us!!<br /> <br /> ==''Monday 11/28/16''==<br /> ===Midterm Review, Data Processing ''Speaker'': Cameron Rodriguez/Agatha Lenartowicz===<br /> Hands on practice with EEG data, in Matlab using EEGLAB. Lab materials distributed by email 11/27/16.<br /> <br /> ==''Wednesady 11/30/16''==<br /> ===Data Processing &quot;Speaker&quot;: Cameron Rodriguez/Agatha Lenartowicz===<br /> Hands on practice with EEG data, in Matlab using EEGLAB.<br /> <br /> =Week 11 - Finals Week=<br /> Final will be distributed 11/2/16.<br /> <br /> ==''Monday 11/5/16''==<br /> ===Final Exam===<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!--<br /> SPARE CONTENT / PREVIOUS YEARS<br /> <br /> =Week 3: Stats begins!=<br /> <br /> ==''Monday 10/12/15''==<br /> <br /> ===- Statistical Fundamentals. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> :*[[media: CohenClassIntroStats11_25_13.pdf‎ | Slides used in class (set 1)]]<br /> <br /> ''Review of'':<br /> :*Descriptive Statistics: mean, mode, variance, standard deviation<br /> :*Statistical Inference. The Binomial and Normal Distribution<br /> :*Basic Tests: t-test, linear correlation<br /> :*Modeling and non-linear relations<br /> :*Bayes rule<br /> <br /> <br /> ==''Wednesday 10/14/15''==<br /> ===- Statistical Fundamentals II. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> =Week 4 - Stats and circuits=<br /> ==''Monday 10/19/15''==<br /> ===- Statistical Fundamentals III. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> ==''Wednesday 10/21/15''==<br /> ===Circuits continued: Active devices. [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ===Optics I. ''Speaker'': [mailto:zdeis@seas.ucla.edu Zachary Taylor]===<br /> [[Image:Reflection.jpg|right]]<br /> The overall goal of this lecture is to establish that:<br /> ''- Physical constants have tangible meanings''<br /> ''- Plane waves form a physically unrealizable but extremely good approximation to real systems''<br /> ''- Boundaries bend light''<br /> ''- Physical constants, plane wave mechanics, and boundaries can be used to describe the operation of a lens''<br /> ''- The PSF gives a good indication of the overall performance of an imaging system''<br /> ''- All of these concepts have analogues in other areas of engineering (ie circuits, mechanical vibrations, etc.)''''<br /> <br /> '''Outline:'''<br /> :* Constitutive parameters (ε, μ, η, n, etc.)<br /> :* Plane wave basics<br /> :* Plane waves at boundaries<br /> :* Lenses<br /> :* Advanced imaging properties of lenses<br /> :* Point spread function.<br /> <br /> ''Required Readings''<br /> Zach has very kindly agreed to post his [http://www.brainmapping.org/NITP/PNA/Readings/OpticsTaylor3-10-10.pdf Optics lecture notes].<br /> ''Suggested Further Reading''<br /> <br /> <br /> <br /> <br /> ==''Wednesday 11/19/14''== <br /> ===Optogenetics. ''Speaker'': [http://faculty.neuroscience.ucla.edu/institution/personnel?personnel_id=47031 Tom Otis]===<br /> <br /> <br /> =Week 8: =<br /> ==''Monday 12/1/14 ''==<br /> ===- Statistics for Imaging. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> [[image:BVTradeoff.jpg|right]]<br /> ''Required Readings''<br /> :*[[media: Mumford_stat_modeling.pdf | Statistical Modeling and Inference (pdf)]]<br /> :*[[media: CohenClassSlides12_2_13.pdf | Slides used in class 12/2/14 and 12/4/14]]<br /> <br /> :*The General Linear Model<br /> *Linear Algebra applied to Statistical Solutions<br /> :*Analysis of Variance<br /> <br /> ==''Wednesday 11/20/14 ...''==<br /> === - Statistics for Imaging II. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> *Fixed and Random Effects<br /> *Repeated measures<br /> :*Bonferroni and Other Corrections<br /> *Non-Parametric Methods<br /> *Autocorrelation<br /> *Unknown Distributions<br /> <br /> ''Required Readings''<br /> :*[[media: CohenClassSlides12_2_13.pdf‎ | same as 12/2/14]]<br /> <br /> ''Suggested Further Reading''<br /> <br /> :*[http://ccn.ucla.edu/media/PNI20121121.mp4 Lecture Video - To be Posted Shortly - Please Right Click and Save rather than Stream - Thank you Alan and Edward!]<br /> <br /> =Week 9: =<br /> ==''Monday 11/25/14''==<br /> === - TBD. [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> ==''Wednesday 11/27/14''==<br /> ===- The Organization of the Human Brain. ''Speaker'': [http://ccn.ucla.edu/bmcweb/bmc_bios/SusanBookheimer/ Susan Bookheimer]===<br /> <br /> We will discuss the general organization of the human brain, and the regional specialization of cortical areas. The emphasis will be on understanding principles of organization:<br /> *Phylogenetic Layering<br /> *Functional Specialization<br /> *Principles Divisions of the Brain<br /> *Brain Systems<br /> <br /> ''Required Readings''<br /> :*[http://da.biostr.washington.edu:80/cgi-bin/DA/PageMaster?atlas:NeuroSyllabus+ffpathIndex/Splash^Page^Syllabus+2 Neuroanatomy Programmed Learning]<br /> :*[[media:Cognitive_science_and_neuro_2012.pdf | Slides shown in Class UPDATED 2012/10/17]]<br /> ''Suggested Further Reading''<br /> <br /> :*[[media:PNIA_2012_PS1_Soln.pdf‎ | Solution to Problem Set 1]]<br /> ===- Human Electrophysiology ''Speakers'': [http://greenlab.npih.ucla.edu/ROSTER.html Agatha Lenartowicz], [http://dgsom.healthsciences.ucla.edu/institution/personnel?personnel_id=9140 John Stern]===<br /> <br /> ''Evoked Responses'' - Guest Lecturer: [http://greenlab.npih.ucla.edu/ROSTER.html Jonathan Wynn]<br /> *A look at real EEG data<br /> *Preprocessing:<br /> **filtering<br /> **artifact detection/removal<br /> *averaging<br /> *single events<br /> *interpretation<br /> <br /> ''Clinical EEG'' - Guest Lecturer: [http://dgsom.healthsciences.ucla.edu/institution/personnel?personnel_id=9140 John Stern]<br /> *Normal and Abnormal EEG<br /> *EEG as a marker for brain state<br /> **sleep staging<br /> **alpha and relaxation<br /> *Neurofeedback??? --&gt;</div>

Alenartowicz https://www.ccn.ucla.edu/wiki/index.php?title=Principles_of_Neuroimaging_A_-_2016&diff=3222 2016-11-29T04:00:22Z

<p>Alenartowicz: /* Monday 11/28/16 */</p> <hr /> <div>Principles of Neuroimaging A, Fall, 2016 - Class Schedule and Syllabus<br /> <br /> <br /> ==='''Neuroimaging journal Club (''required for NITP certificate'')'''===<br /> Contact: Katherine Lawrence (katherine.E.Lawrence@ucla.edu) or Janelle Liu (janelle.j.liu@ucla.edu), Faculty Sponsor: Jamie Feusner (JFeusner@mednet.ucla.edu)<br /> <br /> ==This schedule ''will'' change!==<br /> <br /> :'''[[Principles_of_Neuroimaging_-_2016-2017 | Back to main course page for Principles of Neuroimaging]]'''<br /> <br /> :'''Coming up next!: [[Principles_of_Neuroimaging_B_-_2017 | M284B Principles of Neuroimaging B]]'''<br /> <br /> =Course Reading=<br /> ===Required Reading===<br /> :'''Signal Processing for Neuroscientists''' by ''Wim van Drongelen''<br /> ::This can be found as a PDF on scribd.com, for a small fee of $8.99<br /> <br /> ===Supplemental Reading===<br /> :'''Matlab for Neuroscientists'''<br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/88212458/Matlab-Matlab-for-Neuroscientists<br /> <br /> :'''Cartoon Guide to Statistics''' <br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/148072668/Cartoon-Guide-to-Statistics<br /> <br /> :'''NOTE:''' if you subscribe for a Scribd account for a day, you can download as many documents as you like for one fee.<br /> <br /> <br /> =Week 1: Orientation to Neuro-imaging, Neurons, Brains=<br /> <br /> ==''Monday 9/26/16'' ==<br /> ===- Images. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284AImages-intro.pdf | Lecture Slides Images]]<br /> <br /> ==''Wednesday 9/28/16''==<br /> ===- Neurons &amp; Signaling. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> In this first class we will review the basics of neurophysiology with an eye towards what signals of brain function might be visible to the neuroimager. We will discuss information coding, energetics, size and time scales.<br /> [[Image:Neurons.jpg|right]]<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:TheActiveBrain.pdf | The Active Brain]]<br /> :*[[media:NeuronFunctionM284-2016_SM.pdf | Neuron function slides shown in class]]<br /> :*[http://www.science.smith.edu/departments/neurosci/courses/bio330/squid.html Videos of giant squid experiments]<br /> ''Suggested Further Reading''<br /> :*[http://ccn.ucla.edu/wiki/images/5/5a/CAVEAT_LECTOR.pdf Caveat Lector - the misuse of neuroimaging]<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Protected/Kosslyn1999.pdf &quot;If Neuroimaging is the Answer, What is the Question?&quot; Kosslyn, 1999]<br /> :*[http://da.biostr.washington.edu:80/cgi-bin/DA/PageMaster?atlas:NeuroSyllabus+ffpathIndex/Splash^Page^Syllabus+2 Neuroanatomy Programmed Learning]<br /> :*[http://www.amazon.com/Fundamental-Neuroscience-Second-Larry-Squire/dp/0126603030 Squire, Fundamentals of Neuroscience]<br /> :*[http://www.amazon.com/Principles-Neural-Science-Eric-Kandel/dp/0838577016 Kandel, et al., &quot;Principles of Neural Science&quot;]<br /> :This paper, by Malhi, is a nice orientation in methods of neuroimaging. *[http://www.ccn.ucla.edu/wiki/images/f/f2/Malhi2007.pdf Making sense of neuroimaging in psychiatry]<br /> :*[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1359308/pdf/jphysiol01232-0142.pdf Replacement of the axoplasm of giant nerve fibres with artificial solutions]<br /> <br /> =Week 2: Linear Systems, Convolution, Fourier Transforms=<br /> ==''Wednesday 10/3/16''==<br /> <br /> ===- Linear Systems I. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ==''Wednesday 10/5/16''==<br /> ===- Linear Systems II. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> Why the emphasis on Linear Systems? Because they are actually ''easy'' (as compared to non-linear systems, which are not.) As we go through this course, we will see many ways in which linear systems theory is applied to:<br /> :Modeling of Neural Systems<br /> :Extraction of Signal from Noise<br /> :Design of Circuits<br /> :Image Enhancement<br /> :Understanding of Image artifacts, and others.<br /> <br /> Linear systems analysis is one of the great technologies of the 20th and 21st century. It is now the basis for virtually all electronics design, and its extension into the discrete (digital) domain is the basis for most of modern signal processing. <br /> <br /> In our specific case, we will use these few basic principles of linear systems to understand both the instruments we use and the neuroimaging signals we collect. When you have mastered this material, you should be in a much better position to model the systems that you study in order to develop an approach to studying them.<br /> <br /> Here is [http://www.brainmapping.org/NITP/PNA/Readings/ImaginaryNumbers.pdf A primer I wrote on imaginary numbers] that might be a helpful review.<br /> <br /> There is a nice [http://en.wikibooks.org/wiki/Calculus Wikibook on Calculus].<br /> <br /> ''Required Readings''<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapter 1<br /> :*[[media:MathematicalTools2016.pdf | Mathematical Tools]]<br /> <br /> ''Suggested Further Reading''<br /> :'''Introduction to matlab'''<br /> <br /> ''Slides shown in class''<br /> :[[media:LinearityM284A-2016.pdf | Linearity and the Fourier Transform]]<br /> <br /> Please see [http://www.brainmapping.org/NITP/PNA/html/Linearity.html MATLAB linearity demo]<br /> <br /> If you are the type who sees beauty in mathematics, the Euler identity may be one of the most beautiful pieces of math in the world.<br /> <br /> =Week 3: Math &amp; Circuits I=<br /> <br /> ==''Monday 10/10/16''==<br /> === - Circuits I. Speaker: Cameron Rodriguez ===<br /> :*[[media:Circuits_2016.pdf | Circuit slides shown in class]]<br /> <br /> ==''Wednesday 10/12/16''==<br /> === - Circuits II. ''Speaker'': Cameron Rodriguez ===<br /> <br /> Why circuits?<br /> :(Virtually) Every device you use in your research is electronic. You access your primary data only indirectly<br /> :The device you ''really'' want in your lab doesn't exist. You very well may have to make it.<br /> :There are electronic analogs to most of the linear systems that you have so far studied (and ''vice versa'' - the tools you now understand can be used to analyze and predict circuit behavior).<br /> <br /> :If you have not had any of this background, you might want to have a look at this handout, [[Media:Electricity.pdf|Electrical Circuits]], in advance. There are near infinite numbers of resources on the web that cover similar material (near enough to infinite that by the time you read all of them, there would be a whole new set.) I have recently come across a link to [http://www.allaboutcircuits.com/ Online Books: All About Circuits] ''IF'' you want practical hands-on knowledge about this material, my all-time favorite text is [http://www.google.com/products/catalog?hl=en&amp;client=safari&amp;rls=en-us&amp;ei=uVSPSfaxE5nMsAPf-tmSCQ&amp;resnum=1&amp;q=art+of+electronics&amp;um=1&amp;ie=UTF-8&amp;cid=8820839049329255765#ps-sellers &quot;Horowitz and Hill: ''The Art of Electronics.''&quot;] The latest edition, however, is dated 1989 and a new third edition is promised. I have therefore stopped short of recommending a purchase unless your need to make circuits is immediate. In this book, you will find an excellent education on the fundamental principles of electrical circuits and an incredible compendium of practical data, such as how to assemble circuit boards, how to make measurements, etc...) <br /> <br /> Readings:<br /> :*[http://www.ti.com/lit/an/sloa093/sloa093.pdf Filter Design in 30 Seconds]<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapter 2 and 10<br /> :*[https://www.circuitlab.com/ Circuit Lab ] A Free Circuit Web Base Simulator<br /> **You may or may not find this comprehensible without chapters 5 through 9.<br /> <br /> We will discuss:<br /> :*Passive Circuit Elements: Resistors, Capacitors, Inductors<br /> :*Gain<br /> :*Transformers<br /> :*Rectifiers<br /> :*Active Elements<br /> ::- ''Amplifiers''<br /> ::- ''Transistors''<br /> ::- ''Op Amps''<br /> :*Solutions with Matrices<br /> ''Suggested Further Reading''<br /> :*[http://video.google.com/videoplay?docid=5645396659673218353&amp;q=Physics+for+Future+Presidents+Electricity&amp;total=5&amp;start=0&amp;num=10&amp;so=0&amp;type=search&amp;plindex=0#0h20m30s Video intro lecture on charge, current and voltage].<br /> :*[[media:Electricity_Basics.pdf‎ | Well organized text on electrical concepts by Tony R. Kuphaldt]]<br /> :*[http://en.wikibooks.org/wiki/Circuit_Theory Circuit Theory - Wikibook]<br /> <br /> '''Suggested, Optional Readings from [http://www.dspguide.com DSPguide.com]:'''<br /> :*[http://www.dspguide.com/CH5.PDF Linear Systems]<br /> :*[http://www.dspguide.com/CH6.PDF Convolution]<br /> :*[http://www.dspguide.com/CH8.PDF Discrete Fourier Transform (DFT)]<br /> :''Note: These chapters are light on math and try to focus on a conceptual understanding''<br /> <br /> Time and Frequency / Spectral Filters<br /> <br /> ----<br /> Practice using the Fourier transform:<br /> :[http://www.brainmapping.org/NITP/PNA/ConvFThtml/ConvolutionWorksheet.pdf Fourier transform and Convolution Worksheet]. &lt;!-- [http://www.brainmapping.org/NITP/PNA/ConvFThtml/ConvFT.html (''Solutions'').] --&gt;<br /> :[http://www.brainmapping.org/NITP/PNA/ConvFThtml/Something.wav Sound file for worksheet above.]<br /> <br /> Other<br /> :*[http://www.youtube.com/watch?feature=player_embedded&amp;v=7uHfjpU3OH0#! Getting Started with Arduino]<br /> <br /> =Week 4 Signal Processing=<br /> <br /> ==''Monday 10/17/16''==<br /> === - Signal Processing. ''Speaker'': Cameron Rodriguez ===<br /> <br /> :*[[media:Signals1-PreProcessing1_2016.pdf | Signal Pre Processing Slide Set 1 shown in class]]<br /> <br /> <br /> ==''Wednesday 10/19/16''==<br /> === - Noise ''Speaker'': Cameron Rodriguez ===<br /> <br /> :*[[media:OneD_signal_Filter_Ex.pdf | Matlab Signal Tutorial printout]]<br /> <br /> It is what you ''don't'' want - usually - but things change in quantized systems<br /> :Additive noise<br /> :White Noise<br /> :Boltzmann noise<br /> :Colored Noise<br /> :Gaussian Noise<br /> :Coherent noise<br /> :Sampling Errors<br /> :Aliasing<br /> :Quantization noise<br /> :Spectral filtering<br /> <br /> Noise comes in all shapes and colors. It is present in every measurement we make, from an EEG voltage to an estimate of the effects of dopamine on forebrain signal. Our best weapons are an understanding of the statistical properties of noise, the sources of noise and the ways to control it. Noise in the discrete digital domain is special, as it is both ''created'' by digitization and amplified by sampling.<br /> <br /> Readings:<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapters 2 through 4<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Noise.pdf Noise Slides]<br /> <br /> =Week 5 - Information &amp; Statistical Theory =<br /> <br /> ==''Monday 10/24/16 - in C8-177''==<br /> === Noise Cont' &amp; Information Theory Speakers: Cameron Rodriguez &amp; John Villasenor===<br /> <br /> ==''Wednesday 10/26/16''==<br /> ===Statistical Theory ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> We will consider the general problems of statistical inference, with a concentration on developing an intuitive understanding of statistical concepts.<br /> [[Image:MeasureForMeasure.jpg|right]]<br /> <br /> Suggested reading<br /> :*[http://www.statsoft.com/textbook/stbasic.html Statsoft online text (''free'')]<br /> :*[http://www.amazon.com/Cartoon-Guide-Statistics-Larry-Gonick/dp/0062731025 The Cartoon Guide to Statistics - Gonick $17.95 new]<br /> :The latter teaches stats at what I feel to be the right level - developing intuitions about the kinds of questions that can be answered using stats and about the statistical tests and measures<br /> <br /> :'''Problem Set 3 - Statistics in matlab'''<br /> ::[[media: Problem_Set_1.doc|Problem set using stats and MATLAB]]<br /> ::[[media: Problem_Set_1B.doc|More practice with stats and MATLAB]]<br /> <br /> <br /> =MIDTERM=<br /> <br /> <br /> =Week 6 - Neurophysiology=<br /> ==''Monday 10/31/16''==<br /> === Electrophysiology I ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> We will examine our first imaging modality, EEG (and MEG).<br /> <br /> ::[[media: EEGNeurophys_161102.pdf|Slides for Neurophysiology Lectures]]<br /> <br /> ==''Wednesday 11/2/16''==<br /> === Electrophysiology II ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> Continuing with neurophysiology. ERPs &amp; Source Localization.<br /> <br /> =Week 7 - Neurophysiology =<br /> ==''Monday 11/7/16''==<br /> === Electrophysiology III ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> Continuing with neurophysiology. Connectivity &amp; Oscillations.<br /> <br /> ==''Wednesday 11/9/16''==<br /> ===Intracranial Recordings ''Speaker'': [http://neurosurgery.ucla.edu/neuromodulation-neuroimaging-lab Nanthia Suthana]===<br /> <br /> ::[[media: Suthana.pdf|Slides for Intracranial Lecture]]<br /> ::[[media: LachauxIntracranial.pdf|Lachaux et al Reading]]<br /> ::[[media: HarrisIntracranial.pdf|Harris et al Reading]]<br /> <br /> =Week 8 - Practical Neurophysiology=<br /> ==''Monday 11/14/16''==<br /> ===Building an EEG System - Circuit 1 - Going over circuit ''Speaker'': Cameron Rodriguez===<br /> <br /> :*[https://learn.sparkfun.com/tutorials/how-to-solder Through holesoldering].<br /> :*[https://learn.sparkfun.com/tutorials/pcb-basics Printed Circuit Board Basics].<br /> :*[https://learn.sparkfun.com/tutorials/how-to-install-and-setup-eagle Installing and Setting Up Eagle].<br /> :*[https://learn.sparkfun.com/tutorials/using-eagle-schematic Using Eagle Schematic].<br /> :*[https://learn.sparkfun.com/tutorials/using-eagle-board-layoutc Using Eagle Board Layout].<br /> <br /> :*[https://www.sparkfun.com/videos#pete/NJKZZArjdg8 PCB Layout 1 - According to Pete];<br /> :*[https://www.sparkfun.com/videos#pete/JANZsjRiM3w PCB Layout 2 - According to Pete];<br /> <br /> ==''Wednesday 11/16/16''==<br /> ===Data Processing Hands On ''Speaker'': Cameron Rodriguez===<br /> <br /> =Week 9 - Data Acquisition =<br /> ==''Monday 11/21/16 - in C8-177''==<br /> ===Data Acquisition ''Speaker'': Agatha Lenartowicz===<br /> Please meet in CCN suite.<br /> <br /> ==''Wednesday 11/23/16''==<br /> ===Thankgiving Wed - No Class ===<br /> <br /> =Week 10 - Data Processing =<br /> ==''Monday 11/28/16''==<br /> Please complete class evaluations via myucla! <br /> Also we have an inhouse feedback form regarding the course syllabus: [https://docs.google.com/forms/d/e/1FAIpQLSd8xn340PxZRWb3-GQ__ZisYwFWoSgmYrcwyjvyBY5JV_mxnQ/viewform Feedback Link]<br /> Your feedback is invaluable to us!!<br /> <br /> ===Midterm Review, Data Processing ''Speaker'': Cameron Rodriguez/Agatha Lenartowicz===<br /> Hands on practice with EEG data, in Matlab using EEGLAB. Lab materials distributed by email 11/27/16.<br /> <br /> ==''Wednesady 11/30/16''==<br /> ===Data Processing &quot;Speaker&quot;: Cameron Rodriguez/Agatha Lenartowicz===<br /> Hands on practice with EEG data, in Matlab using EEGLAB.<br /> <br /> =Week 11 - Finals Week=<br /> Final will be distributed 11/2/16.<br /> <br /> ==''Monday 11/5/16''==<br /> ===Final Exam===<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!--<br /> SPARE CONTENT / PREVIOUS YEARS<br /> <br /> =Week 3: Stats begins!=<br /> <br /> ==''Monday 10/12/15''==<br /> <br /> ===- Statistical Fundamentals. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> :*[[media: CohenClassIntroStats11_25_13.pdf‎ | Slides used in class (set 1)]]<br /> <br /> ''Review of'':<br /> :*Descriptive Statistics: mean, mode, variance, standard deviation<br /> :*Statistical Inference. The Binomial and Normal Distribution<br /> :*Basic Tests: t-test, linear correlation<br /> :*Modeling and non-linear relations<br /> :*Bayes rule<br /> <br /> <br /> ==''Wednesday 10/14/15''==<br /> ===- Statistical Fundamentals II. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> =Week 4 - Stats and circuits=<br /> ==''Monday 10/19/15''==<br /> ===- Statistical Fundamentals III. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> ==''Wednesday 10/21/15''==<br /> ===Circuits continued: Active devices. [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ===Optics I. ''Speaker'': [mailto:zdeis@seas.ucla.edu Zachary Taylor]===<br /> [[Image:Reflection.jpg|right]]<br /> The overall goal of this lecture is to establish that:<br /> ''- Physical constants have tangible meanings''<br /> ''- Plane waves form a physically unrealizable but extremely good approximation to real systems''<br /> ''- Boundaries bend light''<br /> ''- Physical constants, plane wave mechanics, and boundaries can be used to describe the operation of a lens''<br /> ''- The PSF gives a good indication of the overall performance of an imaging system''<br /> ''- All of these concepts have analogues in other areas of engineering (ie circuits, mechanical vibrations, etc.)''''<br /> <br /> '''Outline:'''<br /> :* Constitutive parameters (ε, μ, η, n, etc.)<br /> :* Plane wave basics<br /> :* Plane waves at boundaries<br /> :* Lenses<br /> :* Advanced imaging properties of lenses<br /> :* Point spread function.<br /> <br /> ''Required Readings''<br /> Zach has very kindly agreed to post his [http://www.brainmapping.org/NITP/PNA/Readings/OpticsTaylor3-10-10.pdf Optics lecture notes].<br /> ''Suggested Further Reading''<br /> <br /> <br /> <br /> <br /> ==''Wednesday 11/19/14''== <br /> ===Optogenetics. ''Speaker'': [http://faculty.neuroscience.ucla.edu/institution/personnel?personnel_id=47031 Tom Otis]===<br /> <br /> <br /> =Week 8: =<br /> ==''Monday 12/1/14 ''==<br /> ===- Statistics for Imaging. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> [[image:BVTradeoff.jpg|right]]<br /> ''Required Readings''<br /> :*[[media: Mumford_stat_modeling.pdf | Statistical Modeling and Inference (pdf)]]<br /> :*[[media: CohenClassSlides12_2_13.pdf | Slides used in class 12/2/14 and 12/4/14]]<br /> <br /> :*The General Linear Model<br /> *Linear Algebra applied to Statistical Solutions<br /> :*Analysis of Variance<br /> <br /> ==''Wednesday 11/20/14 ...''==<br /> === - Statistics for Imaging II. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> *Fixed and Random Effects<br /> *Repeated measures<br /> :*Bonferroni and Other Corrections<br /> *Non-Parametric Methods<br /> *Autocorrelation<br /> *Unknown Distributions<br /> <br /> ''Required Readings''<br /> :*[[media: CohenClassSlides12_2_13.pdf‎ | same as 12/2/14]]<br /> <br /> ''Suggested Further Reading''<br /> <br /> :*[http://ccn.ucla.edu/media/PNI20121121.mp4 Lecture Video - To be Posted Shortly - Please Right Click and Save rather than Stream - Thank you Alan and Edward!]<br /> <br /> =Week 9: =<br /> ==''Monday 11/25/14''==<br /> === - TBD. [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> ==''Wednesday 11/27/14''==<br /> ===- The Organization of the Human Brain. ''Speaker'': [http://ccn.ucla.edu/bmcweb/bmc_bios/SusanBookheimer/ Susan Bookheimer]===<br /> <br /> We will discuss the general organization of the human brain, and the regional specialization of cortical areas. The emphasis will be on understanding principles of organization:<br /> *Phylogenetic Layering<br /> *Functional Specialization<br /> *Principles Divisions of the Brain<br /> *Brain Systems<br /> <br /> ''Required Readings''<br /> :*[http://da.biostr.washington.edu:80/cgi-bin/DA/PageMaster?atlas:NeuroSyllabus+ffpathIndex/Splash^Page^Syllabus+2 Neuroanatomy Programmed Learning]<br /> :*[[media:Cognitive_science_and_neuro_2012.pdf | Slides shown in Class UPDATED 2012/10/17]]<br /> ''Suggested Further Reading''<br /> <br /> :*[[media:PNIA_2012_PS1_Soln.pdf‎ | Solution to Problem Set 1]]<br /> ===- Human Electrophysiology ''Speakers'': [http://greenlab.npih.ucla.edu/ROSTER.html Agatha Lenartowicz], [http://dgsom.healthsciences.ucla.edu/institution/personnel?personnel_id=9140 John Stern]===<br /> <br /> ''Evoked Responses'' - Guest Lecturer: [http://greenlab.npih.ucla.edu/ROSTER.html Jonathan Wynn]<br /> *A look at real EEG data<br /> *Preprocessing:<br /> **filtering<br /> **artifact detection/removal<br /> *averaging<br /> *single events<br /> *interpretation<br /> <br /> ''Clinical EEG'' - Guest Lecturer: [http://dgsom.healthsciences.ucla.edu/institution/personnel?personnel_id=9140 John Stern]<br /> *Normal and Abnormal EEG<br /> *EEG as a marker for brain state<br /> **sleep staging<br /> **alpha and relaxation<br /> *Neurofeedback??? --&gt;</div>

Alenartowicz https://www.ccn.ucla.edu/wiki/index.php?title=Principles_of_Neuroimaging_A_-_2016&diff=3221 2016-11-29T03:59:40Z

<p>Alenartowicz: /* Week 10 - Data Processing */</p> <hr /> <div>Principles of Neuroimaging A, Fall, 2016 - Class Schedule and Syllabus<br /> <br /> <br /> ==='''Neuroimaging journal Club (''required for NITP certificate'')'''===<br /> Contact: Katherine Lawrence (katherine.E.Lawrence@ucla.edu) or Janelle Liu (janelle.j.liu@ucla.edu), Faculty Sponsor: Jamie Feusner (JFeusner@mednet.ucla.edu)<br /> <br /> ==This schedule ''will'' change!==<br /> <br /> :'''[[Principles_of_Neuroimaging_-_2016-2017 | Back to main course page for Principles of Neuroimaging]]'''<br /> <br /> :'''Coming up next!: [[Principles_of_Neuroimaging_B_-_2017 | M284B Principles of Neuroimaging B]]'''<br /> <br /> =Course Reading=<br /> ===Required Reading===<br /> :'''Signal Processing for Neuroscientists''' by ''Wim van Drongelen''<br /> ::This can be found as a PDF on scribd.com, for a small fee of $8.99<br /> <br /> ===Supplemental Reading===<br /> :'''Matlab for Neuroscientists'''<br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/88212458/Matlab-Matlab-for-Neuroscientists<br /> <br /> :'''Cartoon Guide to Statistics''' <br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/148072668/Cartoon-Guide-to-Statistics<br /> <br /> :'''NOTE:''' if you subscribe for a Scribd account for a day, you can download as many documents as you like for one fee.<br /> <br /> <br /> =Week 1: Orientation to Neuro-imaging, Neurons, Brains=<br /> <br /> ==''Monday 9/26/16'' ==<br /> ===- Images. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284AImages-intro.pdf | Lecture Slides Images]]<br /> <br /> ==''Wednesday 9/28/16''==<br /> ===- Neurons &amp; Signaling. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> In this first class we will review the basics of neurophysiology with an eye towards what signals of brain function might be visible to the neuroimager. We will discuss information coding, energetics, size and time scales.<br /> [[Image:Neurons.jpg|right]]<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:TheActiveBrain.pdf | The Active Brain]]<br /> :*[[media:NeuronFunctionM284-2016_SM.pdf | Neuron function slides shown in class]]<br /> :*[http://www.science.smith.edu/departments/neurosci/courses/bio330/squid.html Videos of giant squid experiments]<br /> ''Suggested Further Reading''<br /> :*[http://ccn.ucla.edu/wiki/images/5/5a/CAVEAT_LECTOR.pdf Caveat Lector - the misuse of neuroimaging]<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Protected/Kosslyn1999.pdf &quot;If Neuroimaging is the Answer, What is the Question?&quot; Kosslyn, 1999]<br /> :*[http://da.biostr.washington.edu:80/cgi-bin/DA/PageMaster?atlas:NeuroSyllabus+ffpathIndex/Splash^Page^Syllabus+2 Neuroanatomy Programmed Learning]<br /> :*[http://www.amazon.com/Fundamental-Neuroscience-Second-Larry-Squire/dp/0126603030 Squire, Fundamentals of Neuroscience]<br /> :*[http://www.amazon.com/Principles-Neural-Science-Eric-Kandel/dp/0838577016 Kandel, et al., &quot;Principles of Neural Science&quot;]<br /> :This paper, by Malhi, is a nice orientation in methods of neuroimaging. *[http://www.ccn.ucla.edu/wiki/images/f/f2/Malhi2007.pdf Making sense of neuroimaging in psychiatry]<br /> :*[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1359308/pdf/jphysiol01232-0142.pdf Replacement of the axoplasm of giant nerve fibres with artificial solutions]<br /> <br /> =Week 2: Linear Systems, Convolution, Fourier Transforms=<br /> ==''Wednesday 10/3/16''==<br /> <br /> ===- Linear Systems I. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ==''Wednesday 10/5/16''==<br /> ===- Linear Systems II. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> Why the emphasis on Linear Systems? Because they are actually ''easy'' (as compared to non-linear systems, which are not.) As we go through this course, we will see many ways in which linear systems theory is applied to:<br /> :Modeling of Neural Systems<br /> :Extraction of Signal from Noise<br /> :Design of Circuits<br /> :Image Enhancement<br /> :Understanding of Image artifacts, and others.<br /> <br /> Linear systems analysis is one of the great technologies of the 20th and 21st century. It is now the basis for virtually all electronics design, and its extension into the discrete (digital) domain is the basis for most of modern signal processing. <br /> <br /> In our specific case, we will use these few basic principles of linear systems to understand both the instruments we use and the neuroimaging signals we collect. When you have mastered this material, you should be in a much better position to model the systems that you study in order to develop an approach to studying them.<br /> <br /> Here is [http://www.brainmapping.org/NITP/PNA/Readings/ImaginaryNumbers.pdf A primer I wrote on imaginary numbers] that might be a helpful review.<br /> <br /> There is a nice [http://en.wikibooks.org/wiki/Calculus Wikibook on Calculus].<br /> <br /> ''Required Readings''<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapter 1<br /> :*[[media:MathematicalTools2016.pdf | Mathematical Tools]]<br /> <br /> ''Suggested Further Reading''<br /> :'''Introduction to matlab'''<br /> <br /> ''Slides shown in class''<br /> :[[media:LinearityM284A-2016.pdf | Linearity and the Fourier Transform]]<br /> <br /> Please see [http://www.brainmapping.org/NITP/PNA/html/Linearity.html MATLAB linearity demo]<br /> <br /> If you are the type who sees beauty in mathematics, the Euler identity may be one of the most beautiful pieces of math in the world.<br /> <br /> =Week 3: Math &amp; Circuits I=<br /> <br /> ==''Monday 10/10/16''==<br /> === - Circuits I. Speaker: Cameron Rodriguez ===<br /> :*[[media:Circuits_2016.pdf | Circuit slides shown in class]]<br /> <br /> ==''Wednesday 10/12/16''==<br /> === - Circuits II. ''Speaker'': Cameron Rodriguez ===<br /> <br /> Why circuits?<br /> :(Virtually) Every device you use in your research is electronic. You access your primary data only indirectly<br /> :The device you ''really'' want in your lab doesn't exist. You very well may have to make it.<br /> :There are electronic analogs to most of the linear systems that you have so far studied (and ''vice versa'' - the tools you now understand can be used to analyze and predict circuit behavior).<br /> <br /> :If you have not had any of this background, you might want to have a look at this handout, [[Media:Electricity.pdf|Electrical Circuits]], in advance. There are near infinite numbers of resources on the web that cover similar material (near enough to infinite that by the time you read all of them, there would be a whole new set.) I have recently come across a link to [http://www.allaboutcircuits.com/ Online Books: All About Circuits] ''IF'' you want practical hands-on knowledge about this material, my all-time favorite text is [http://www.google.com/products/catalog?hl=en&amp;client=safari&amp;rls=en-us&amp;ei=uVSPSfaxE5nMsAPf-tmSCQ&amp;resnum=1&amp;q=art+of+electronics&amp;um=1&amp;ie=UTF-8&amp;cid=8820839049329255765#ps-sellers &quot;Horowitz and Hill: ''The Art of Electronics.''&quot;] The latest edition, however, is dated 1989 and a new third edition is promised. I have therefore stopped short of recommending a purchase unless your need to make circuits is immediate. In this book, you will find an excellent education on the fundamental principles of electrical circuits and an incredible compendium of practical data, such as how to assemble circuit boards, how to make measurements, etc...) <br /> <br /> Readings:<br /> :*[http://www.ti.com/lit/an/sloa093/sloa093.pdf Filter Design in 30 Seconds]<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapter 2 and 10<br /> :*[https://www.circuitlab.com/ Circuit Lab ] A Free Circuit Web Base Simulator<br /> **You may or may not find this comprehensible without chapters 5 through 9.<br /> <br /> We will discuss:<br /> :*Passive Circuit Elements: Resistors, Capacitors, Inductors<br /> :*Gain<br /> :*Transformers<br /> :*Rectifiers<br /> :*Active Elements<br /> ::- ''Amplifiers''<br /> ::- ''Transistors''<br /> ::- ''Op Amps''<br /> :*Solutions with Matrices<br /> ''Suggested Further Reading''<br /> :*[http://video.google.com/videoplay?docid=5645396659673218353&amp;q=Physics+for+Future+Presidents+Electricity&amp;total=5&amp;start=0&amp;num=10&amp;so=0&amp;type=search&amp;plindex=0#0h20m30s Video intro lecture on charge, current and voltage].<br /> :*[[media:Electricity_Basics.pdf‎ | Well organized text on electrical concepts by Tony R. Kuphaldt]]<br /> :*[http://en.wikibooks.org/wiki/Circuit_Theory Circuit Theory - Wikibook]<br /> <br /> '''Suggested, Optional Readings from [http://www.dspguide.com DSPguide.com]:'''<br /> :*[http://www.dspguide.com/CH5.PDF Linear Systems]<br /> :*[http://www.dspguide.com/CH6.PDF Convolution]<br /> :*[http://www.dspguide.com/CH8.PDF Discrete Fourier Transform (DFT)]<br /> :''Note: These chapters are light on math and try to focus on a conceptual understanding''<br /> <br /> Time and Frequency / Spectral Filters<br /> <br /> ----<br /> Practice using the Fourier transform:<br /> :[http://www.brainmapping.org/NITP/PNA/ConvFThtml/ConvolutionWorksheet.pdf Fourier transform and Convolution Worksheet]. &lt;!-- [http://www.brainmapping.org/NITP/PNA/ConvFThtml/ConvFT.html (''Solutions'').] --&gt;<br /> :[http://www.brainmapping.org/NITP/PNA/ConvFThtml/Something.wav Sound file for worksheet above.]<br /> <br /> Other<br /> :*[http://www.youtube.com/watch?feature=player_embedded&amp;v=7uHfjpU3OH0#! Getting Started with Arduino]<br /> <br /> =Week 4 Signal Processing=<br /> <br /> ==''Monday 10/17/16''==<br /> === - Signal Processing. ''Speaker'': Cameron Rodriguez ===<br /> <br /> :*[[media:Signals1-PreProcessing1_2016.pdf | Signal Pre Processing Slide Set 1 shown in class]]<br /> <br /> <br /> ==''Wednesday 10/19/16''==<br /> === - Noise ''Speaker'': Cameron Rodriguez ===<br /> <br /> :*[[media:OneD_signal_Filter_Ex.pdf | Matlab Signal Tutorial printout]]<br /> <br /> It is what you ''don't'' want - usually - but things change in quantized systems<br /> :Additive noise<br /> :White Noise<br /> :Boltzmann noise<br /> :Colored Noise<br /> :Gaussian Noise<br /> :Coherent noise<br /> :Sampling Errors<br /> :Aliasing<br /> :Quantization noise<br /> :Spectral filtering<br /> <br /> Noise comes in all shapes and colors. It is present in every measurement we make, from an EEG voltage to an estimate of the effects of dopamine on forebrain signal. Our best weapons are an understanding of the statistical properties of noise, the sources of noise and the ways to control it. Noise in the discrete digital domain is special, as it is both ''created'' by digitization and amplified by sampling.<br /> <br /> Readings:<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapters 2 through 4<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Noise.pdf Noise Slides]<br /> <br /> =Week 5 - Information &amp; Statistical Theory =<br /> <br /> ==''Monday 10/24/16 - in C8-177''==<br /> === Noise Cont' &amp; Information Theory Speakers: Cameron Rodriguez &amp; John Villasenor===<br /> <br /> ==''Wednesday 10/26/16''==<br /> ===Statistical Theory ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> We will consider the general problems of statistical inference, with a concentration on developing an intuitive understanding of statistical concepts.<br /> [[Image:MeasureForMeasure.jpg|right]]<br /> <br /> Suggested reading<br /> :*[http://www.statsoft.com/textbook/stbasic.html Statsoft online text (''free'')]<br /> :*[http://www.amazon.com/Cartoon-Guide-Statistics-Larry-Gonick/dp/0062731025 The Cartoon Guide to Statistics - Gonick $17.95 new]<br /> :The latter teaches stats at what I feel to be the right level - developing intuitions about the kinds of questions that can be answered using stats and about the statistical tests and measures<br /> <br /> :'''Problem Set 3 - Statistics in matlab'''<br /> ::[[media: Problem_Set_1.doc|Problem set using stats and MATLAB]]<br /> ::[[media: Problem_Set_1B.doc|More practice with stats and MATLAB]]<br /> <br /> <br /> =MIDTERM=<br /> <br /> <br /> =Week 6 - Neurophysiology=<br /> ==''Monday 10/31/16''==<br /> === Electrophysiology I ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> We will examine our first imaging modality, EEG (and MEG).<br /> <br /> ::[[media: EEGNeurophys_161102.pdf|Slides for Neurophysiology Lectures]]<br /> <br /> ==''Wednesday 11/2/16''==<br /> === Electrophysiology II ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> Continuing with neurophysiology. ERPs &amp; Source Localization.<br /> <br /> =Week 7 - Neurophysiology =<br /> ==''Monday 11/7/16''==<br /> === Electrophysiology III ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> Continuing with neurophysiology. Connectivity &amp; Oscillations.<br /> <br /> ==''Wednesday 11/9/16''==<br /> ===Intracranial Recordings ''Speaker'': [http://neurosurgery.ucla.edu/neuromodulation-neuroimaging-lab Nanthia Suthana]===<br /> <br /> ::[[media: Suthana.pdf|Slides for Intracranial Lecture]]<br /> ::[[media: LachauxIntracranial.pdf|Lachaux et al Reading]]<br /> ::[[media: HarrisIntracranial.pdf|Harris et al Reading]]<br /> <br /> =Week 8 - Practical Neurophysiology=<br /> ==''Monday 11/14/16''==<br /> ===Building an EEG System - Circuit 1 - Going over circuit ''Speaker'': Cameron Rodriguez===<br /> <br /> :*[https://learn.sparkfun.com/tutorials/how-to-solder Through holesoldering].<br /> :*[https://learn.sparkfun.com/tutorials/pcb-basics Printed Circuit Board Basics].<br /> :*[https://learn.sparkfun.com/tutorials/how-to-install-and-setup-eagle Installing and Setting Up Eagle].<br /> :*[https://learn.sparkfun.com/tutorials/using-eagle-schematic Using Eagle Schematic].<br /> :*[https://learn.sparkfun.com/tutorials/using-eagle-board-layoutc Using Eagle Board Layout].<br /> <br /> :*[https://www.sparkfun.com/videos#pete/NJKZZArjdg8 PCB Layout 1 - According to Pete];<br /> :*[https://www.sparkfun.com/videos#pete/JANZsjRiM3w PCB Layout 2 - According to Pete];<br /> <br /> ==''Wednesday 11/16/16''==<br /> ===Data Processing Hands On ''Speaker'': Cameron Rodriguez===<br /> <br /> =Week 9 - Data Acquisition =<br /> ==''Monday 11/21/16 - in C8-177''==<br /> ===Data Acquisition ''Speaker'': Agatha Lenartowicz===<br /> Please meet in CCN suite.<br /> <br /> ==''Wednesday 11/23/16''==<br /> ===Thankgiving Wed - No Class ===<br /> <br /> =Week 10 - Data Processing =<br /> ==''Monday 11/28/16''==<br /> Please complete class evaluations via myucla! <br /> Also we have an inhouse feedback form regarding the course syllabus: https://docs.google.com/forms/d/e/1FAIpQLSd8xn340PxZRWb3-GQ__ZisYwFWoSgmYrcwyjvyBY5JV_mxnQ/viewform<br /> Your feedback is invaluable to us!!<br /> <br /> ===Midterm Review, Data Processing ''Speaker'': Cameron Rodriguez/Agatha Lenartowicz===<br /> Hands on practice with EEG data, in Matlab using EEGLAB. Lab materials distributed by email 11/27/16.<br /> <br /> ==''Wednesady 11/30/16''==<br /> ===Data Processing &quot;Speaker&quot;: Cameron Rodriguez/Agatha Lenartowicz===<br /> Hands on practice with EEG data, in Matlab using EEGLAB.<br /> <br /> =Week 11 - Finals Week=<br /> Final will be distributed 11/2/16.<br /> <br /> ==''Monday 11/5/16''==<br /> ===Final Exam===<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!--<br /> SPARE CONTENT / PREVIOUS YEARS<br /> <br /> =Week 3: Stats begins!=<br /> <br /> ==''Monday 10/12/15''==<br /> <br /> ===- Statistical Fundamentals. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> :*[[media: CohenClassIntroStats11_25_13.pdf‎ | Slides used in class (set 1)]]<br /> <br /> ''Review of'':<br /> :*Descriptive Statistics: mean, mode, variance, standard deviation<br /> :*Statistical Inference. The Binomial and Normal Distribution<br /> :*Basic Tests: t-test, linear correlation<br /> :*Modeling and non-linear relations<br /> :*Bayes rule<br /> <br /> <br /> ==''Wednesday 10/14/15''==<br /> ===- Statistical Fundamentals II. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> =Week 4 - Stats and circuits=<br /> ==''Monday 10/19/15''==<br /> ===- Statistical Fundamentals III. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> ==''Wednesday 10/21/15''==<br /> ===Circuits continued: Active devices. [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ===Optics I. ''Speaker'': [mailto:zdeis@seas.ucla.edu Zachary Taylor]===<br /> [[Image:Reflection.jpg|right]]<br /> The overall goal of this lecture is to establish that:<br /> ''- Physical constants have tangible meanings''<br /> ''- Plane waves form a physically unrealizable but extremely good approximation to real systems''<br /> ''- Boundaries bend light''<br /> ''- Physical constants, plane wave mechanics, and boundaries can be used to describe the operation of a lens''<br /> ''- The PSF gives a good indication of the overall performance of an imaging system''<br /> ''- All of these concepts have analogues in other areas of engineering (ie circuits, mechanical vibrations, etc.)''''<br /> <br /> '''Outline:'''<br /> :* Constitutive parameters (ε, μ, η, n, etc.)<br /> :* Plane wave basics<br /> :* Plane waves at boundaries<br /> :* Lenses<br /> :* Advanced imaging properties of lenses<br /> :* Point spread function.<br /> <br /> ''Required Readings''<br /> Zach has very kindly agreed to post his [http://www.brainmapping.org/NITP/PNA/Readings/OpticsTaylor3-10-10.pdf Optics lecture notes].<br /> ''Suggested Further Reading''<br /> <br /> <br /> <br /> <br /> ==''Wednesday 11/19/14''== <br /> ===Optogenetics. ''Speaker'': [http://faculty.neuroscience.ucla.edu/institution/personnel?personnel_id=47031 Tom Otis]===<br /> <br /> <br /> =Week 8: =<br /> ==''Monday 12/1/14 ''==<br /> ===- Statistics for Imaging. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> [[image:BVTradeoff.jpg|right]]<br /> ''Required Readings''<br /> :*[[media: Mumford_stat_modeling.pdf | Statistical Modeling and Inference (pdf)]]<br /> :*[[media: CohenClassSlides12_2_13.pdf | Slides used in class 12/2/14 and 12/4/14]]<br /> <br /> :*The General Linear Model<br /> *Linear Algebra applied to Statistical Solutions<br /> :*Analysis of Variance<br /> <br /> ==''Wednesday 11/20/14 ...''==<br /> === - Statistics for Imaging II. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> *Fixed and Random Effects<br /> *Repeated measures<br /> :*Bonferroni and Other Corrections<br /> *Non-Parametric Methods<br /> *Autocorrelation<br /> *Unknown Distributions<br /> <br /> ''Required Readings''<br /> :*[[media: CohenClassSlides12_2_13.pdf‎ | same as 12/2/14]]<br /> <br /> ''Suggested Further Reading''<br /> <br /> :*[http://ccn.ucla.edu/media/PNI20121121.mp4 Lecture Video - To be Posted Shortly - Please Right Click and Save rather than Stream - Thank you Alan and Edward!]<br /> <br /> =Week 9: =<br /> ==''Monday 11/25/14''==<br /> === - TBD. [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> ==''Wednesday 11/27/14''==<br /> ===- The Organization of the Human Brain. ''Speaker'': [http://ccn.ucla.edu/bmcweb/bmc_bios/SusanBookheimer/ Susan Bookheimer]===<br /> <br /> We will discuss the general organization of the human brain, and the regional specialization of cortical areas. The emphasis will be on understanding principles of organization:<br /> *Phylogenetic Layering<br /> *Functional Specialization<br /> *Principles Divisions of the Brain<br /> *Brain Systems<br /> <br /> ''Required Readings''<br /> :*[http://da.biostr.washington.edu:80/cgi-bin/DA/PageMaster?atlas:NeuroSyllabus+ffpathIndex/Splash^Page^Syllabus+2 Neuroanatomy Programmed Learning]<br /> :*[[media:Cognitive_science_and_neuro_2012.pdf | Slides shown in Class UPDATED 2012/10/17]]<br /> ''Suggested Further Reading''<br /> <br /> :*[[media:PNIA_2012_PS1_Soln.pdf‎ | Solution to Problem Set 1]]<br /> ===- Human Electrophysiology ''Speakers'': [http://greenlab.npih.ucla.edu/ROSTER.html Agatha Lenartowicz], [http://dgsom.healthsciences.ucla.edu/institution/personnel?personnel_id=9140 John Stern]===<br /> <br /> ''Evoked Responses'' - Guest Lecturer: [http://greenlab.npih.ucla.edu/ROSTER.html Jonathan Wynn]<br /> *A look at real EEG data<br /> *Preprocessing:<br /> **filtering<br /> **artifact detection/removal<br /> *averaging<br /> *single events<br /> *interpretation<br /> <br /> ''Clinical EEG'' - Guest Lecturer: [http://dgsom.healthsciences.ucla.edu/institution/personnel?personnel_id=9140 John Stern]<br /> *Normal and Abnormal EEG<br /> *EEG as a marker for brain state<br /> **sleep staging<br /> **alpha and relaxation<br /> *Neurofeedback??? --&gt;</div>

Alenartowicz https://www.ccn.ucla.edu/wiki/index.php?title=Principles_of_Neuroimaging_A_-_2016&diff=3220 2016-11-29T03:56:52Z

<p>Alenartowicz: </p> <hr /> <div>Principles of Neuroimaging A, Fall, 2016 - Class Schedule and Syllabus<br /> <br /> <br /> ==='''Neuroimaging journal Club (''required for NITP certificate'')'''===<br /> Contact: Katherine Lawrence (katherine.E.Lawrence@ucla.edu) or Janelle Liu (janelle.j.liu@ucla.edu), Faculty Sponsor: Jamie Feusner (JFeusner@mednet.ucla.edu)<br /> <br /> ==This schedule ''will'' change!==<br /> <br /> :'''[[Principles_of_Neuroimaging_-_2016-2017 | Back to main course page for Principles of Neuroimaging]]'''<br /> <br /> :'''Coming up next!: [[Principles_of_Neuroimaging_B_-_2017 | M284B Principles of Neuroimaging B]]'''<br /> <br /> =Course Reading=<br /> ===Required Reading===<br /> :'''Signal Processing for Neuroscientists''' by ''Wim van Drongelen''<br /> ::This can be found as a PDF on scribd.com, for a small fee of $8.99<br /> <br /> ===Supplemental Reading===<br /> :'''Matlab for Neuroscientists'''<br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/88212458/Matlab-Matlab-for-Neuroscientists<br /> <br /> :'''Cartoon Guide to Statistics''' <br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/148072668/Cartoon-Guide-to-Statistics<br /> <br /> :'''NOTE:''' if you subscribe for a Scribd account for a day, you can download as many documents as you like for one fee.<br /> <br /> <br /> =Week 1: Orientation to Neuro-imaging, Neurons, Brains=<br /> <br /> ==''Monday 9/26/16'' ==<br /> ===- Images. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284AImages-intro.pdf | Lecture Slides Images]]<br /> <br /> ==''Wednesday 9/28/16''==<br /> ===- Neurons &amp; Signaling. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> In this first class we will review the basics of neurophysiology with an eye towards what signals of brain function might be visible to the neuroimager. We will discuss information coding, energetics, size and time scales.<br /> [[Image:Neurons.jpg|right]]<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:TheActiveBrain.pdf | The Active Brain]]<br /> :*[[media:NeuronFunctionM284-2016_SM.pdf | Neuron function slides shown in class]]<br /> :*[http://www.science.smith.edu/departments/neurosci/courses/bio330/squid.html Videos of giant squid experiments]<br /> ''Suggested Further Reading''<br /> :*[http://ccn.ucla.edu/wiki/images/5/5a/CAVEAT_LECTOR.pdf Caveat Lector - the misuse of neuroimaging]<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Protected/Kosslyn1999.pdf &quot;If Neuroimaging is the Answer, What is the Question?&quot; Kosslyn, 1999]<br /> :*[http://da.biostr.washington.edu:80/cgi-bin/DA/PageMaster?atlas:NeuroSyllabus+ffpathIndex/Splash^Page^Syllabus+2 Neuroanatomy Programmed Learning]<br /> :*[http://www.amazon.com/Fundamental-Neuroscience-Second-Larry-Squire/dp/0126603030 Squire, Fundamentals of Neuroscience]<br /> :*[http://www.amazon.com/Principles-Neural-Science-Eric-Kandel/dp/0838577016 Kandel, et al., &quot;Principles of Neural Science&quot;]<br /> :This paper, by Malhi, is a nice orientation in methods of neuroimaging. *[http://www.ccn.ucla.edu/wiki/images/f/f2/Malhi2007.pdf Making sense of neuroimaging in psychiatry]<br /> :*[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1359308/pdf/jphysiol01232-0142.pdf Replacement of the axoplasm of giant nerve fibres with artificial solutions]<br /> <br /> =Week 2: Linear Systems, Convolution, Fourier Transforms=<br /> ==''Wednesday 10/3/16''==<br /> <br /> ===- Linear Systems I. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ==''Wednesday 10/5/16''==<br /> ===- Linear Systems II. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> Why the emphasis on Linear Systems? Because they are actually ''easy'' (as compared to non-linear systems, which are not.) As we go through this course, we will see many ways in which linear systems theory is applied to:<br /> :Modeling of Neural Systems<br /> :Extraction of Signal from Noise<br /> :Design of Circuits<br /> :Image Enhancement<br /> :Understanding of Image artifacts, and others.<br /> <br /> Linear systems analysis is one of the great technologies of the 20th and 21st century. It is now the basis for virtually all electronics design, and its extension into the discrete (digital) domain is the basis for most of modern signal processing. <br /> <br /> In our specific case, we will use these few basic principles of linear systems to understand both the instruments we use and the neuroimaging signals we collect. When you have mastered this material, you should be in a much better position to model the systems that you study in order to develop an approach to studying them.<br /> <br /> Here is [http://www.brainmapping.org/NITP/PNA/Readings/ImaginaryNumbers.pdf A primer I wrote on imaginary numbers] that might be a helpful review.<br /> <br /> There is a nice [http://en.wikibooks.org/wiki/Calculus Wikibook on Calculus].<br /> <br /> ''Required Readings''<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapter 1<br /> :*[[media:MathematicalTools2016.pdf | Mathematical Tools]]<br /> <br /> ''Suggested Further Reading''<br /> :'''Introduction to matlab'''<br /> <br /> ''Slides shown in class''<br /> :[[media:LinearityM284A-2016.pdf | Linearity and the Fourier Transform]]<br /> <br /> Please see [http://www.brainmapping.org/NITP/PNA/html/Linearity.html MATLAB linearity demo]<br /> <br /> If you are the type who sees beauty in mathematics, the Euler identity may be one of the most beautiful pieces of math in the world.<br /> <br /> =Week 3: Math &amp; Circuits I=<br /> <br /> ==''Monday 10/10/16''==<br /> === - Circuits I. Speaker: Cameron Rodriguez ===<br /> :*[[media:Circuits_2016.pdf | Circuit slides shown in class]]<br /> <br /> ==''Wednesday 10/12/16''==<br /> === - Circuits II. ''Speaker'': Cameron Rodriguez ===<br /> <br /> Why circuits?<br /> :(Virtually) Every device you use in your research is electronic. You access your primary data only indirectly<br /> :The device you ''really'' want in your lab doesn't exist. You very well may have to make it.<br /> :There are electronic analogs to most of the linear systems that you have so far studied (and ''vice versa'' - the tools you now understand can be used to analyze and predict circuit behavior).<br /> <br /> :If you have not had any of this background, you might want to have a look at this handout, [[Media:Electricity.pdf|Electrical Circuits]], in advance. There are near infinite numbers of resources on the web that cover similar material (near enough to infinite that by the time you read all of them, there would be a whole new set.) I have recently come across a link to [http://www.allaboutcircuits.com/ Online Books: All About Circuits] ''IF'' you want practical hands-on knowledge about this material, my all-time favorite text is [http://www.google.com/products/catalog?hl=en&amp;client=safari&amp;rls=en-us&amp;ei=uVSPSfaxE5nMsAPf-tmSCQ&amp;resnum=1&amp;q=art+of+electronics&amp;um=1&amp;ie=UTF-8&amp;cid=8820839049329255765#ps-sellers &quot;Horowitz and Hill: ''The Art of Electronics.''&quot;] The latest edition, however, is dated 1989 and a new third edition is promised. I have therefore stopped short of recommending a purchase unless your need to make circuits is immediate. In this book, you will find an excellent education on the fundamental principles of electrical circuits and an incredible compendium of practical data, such as how to assemble circuit boards, how to make measurements, etc...) <br /> <br /> Readings:<br /> :*[http://www.ti.com/lit/an/sloa093/sloa093.pdf Filter Design in 30 Seconds]<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapter 2 and 10<br /> :*[https://www.circuitlab.com/ Circuit Lab ] A Free Circuit Web Base Simulator<br /> **You may or may not find this comprehensible without chapters 5 through 9.<br /> <br /> We will discuss:<br /> :*Passive Circuit Elements: Resistors, Capacitors, Inductors<br /> :*Gain<br /> :*Transformers<br /> :*Rectifiers<br /> :*Active Elements<br /> ::- ''Amplifiers''<br /> ::- ''Transistors''<br /> ::- ''Op Amps''<br /> :*Solutions with Matrices<br /> ''Suggested Further Reading''<br /> :*[http://video.google.com/videoplay?docid=5645396659673218353&amp;q=Physics+for+Future+Presidents+Electricity&amp;total=5&amp;start=0&amp;num=10&amp;so=0&amp;type=search&amp;plindex=0#0h20m30s Video intro lecture on charge, current and voltage].<br /> :*[[media:Electricity_Basics.pdf‎ | Well organized text on electrical concepts by Tony R. Kuphaldt]]<br /> :*[http://en.wikibooks.org/wiki/Circuit_Theory Circuit Theory - Wikibook]<br /> <br /> '''Suggested, Optional Readings from [http://www.dspguide.com DSPguide.com]:'''<br /> :*[http://www.dspguide.com/CH5.PDF Linear Systems]<br /> :*[http://www.dspguide.com/CH6.PDF Convolution]<br /> :*[http://www.dspguide.com/CH8.PDF Discrete Fourier Transform (DFT)]<br /> :''Note: These chapters are light on math and try to focus on a conceptual understanding''<br /> <br /> Time and Frequency / Spectral Filters<br /> <br /> ----<br /> Practice using the Fourier transform:<br /> :[http://www.brainmapping.org/NITP/PNA/ConvFThtml/ConvolutionWorksheet.pdf Fourier transform and Convolution Worksheet]. &lt;!-- [http://www.brainmapping.org/NITP/PNA/ConvFThtml/ConvFT.html (''Solutions'').] --&gt;<br /> :[http://www.brainmapping.org/NITP/PNA/ConvFThtml/Something.wav Sound file for worksheet above.]<br /> <br /> Other<br /> :*[http://www.youtube.com/watch?feature=player_embedded&amp;v=7uHfjpU3OH0#! Getting Started with Arduino]<br /> <br /> =Week 4 Signal Processing=<br /> <br /> ==''Monday 10/17/16''==<br /> === - Signal Processing. ''Speaker'': Cameron Rodriguez ===<br /> <br /> :*[[media:Signals1-PreProcessing1_2016.pdf | Signal Pre Processing Slide Set 1 shown in class]]<br /> <br /> <br /> ==''Wednesday 10/19/16''==<br /> === - Noise ''Speaker'': Cameron Rodriguez ===<br /> <br /> :*[[media:OneD_signal_Filter_Ex.pdf | Matlab Signal Tutorial printout]]<br /> <br /> It is what you ''don't'' want - usually - but things change in quantized systems<br /> :Additive noise<br /> :White Noise<br /> :Boltzmann noise<br /> :Colored Noise<br /> :Gaussian Noise<br /> :Coherent noise<br /> :Sampling Errors<br /> :Aliasing<br /> :Quantization noise<br /> :Spectral filtering<br /> <br /> Noise comes in all shapes and colors. It is present in every measurement we make, from an EEG voltage to an estimate of the effects of dopamine on forebrain signal. Our best weapons are an understanding of the statistical properties of noise, the sources of noise and the ways to control it. Noise in the discrete digital domain is special, as it is both ''created'' by digitization and amplified by sampling.<br /> <br /> Readings:<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapters 2 through 4<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Noise.pdf Noise Slides]<br /> <br /> =Week 5 - Information &amp; Statistical Theory =<br /> <br /> ==''Monday 10/24/16 - in C8-177''==<br /> === Noise Cont' &amp; Information Theory Speakers: Cameron Rodriguez &amp; John Villasenor===<br /> <br /> ==''Wednesday 10/26/16''==<br /> ===Statistical Theory ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> We will consider the general problems of statistical inference, with a concentration on developing an intuitive understanding of statistical concepts.<br /> [[Image:MeasureForMeasure.jpg|right]]<br /> <br /> Suggested reading<br /> :*[http://www.statsoft.com/textbook/stbasic.html Statsoft online text (''free'')]<br /> :*[http://www.amazon.com/Cartoon-Guide-Statistics-Larry-Gonick/dp/0062731025 The Cartoon Guide to Statistics - Gonick $17.95 new]<br /> :The latter teaches stats at what I feel to be the right level - developing intuitions about the kinds of questions that can be answered using stats and about the statistical tests and measures<br /> <br /> :'''Problem Set 3 - Statistics in matlab'''<br /> ::[[media: Problem_Set_1.doc|Problem set using stats and MATLAB]]<br /> ::[[media: Problem_Set_1B.doc|More practice with stats and MATLAB]]<br /> <br /> <br /> =MIDTERM=<br /> <br /> <br /> =Week 6 - Neurophysiology=<br /> ==''Monday 10/31/16''==<br /> === Electrophysiology I ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> We will examine our first imaging modality, EEG (and MEG).<br /> <br /> ::[[media: EEGNeurophys_161102.pdf|Slides for Neurophysiology Lectures]]<br /> <br /> ==''Wednesday 11/2/16''==<br /> === Electrophysiology II ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> Continuing with neurophysiology. ERPs &amp; Source Localization.<br /> <br /> =Week 7 - Neurophysiology =<br /> ==''Monday 11/7/16''==<br /> === Electrophysiology III ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> Continuing with neurophysiology. Connectivity &amp; Oscillations.<br /> <br /> ==''Wednesday 11/9/16''==<br /> ===Intracranial Recordings ''Speaker'': [http://neurosurgery.ucla.edu/neuromodulation-neuroimaging-lab Nanthia Suthana]===<br /> <br /> ::[[media: Suthana.pdf|Slides for Intracranial Lecture]]<br /> ::[[media: LachauxIntracranial.pdf|Lachaux et al Reading]]<br /> ::[[media: HarrisIntracranial.pdf|Harris et al Reading]]<br /> <br /> =Week 8 - Practical Neurophysiology=<br /> ==''Monday 11/14/16''==<br /> ===Building an EEG System - Circuit 1 - Going over circuit ''Speaker'': Cameron Rodriguez===<br /> <br /> :*[https://learn.sparkfun.com/tutorials/how-to-solder Through holesoldering].<br /> :*[https://learn.sparkfun.com/tutorials/pcb-basics Printed Circuit Board Basics].<br /> :*[https://learn.sparkfun.com/tutorials/how-to-install-and-setup-eagle Installing and Setting Up Eagle].<br /> :*[https://learn.sparkfun.com/tutorials/using-eagle-schematic Using Eagle Schematic].<br /> :*[https://learn.sparkfun.com/tutorials/using-eagle-board-layoutc Using Eagle Board Layout].<br /> <br /> :*[https://www.sparkfun.com/videos#pete/NJKZZArjdg8 PCB Layout 1 - According to Pete];<br /> :*[https://www.sparkfun.com/videos#pete/JANZsjRiM3w PCB Layout 2 - According to Pete];<br /> <br /> ==''Wednesday 11/16/16''==<br /> ===Data Processing Hands On ''Speaker'': Cameron Rodriguez===<br /> <br /> =Week 9 - Data Acquisition =<br /> ==''Monday 11/21/16 - in C8-177''==<br /> ===Data Acquisition ''Speaker'': Agatha Lenartowicz===<br /> Please meet in CCN suite.<br /> <br /> ==''Wednesday 11/23/16''==<br /> ===Thankgiving Wed - No Class ===<br /> <br /> =Week 10 - Data Processing =<br /> ==''Monday 11/28/16''==<br /> ===Midterm Review, Data Processing ''Speaker'': Cameron Rodriguez/Agatha Lenartowicz===<br /> Hands on practice with EEG data, in Matlab using EEGLAB. Lab materials distributed by email 11/27/16.<br /> <br /> ==''Wednesady 11/30/16''==<br /> ===Data Processing &quot;Speaker&quot;: Cameron Rodriguez/Agatha Lenartowicz===<br /> Hands on practice with EEG data, in Matlab using EEGLAB.<br /> <br /> <br /> =Week 11 - Finals Week=<br /> Final will be distributed 11/2/16.<br /> <br /> ==''Monday 11/5/16''==<br /> ===Final Exam===<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!--<br /> SPARE CONTENT / PREVIOUS YEARS<br /> <br /> =Week 3: Stats begins!=<br /> <br /> ==''Monday 10/12/15''==<br /> <br /> ===- Statistical Fundamentals. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> :*[[media: CohenClassIntroStats11_25_13.pdf‎ | Slides used in class (set 1)]]<br /> <br /> ''Review of'':<br /> :*Descriptive Statistics: mean, mode, variance, standard deviation<br /> :*Statistical Inference. The Binomial and Normal Distribution<br /> :*Basic Tests: t-test, linear correlation<br /> :*Modeling and non-linear relations<br /> :*Bayes rule<br /> <br /> <br /> ==''Wednesday 10/14/15''==<br /> ===- Statistical Fundamentals II. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> =Week 4 - Stats and circuits=<br /> ==''Monday 10/19/15''==<br /> ===- Statistical Fundamentals III. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> ==''Wednesday 10/21/15''==<br /> ===Circuits continued: Active devices. [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ===Optics I. ''Speaker'': [mailto:zdeis@seas.ucla.edu Zachary Taylor]===<br /> [[Image:Reflection.jpg|right]]<br /> The overall goal of this lecture is to establish that:<br /> ''- Physical constants have tangible meanings''<br /> ''- Plane waves form a physically unrealizable but extremely good approximation to real systems''<br /> ''- Boundaries bend light''<br /> ''- Physical constants, plane wave mechanics, and boundaries can be used to describe the operation of a lens''<br /> ''- The PSF gives a good indication of the overall performance of an imaging system''<br /> ''- All of these concepts have analogues in other areas of engineering (ie circuits, mechanical vibrations, etc.)''''<br /> <br /> '''Outline:'''<br /> :* Constitutive parameters (ε, μ, η, n, etc.)<br /> :* Plane wave basics<br /> :* Plane waves at boundaries<br /> :* Lenses<br /> :* Advanced imaging properties of lenses<br /> :* Point spread function.<br /> <br /> ''Required Readings''<br /> Zach has very kindly agreed to post his [http://www.brainmapping.org/NITP/PNA/Readings/OpticsTaylor3-10-10.pdf Optics lecture notes].<br /> ''Suggested Further Reading''<br /> <br /> <br /> <br /> <br /> ==''Wednesday 11/19/14''== <br /> ===Optogenetics. ''Speaker'': [http://faculty.neuroscience.ucla.edu/institution/personnel?personnel_id=47031 Tom Otis]===<br /> <br /> <br /> =Week 8: =<br /> ==''Monday 12/1/14 ''==<br /> ===- Statistics for Imaging. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> [[image:BVTradeoff.jpg|right]]<br /> ''Required Readings''<br /> :*[[media: Mumford_stat_modeling.pdf | Statistical Modeling and Inference (pdf)]]<br /> :*[[media: CohenClassSlides12_2_13.pdf | Slides used in class 12/2/14 and 12/4/14]]<br /> <br /> :*The General Linear Model<br /> *Linear Algebra applied to Statistical Solutions<br /> :*Analysis of Variance<br /> <br /> ==''Wednesday 11/20/14 ...''==<br /> === - Statistics for Imaging II. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> *Fixed and Random Effects<br /> *Repeated measures<br /> :*Bonferroni and Other Corrections<br /> *Non-Parametric Methods<br /> *Autocorrelation<br /> *Unknown Distributions<br /> <br /> ''Required Readings''<br /> :*[[media: CohenClassSlides12_2_13.pdf‎ | same as 12/2/14]]<br /> <br /> ''Suggested Further Reading''<br /> <br /> :*[http://ccn.ucla.edu/media/PNI20121121.mp4 Lecture Video - To be Posted Shortly - Please Right Click and Save rather than Stream - Thank you Alan and Edward!]<br /> <br /> =Week 9: =<br /> ==''Monday 11/25/14''==<br /> === - TBD. [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> ==''Wednesday 11/27/14''==<br /> ===- The Organization of the Human Brain. ''Speaker'': [http://ccn.ucla.edu/bmcweb/bmc_bios/SusanBookheimer/ Susan Bookheimer]===<br /> <br /> We will discuss the general organization of the human brain, and the regional specialization of cortical areas. The emphasis will be on understanding principles of organization:<br /> *Phylogenetic Layering<br /> *Functional Specialization<br /> *Principles Divisions of the Brain<br /> *Brain Systems<br /> <br /> ''Required Readings''<br /> :*[http://da.biostr.washington.edu:80/cgi-bin/DA/PageMaster?atlas:NeuroSyllabus+ffpathIndex/Splash^Page^Syllabus+2 Neuroanatomy Programmed Learning]<br /> :*[[media:Cognitive_science_and_neuro_2012.pdf | Slides shown in Class UPDATED 2012/10/17]]<br /> ''Suggested Further Reading''<br /> <br /> :*[[media:PNIA_2012_PS1_Soln.pdf‎ | Solution to Problem Set 1]]<br /> ===- Human Electrophysiology ''Speakers'': [http://greenlab.npih.ucla.edu/ROSTER.html Agatha Lenartowicz], [http://dgsom.healthsciences.ucla.edu/institution/personnel?personnel_id=9140 John Stern]===<br /> <br /> ''Evoked Responses'' - Guest Lecturer: [http://greenlab.npih.ucla.edu/ROSTER.html Jonathan Wynn]<br /> *A look at real EEG data<br /> *Preprocessing:<br /> **filtering<br /> **artifact detection/removal<br /> *averaging<br /> *single events<br /> *interpretation<br /> <br /> ''Clinical EEG'' - Guest Lecturer: [http://dgsom.healthsciences.ucla.edu/institution/personnel?personnel_id=9140 John Stern]<br /> *Normal and Abnormal EEG<br /> *EEG as a marker for brain state<br /> **sleep staging<br /> **alpha and relaxation<br /> *Neurofeedback??? --&gt;</div>

Alenartowicz https://www.ccn.ucla.edu/wiki/index.php?title=Principles_of_Neuroimaging_B_-_2017&diff=3219 2016-11-16T21:28:24Z

<p>Alenartowicz: </p> <hr /> <div>Principles of Neuroimaging B, Winter, 2017 - Class Schedule and Syllabus<br /> <br /> == This schedule is a draft only and ''will'' change!==<br /> <br /> :'''[[Principles_of_Neuroimaging_-_2016-2017 | Back to main course page for Principles of Neuroimaging]]'''<br /> <br /> =Course Reading=<br /> ===Required Reading===<br /> :'''Signal Processing for Neuroscientists''' by ''Wim van Drongelen''<br /> ::This can be found as a PDF on scribd.com, for a small fee of $8.99<br /> <br /> ===Supplemental Reading===<br /> :'''Matlab for Neuroscientists'''<br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/88212458/Matlab-Matlab-for-Neuroscientists<br /> <br /> :'''Cartoon Guide to Statistics''' <br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/148072668/Cartoon-Guide-to-Statistics<br /> <br /> :'''NOTE:''' if you subscribe for a Scribd account for a day, you can download as many documents as you like for one fee.<br /> <br /> =Week 1: Magnetic Resonance Imaging: Fundamentals=<br /> <br /> Magnetic Resonance Imaging (MRI) is probably the most influential and most flexible current means of imaging the human brain. It features a vast number of separable contrast mechanisms, and a near ideal combination of non-invasiveness, safety, resolution and metric accuracy. However, it is extraordinarily expensive and has limited temporal resolution, especially for functional studies<br /> <br /> <br /> OUTLINE<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> :*[http://www.cis.rit.edu/htbooks/mri/ These notes] by Joseph Hornak are highly professional and complete coverage of MRI.<br /> :*[http://www.imaios.com/en/e-Courses/e-MRI eMRI] is another excellent online MRI learning resource<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Hahn1950.pdf Erwin Hahn - Spin Echoes: ''Essential reading for the MRI community'']<br /> [[Image:HahnFig1.png]]<br /> ''above'': Figure 1 from Hahn, 1950<br /> <br /> ''Suggested Further Reading''<br /> [[image:PSatSeq.jpg|right]]<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> <br /> ==''Monday 1/9/17'' ==<br /> ===- Pulse Sequences. ''Speaker'': TBD===<br /> <br /> ==''Wednesday 1/11/17'' ==<br /> ===- MR Parameters. ''Speaker'': TBD===<br /> <br /> =Week 2: MRI Techniques=<br /> ==''Monday 1/16/17'' ==<br /> ===MLK Day No Class===<br /> <br /> ==''Wednesday 1/18/17'' ==<br /> ===-MR Applications (MR, fMRI, DTI, ASL, Spectroscopy). ''Speaker'' TBD===<br /> <br /> [[Image:PET-ring.png | left]]<br /> <br /> =Week 3: Non-MR Imaging=<br /> ==''Monday 1/23/17'' ==<br /> ===- PET I. ''Speaker'' TBD===<br /> <br /> ==''Wednesday 1/25/17'' ==<br /> ===- PET II. ''Speaker'' TBD===<br /> <br /> [[Image:PETfounders.png | right]]<br /> <br /> =Week 4: Non-MR Imaging=<br /> ==''Monday 1/30/17'' ==<br /> ===- Optogenetics I. ''Speaker'' TBD===<br /> <br /> ==''Wednesday 2/1/17'' ==<br /> ===- Optogenetics II. ''Speaker'' TBD===<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:Golshani_NITP_2016.pdf | Dr. Golshani's Methods slides]]<br /> :*[[media:PeymanResearchTalk.pdf | Dr. Golshani's Research slides]]<br /> ''Suggested Further Reading''<br /> :*<br /> <br /> =Week 5: Multimodal Imaging &amp; Neuroimaging Data=<br /> ==''Monday 2/6/17'' ==<br /> ===- Multimodal Imaging ''Speaker'' TBD===<br /> <br /> ==''Wednesday 2/8/17'' ==<br /> ===- Neuroimaging Data: Fundamentals. ''Speaker'' TBD===<br /> <br /> =MIDTERM=<br /> <br /> =Week 6: Neuroimaging Data=<br /> ==''Monday 2/13/17'' ==<br /> ===Midterm Review===<br /> <br /> ==''Wednesday 2/15/17'' ==<br /> ===-Multivariate Analyses: Dimension Reduction. ''Speaker'' TBD===<br /> <br /> =Week 7: Neuroimaging Data=<br /> ==''Monday 2/20/17'' ==<br /> ===President’s Day Holiday No Class===<br /> <br /> ==''Wednesday 2/22/17'' ==<br /> ===- Multivariate Analyses: Brain Decoding ''Speaker'' TBD===<br /> <br /> =Week 8: Practical Brain Decoding &amp; Connectivity=<br /> ==''Monday 2/27/17'' ==<br /> ===Building a BCI System===<br /> <br /> ==''Wednesday 3/1/17'' ==<br /> ===- Connectivity Fundamentals ''Speaker'' TBD===<br /> <br /> =Week 9: Neuromodulation=<br /> ==''Monday 3/6/17'' ==<br /> ===-TMS. ''Speaker'' TBD===<br /> <br /> ==''Wednesday 3/8/17'' ==<br /> ===- Ultrasound. ''Speaker'' TBD===<br /> <br /> 'Required Readings''<br /> :* [[media:TMSSafetyAndEthics-Rossi.pdf | TMS Safety and Ethics - Rossi, 2009]]<br /> :* [http://www.sciencedirect.com/science?_ob=ArticleURL&amp;_udi=B6T0J-50CV801-1&amp;_user=4423&amp;_coverDate=01%2F31%2F2011&amp;_rdoc=1&amp;_fmt=high&amp;_orig=search&amp;_origin=search&amp;_sort=d&amp;_docanchor=&amp;view=c&amp;_acct=C000059605&amp;_version=1&amp;_urlVersion=0&amp;_userid=4423&amp;md5=f9fa3c7d63942dfb3c74e047a1f848bc&amp;searchtype=a | M Sandrini, C Umilta and E Rusconi, “''The use of transcranial magnetic stimulation in cognitive neuroscience: a new synthesis of methodological issues.''” '''Neurosci Biobehav Rev''', '''35'''(3): p. 516-536. 2011]<br /> <br /> ''Suggested Further Reading''<br /> Once upon a time we demonstrated that this sort of magnetic stimulation can take place in the MRI machines:<br /> :*[http://onlinelibrary.wiley.com/doi/10.1002/mrm.1910140226/pdf MS Cohen, RM Weisskoff, RR Rzedzian and HL Kantor, “Sensory stimulation by time-varying magnetic fields.” Magnetic Resonance in Medicine, 14(2): p. 409-414. 1990]<br /> <br /> [[Image:Cetacean.png | right]]<br /> :*[[media:US_Imaging_Systems_1.pdf | Ultrasound Slides (2016)]]<br /> <br /> ''Suggested Further Reading''<br /> :* [[media:Culjat2010.pdf | A review of tissue substitutes for ultrasound imaging ]]<br /> <br /> <br /> =Week 10: Review=<br /> ==''Monday 3/13/17'' ==<br /> ===-Final Exam Review. ''Speaker'' TBD===<br /> <br /> ==''Wednesday 3/15/17'' ==<br /> ===-TBD===<br /> <br /> <br /> <br /> <br /> <br /> &lt;!--<br /> [[Image:ASL.png | right]]<br /> ===- Measurement of Blood Flow by MRI. ''Danny JJ Wang'': [http://www.bri.ucla.edu/people/danny-jiong-jiong-wang-phd]===<br /> Most MRI images in use today are static maps, generally called &quot;structural&quot; images. However, the MRI signal is sensitive to a wide variety of dynamic phenomena. Practitioners typically use the term &quot;functional&quot; to describe images that pick up time-varying signals. The term ''functional MRI'', of course, has been usurped to describe brain activity mapping and, in most cases, refers to BOLD signal effects.<br /> <br /> Measurement of blood ''flow'' in particular is another window into brain activity. Several methods exist to create signal sensitive to blood flow, or blood flow changes. Some label the blood flow based on relaxation effects (Arterial Spin Labeling - ASL - being one important method) and others measure flow by velocity.<br /> <br /> In most cases, MRI is used as a semi quantitative modality. We get accurate spatial metrics but, in general, the image intensities are in arbitrary units. Blood flow is one particular feature that we can measure in native units.<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_perfusion-web.pdf‎ | Dr. Wang's Handout]]<br /> <br /> :*[http://www.cis.rit.edu/htbooks/mri/ These notes] by Joseph Hornak are highly professional and complete coverage of MRI.]<br /> :*[[media:M284B_perfusion-web.pdf‎ | Dr. Wang's Handout]]<br /> [[Image:HahnFig1.png]]<br /> ''above'': Figure 1 from Hahn, 1950<br /> <br /> ''Suggested Further Reading''<br /> :*[http://www.pnas.org/content/89/1/212.long Williams, PNAS 1992]<br /> :*[http://onlinelibrary.wiley.com/doi/10.1002/mrm.1910230106/abstract;jsessionid=E42CC3817A5B7B6BB12B6BA3562C1913.f03t04 Detre, MRM 1992]<br /> :*[http://onlinelibrary.wiley.com/doi/10.1002/mrm.21790/epdf Dai, MRM 2008]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/17969096 Wu, MRM 2007]<br /> :*[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3155947/ Zheng, Neuroimage 2012]<br /> <br /> <br /> <br /> =Week 8: Advanced MRI methods=<br /> ==''Monday 2/22/16'' ==<br /> [[Image:DIffusingSpheres.png | left]]<br /> ===- Diffusion Physics ''Speaker'': [http://radiology.ucla.edu/body.cfm?id=48&amp;action=detail&amp;ref=18 Ben Ellingson]===<br /> ''Required Readings''<br /> Slides should be up soon.<br /> :*[https://www.dropbox.com/s/0feqz7tlojbfgzl/1_DiffusionMRI_Methods_2015.key.pdf?dl=0 Handouts for class on 1/14/15] ''&lt;- New 1/26-2015''<br /> :*[[media:1_DiffusionMRI_Methods_v1 - 2014.pdf | Ben Ellingson Diffusion Slides]] uploaded 3/14/14<br /> ''Suggested Further Reading''<br /> :*[[media:DTIHageman.pdf | Hageman DTI Slides]]<br /> :*[[media:NICourse_DTILecture4Post_11-01-26.pdf | Hageman DTI Notes 2/26/12]]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/15037456?dopt=Citation Diffusion tensor imaging of cerebral white matter: a pictorial review of physics, fiber tract anatomy, and tumor imaging patterns AJNR Am J Neuroradiol. 2004 Mar;25(3):356-69]<br /> <br /> :Sadly, the library does not have a subscription for the journals below (Mark has copies on reserve in his office):<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/8661285?dopt=Citation Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J Magn Reson B. 1996 Jun;111(3):209-19]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/16950152?dopt=Citation Principles of diffusion tensor imaging and its applications to basic neuroscience research. Neuron. 2006 Sep 7;51(5):527-39]<br /> <br /> ==''Wednesday 2/25/16'' ==<br /> [[Image:Callosum.png | right]]<br /> ===- Brain Morphometry. ''Speaker'': [http://www.bmap.ucla.edu/about/peopledetails/roger_woods/ Roger Woods]: ===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_Woods.pdf‎ | Dr. Woods' slides]]<br /> <br /> ''Suggested Further Reading''<br /> :[http://www.ncbi.nlm.nih.gov/pubmed/12667854 *RP Woods, “Characterizing volume and surface deformations in an atlas framework: theory, applications, and implementation.” Neuroimage, 18(3): p. 769-788. 2003]<br /> <br /> =Week 9: MR Spectroscopy=<br /> ==''Monday 2/29/16'' ==<br /> No class<br /> <br /> ==''Wednesday 3/2/16'' ==<br /> === Spectroscopy fundamentals. ''Speaker'': [http://people.healthsciences.ucla.edu/institution/personnel?personnel_id=74852 Joseph O'Neill] ===<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:ONeill2016Spectro.pdf‎ | Dr. O'Neill's slides]]<br /> <br /> ===- Spectroscopic Methods. ''Speaker'': [http://radiology.ucla.edu/body.cfm?id=48&amp;action=detail&amp;ref=65 Albert Thomas] ===<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_AThomas030216.pdf‎ | Dr. Thomas' slides]]<br /> ''Suggested Further Reading''<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/23292856 SA Wijtenburg, LM Rowland, RA Edden and PB Barker, “Reproducibility of brain spectroscopy at 7T using conventional localization and spectral editing techniques.” J Magn Reson Imaging, 38(2): p. 460-467. 2013. PMCID: PMC3620961]<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/20574964 S Lipnick, G Verma, S Ramadan, J Furuyama and MA Thomas, “Echo planar correlated spectroscopic imaging: implementation and pilot evaluation in human calf in vivo.” Magn Reson Med, 64(4): p. 947-956. 2010. PMCID: PMC2946523]<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/22505247 JK Furuyama, NE Wilson, BL Burns, R Nagarajan, DJ Margolis and MA Thomas, “Application of compressed sensing to multidimensional spectroscopic imaging in human prostate.” Magn Reson Med, 67(6): p. 1499-1505. 2012.]<br /> <br /> =Week 10: Multimodal Methods / Course wrap up=<br /> ==''Monday 3/7/16'' ==<br /> [[Image:Kinship.png | right]]<br /> ===- Imaging Genetics. ''Speaker'': [http://www.semel.ucla.edu/bearden-lab Carrie Bearden]: ===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:Bearden_NITP2_2016-3-4.pdf | Dr. Bearden's slides]]<br /> <br /> ''Suggested Further Reading''<br /> :*[[media:Carter-Genomics.pdf | ''Enhancing the Informativeness and Replicability of Imaging Genomics Studies'' Carter et al.]]<br /> :*[[media:Flint_and_Geschwind.pdf | ''Genetics and genomics of psychiatric disease'' Flint and Geschwind]]<br /> <br /> <br /> --&gt;</div>

Alenartowicz https://www.ccn.ucla.edu/wiki/index.php?title=Principles_of_Neuroimaging_B_-_2017&diff=3218 2016-11-16T21:27:52Z

<p>Alenartowicz: /* Week 1: Magnetic Resonance Imaging */</p> <hr /> <div>Principles of Neuroimaging B, Winter, 2016 - Class Schedule and Syllabus<br /> <br /> == This schedule is a draft only and ''will'' change!==<br /> <br /> :'''[[Principles_of_Neuroimaging_-_2016-2017 | Back to main course page for Principles of Neuroimaging]]'''<br /> <br /> =Course Reading=<br /> ===Required Reading===<br /> :'''Signal Processing for Neuroscientists''' by ''Wim van Drongelen''<br /> ::This can be found as a PDF on scribd.com, for a small fee of $8.99<br /> <br /> ===Supplemental Reading===<br /> :'''Matlab for Neuroscientists'''<br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/88212458/Matlab-Matlab-for-Neuroscientists<br /> <br /> :'''Cartoon Guide to Statistics''' <br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/148072668/Cartoon-Guide-to-Statistics<br /> <br /> :'''NOTE:''' if you subscribe for a Scribd account for a day, you can download as many documents as you like for one fee.<br /> <br /> =Week 1: Magnetic Resonance Imaging: Fundamentals=<br /> <br /> Magnetic Resonance Imaging (MRI) is probably the most influential and most flexible current means of imaging the human brain. It features a vast number of separable contrast mechanisms, and a near ideal combination of non-invasiveness, safety, resolution and metric accuracy. However, it is extraordinarily expensive and has limited temporal resolution, especially for functional studies<br /> <br /> <br /> OUTLINE<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> :*[http://www.cis.rit.edu/htbooks/mri/ These notes] by Joseph Hornak are highly professional and complete coverage of MRI.<br /> :*[http://www.imaios.com/en/e-Courses/e-MRI eMRI] is another excellent online MRI learning resource<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Hahn1950.pdf Erwin Hahn - Spin Echoes: ''Essential reading for the MRI community'']<br /> [[Image:HahnFig1.png]]<br /> ''above'': Figure 1 from Hahn, 1950<br /> <br /> ''Suggested Further Reading''<br /> [[image:PSatSeq.jpg|right]]<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> <br /> ==''Monday 1/9/17'' ==<br /> ===- Pulse Sequences. ''Speaker'': TBD===<br /> <br /> ==''Wednesday 1/11/17'' ==<br /> ===- MR Parameters. ''Speaker'': TBD===<br /> <br /> =Week 2: MRI Techniques=<br /> ==''Monday 1/16/17'' ==<br /> ===MLK Day No Class===<br /> <br /> ==''Wednesday 1/18/17'' ==<br /> ===-MR Applications (MR, fMRI, DTI, ASL, Spectroscopy). ''Speaker'' TBD===<br /> <br /> [[Image:PET-ring.png | left]]<br /> <br /> =Week 3: Non-MR Imaging=<br /> ==''Monday 1/23/17'' ==<br /> ===- PET I. ''Speaker'' TBD===<br /> <br /> ==''Wednesday 1/25/17'' ==<br /> ===- PET II. ''Speaker'' TBD===<br /> <br /> [[Image:PETfounders.png | right]]<br /> <br /> =Week 4: Non-MR Imaging=<br /> ==''Monday 1/30/17'' ==<br /> ===- Optogenetics I. ''Speaker'' TBD===<br /> <br /> ==''Wednesday 2/1/17'' ==<br /> ===- Optogenetics II. ''Speaker'' TBD===<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:Golshani_NITP_2016.pdf | Dr. Golshani's Methods slides]]<br /> :*[[media:PeymanResearchTalk.pdf | Dr. Golshani's Research slides]]<br /> ''Suggested Further Reading''<br /> :*<br /> <br /> =Week 5: Multimodal Imaging &amp; Neuroimaging Data=<br /> ==''Monday 2/6/17'' ==<br /> ===- Multimodal Imaging ''Speaker'' TBD===<br /> <br /> ==''Wednesday 2/8/17'' ==<br /> ===- Neuroimaging Data: Fundamentals. ''Speaker'' TBD===<br /> <br /> =MIDTERM=<br /> <br /> =Week 6: Neuroimaging Data=<br /> ==''Monday 2/13/17'' ==<br /> ===Midterm Review===<br /> <br /> ==''Wednesday 2/15/17'' ==<br /> ===-Multivariate Analyses: Dimension Reduction. ''Speaker'' TBD===<br /> <br /> =Week 7: Neuroimaging Data=<br /> ==''Monday 2/20/17'' ==<br /> ===President’s Day Holiday No Class===<br /> <br /> ==''Wednesday 2/22/17'' ==<br /> ===- Multivariate Analyses: Brain Decoding ''Speaker'' TBD===<br /> <br /> =Week 8: Practical Brain Decoding &amp; Connectivity=<br /> ==''Monday 2/27/17'' ==<br /> ===Building a BCI System===<br /> <br /> ==''Wednesday 3/1/17'' ==<br /> ===- Connectivity Fundamentals ''Speaker'' TBD===<br /> <br /> =Week 9: Neuromodulation=<br /> ==''Monday 3/6/17'' ==<br /> ===-TMS. ''Speaker'' TBD===<br /> <br /> ==''Wednesday 3/8/17'' ==<br /> ===- Ultrasound. ''Speaker'' TBD===<br /> <br /> 'Required Readings''<br /> :* [[media:TMSSafetyAndEthics-Rossi.pdf | TMS Safety and Ethics - Rossi, 2009]]<br /> :* [http://www.sciencedirect.com/science?_ob=ArticleURL&amp;_udi=B6T0J-50CV801-1&amp;_user=4423&amp;_coverDate=01%2F31%2F2011&amp;_rdoc=1&amp;_fmt=high&amp;_orig=search&amp;_origin=search&amp;_sort=d&amp;_docanchor=&amp;view=c&amp;_acct=C000059605&amp;_version=1&amp;_urlVersion=0&amp;_userid=4423&amp;md5=f9fa3c7d63942dfb3c74e047a1f848bc&amp;searchtype=a | M Sandrini, C Umilta and E Rusconi, “''The use of transcranial magnetic stimulation in cognitive neuroscience: a new synthesis of methodological issues.''” '''Neurosci Biobehav Rev''', '''35'''(3): p. 516-536. 2011]<br /> <br /> ''Suggested Further Reading''<br /> Once upon a time we demonstrated that this sort of magnetic stimulation can take place in the MRI machines:<br /> :*[http://onlinelibrary.wiley.com/doi/10.1002/mrm.1910140226/pdf MS Cohen, RM Weisskoff, RR Rzedzian and HL Kantor, “Sensory stimulation by time-varying magnetic fields.” Magnetic Resonance in Medicine, 14(2): p. 409-414. 1990]<br /> <br /> [[Image:Cetacean.png | right]]<br /> :*[[media:US_Imaging_Systems_1.pdf | Ultrasound Slides (2016)]]<br /> <br /> ''Suggested Further Reading''<br /> :* [[media:Culjat2010.pdf | A review of tissue substitutes for ultrasound imaging ]]<br /> <br /> <br /> =Week 10: Review=<br /> ==''Monday 3/13/17'' ==<br /> ===-Final Exam Review. ''Speaker'' TBD===<br /> <br /> ==''Wednesday 3/15/17'' ==<br /> ===-TBD===<br /> <br /> <br /> <br /> <br /> <br /> &lt;!--<br /> [[Image:ASL.png | right]]<br /> ===- Measurement of Blood Flow by MRI. ''Danny JJ Wang'': [http://www.bri.ucla.edu/people/danny-jiong-jiong-wang-phd]===<br /> Most MRI images in use today are static maps, generally called &quot;structural&quot; images. However, the MRI signal is sensitive to a wide variety of dynamic phenomena. Practitioners typically use the term &quot;functional&quot; to describe images that pick up time-varying signals. The term ''functional MRI'', of course, has been usurped to describe brain activity mapping and, in most cases, refers to BOLD signal effects.<br /> <br /> Measurement of blood ''flow'' in particular is another window into brain activity. Several methods exist to create signal sensitive to blood flow, or blood flow changes. Some label the blood flow based on relaxation effects (Arterial Spin Labeling - ASL - being one important method) and others measure flow by velocity.<br /> <br /> In most cases, MRI is used as a semi quantitative modality. We get accurate spatial metrics but, in general, the image intensities are in arbitrary units. Blood flow is one particular feature that we can measure in native units.<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_perfusion-web.pdf‎ | Dr. Wang's Handout]]<br /> <br /> :*[http://www.cis.rit.edu/htbooks/mri/ These notes] by Joseph Hornak are highly professional and complete coverage of MRI.]<br /> :*[[media:M284B_perfusion-web.pdf‎ | Dr. Wang's Handout]]<br /> [[Image:HahnFig1.png]]<br /> ''above'': Figure 1 from Hahn, 1950<br /> <br /> ''Suggested Further Reading''<br /> :*[http://www.pnas.org/content/89/1/212.long Williams, PNAS 1992]<br /> :*[http://onlinelibrary.wiley.com/doi/10.1002/mrm.1910230106/abstract;jsessionid=E42CC3817A5B7B6BB12B6BA3562C1913.f03t04 Detre, MRM 1992]<br /> :*[http://onlinelibrary.wiley.com/doi/10.1002/mrm.21790/epdf Dai, MRM 2008]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/17969096 Wu, MRM 2007]<br /> :*[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3155947/ Zheng, Neuroimage 2012]<br /> <br /> <br /> <br /> =Week 8: Advanced MRI methods=<br /> ==''Monday 2/22/16'' ==<br /> [[Image:DIffusingSpheres.png | left]]<br /> ===- Diffusion Physics ''Speaker'': [http://radiology.ucla.edu/body.cfm?id=48&amp;action=detail&amp;ref=18 Ben Ellingson]===<br /> ''Required Readings''<br /> Slides should be up soon.<br /> :*[https://www.dropbox.com/s/0feqz7tlojbfgzl/1_DiffusionMRI_Methods_2015.key.pdf?dl=0 Handouts for class on 1/14/15] ''&lt;- New 1/26-2015''<br /> :*[[media:1_DiffusionMRI_Methods_v1 - 2014.pdf | Ben Ellingson Diffusion Slides]] uploaded 3/14/14<br /> ''Suggested Further Reading''<br /> :*[[media:DTIHageman.pdf | Hageman DTI Slides]]<br /> :*[[media:NICourse_DTILecture4Post_11-01-26.pdf | Hageman DTI Notes 2/26/12]]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/15037456?dopt=Citation Diffusion tensor imaging of cerebral white matter: a pictorial review of physics, fiber tract anatomy, and tumor imaging patterns AJNR Am J Neuroradiol. 2004 Mar;25(3):356-69]<br /> <br /> :Sadly, the library does not have a subscription for the journals below (Mark has copies on reserve in his office):<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/8661285?dopt=Citation Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J Magn Reson B. 1996 Jun;111(3):209-19]<br /> :*[http://www.ncbi.nlm.nih.gov/pubmed/16950152?dopt=Citation Principles of diffusion tensor imaging and its applications to basic neuroscience research. Neuron. 2006 Sep 7;51(5):527-39]<br /> <br /> ==''Wednesday 2/25/16'' ==<br /> [[Image:Callosum.png | right]]<br /> ===- Brain Morphometry. ''Speaker'': [http://www.bmap.ucla.edu/about/peopledetails/roger_woods/ Roger Woods]: ===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_Woods.pdf‎ | Dr. Woods' slides]]<br /> <br /> ''Suggested Further Reading''<br /> :[http://www.ncbi.nlm.nih.gov/pubmed/12667854 *RP Woods, “Characterizing volume and surface deformations in an atlas framework: theory, applications, and implementation.” Neuroimage, 18(3): p. 769-788. 2003]<br /> <br /> =Week 9: MR Spectroscopy=<br /> ==''Monday 2/29/16'' ==<br /> No class<br /> <br /> ==''Wednesday 3/2/16'' ==<br /> === Spectroscopy fundamentals. ''Speaker'': [http://people.healthsciences.ucla.edu/institution/personnel?personnel_id=74852 Joseph O'Neill] ===<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:ONeill2016Spectro.pdf‎ | Dr. O'Neill's slides]]<br /> <br /> ===- Spectroscopic Methods. ''Speaker'': [http://radiology.ucla.edu/body.cfm?id=48&amp;action=detail&amp;ref=65 Albert Thomas] ===<br /> <br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284B_AThomas030216.pdf‎ | Dr. Thomas' slides]]<br /> ''Suggested Further Reading''<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/23292856 SA Wijtenburg, LM Rowland, RA Edden and PB Barker, “Reproducibility of brain spectroscopy at 7T using conventional localization and spectral editing techniques.” J Magn Reson Imaging, 38(2): p. 460-467. 2013. PMCID: PMC3620961]<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/20574964 S Lipnick, G Verma, S Ramadan, J Furuyama and MA Thomas, “Echo planar correlated spectroscopic imaging: implementation and pilot evaluation in human calf in vivo.” Magn Reson Med, 64(4): p. 947-956. 2010. PMCID: PMC2946523]<br /> :* [http://www.ncbi.nlm.nih.gov/pubmed/22505247 JK Furuyama, NE Wilson, BL Burns, R Nagarajan, DJ Margolis and MA Thomas, “Application of compressed sensing to multidimensional spectroscopic imaging in human prostate.” Magn Reson Med, 67(6): p. 1499-1505. 2012.]<br /> <br /> =Week 10: Multimodal Methods / Course wrap up=<br /> ==''Monday 3/7/16'' ==<br /> [[Image:Kinship.png | right]]<br /> ===- Imaging Genetics. ''Speaker'': [http://www.semel.ucla.edu/bearden-lab Carrie Bearden]: ===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:Bearden_NITP2_2016-3-4.pdf | Dr. Bearden's slides]]<br /> <br /> ''Suggested Further Reading''<br /> :*[[media:Carter-Genomics.pdf | ''Enhancing the Informativeness and Replicability of Imaging Genomics Studies'' Carter et al.]]<br /> :*[[media:Flint_and_Geschwind.pdf | ''Genetics and genomics of psychiatric disease'' Flint and Geschwind]]<br /> <br /> <br /> --&gt;</div>

Alenartowicz https://www.ccn.ucla.edu/wiki/index.php?title=Principles_of_Neuroimaging_A_-_2016&diff=3217 2016-11-16T21:26:57Z

<p>Alenartowicz: /* Week 9 - Magnetic Resonance Fundamentals */</p> <hr /> <div>Principles of Neuroimaging A, Fall, 2016 - Class Schedule and Syllabus<br /> <br /> <br /> ==='''Neuroimaging journal Club (''required for NITP certificate'')'''===<br /> Contact: Katherine Lawrence (katherine.E.Lawrence@ucla.edu) or Janelle Liu (janelle.j.liu@ucla.edu), Faculty Sponsor: Jamie Feusner (JFeusner@mednet.ucla.edu)<br /> <br /> ==This schedule ''will'' change!==<br /> <br /> :'''[[Principles_of_Neuroimaging_-_2016-2017 | Back to main course page for Principles of Neuroimaging]]'''<br /> <br /> :'''Coming up next!: [[Principles_of_Neuroimaging_B_-_2017 | M284B Principles of Neuroimaging B]]'''<br /> <br /> =Course Reading=<br /> ===Required Reading===<br /> :'''Signal Processing for Neuroscientists''' by ''Wim van Drongelen''<br /> ::This can be found as a PDF on scribd.com, for a small fee of $8.99<br /> <br /> ===Supplemental Reading===<br /> :'''Matlab for Neuroscientists'''<br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/88212458/Matlab-Matlab-for-Neuroscientists<br /> <br /> :'''Cartoon Guide to Statistics''' <br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/148072668/Cartoon-Guide-to-Statistics<br /> <br /> :'''NOTE:''' if you subscribe for a Scribd account for a day, you can download as many documents as you like for one fee.<br /> <br /> <br /> =Week 1: Orientation to Neuro-imaging, Neurons, Brains=<br /> <br /> ==''Monday 9/26/16'' ==<br /> ===- Images. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284AImages-intro.pdf | Lecture Slides Images]]<br /> <br /> ==''Wednesday 9/28/16''==<br /> ===- Neurons &amp; Signaling. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> In this first class we will review the basics of neurophysiology with an eye towards what signals of brain function might be visible to the neuroimager. We will discuss information coding, energetics, size and time scales.<br /> [[Image:Neurons.jpg|right]]<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:TheActiveBrain.pdf | The Active Brain]]<br /> :*[[media:NeuronFunctionM284-2016_SM.pdf | Neuron function slides shown in class]]<br /> :*[http://www.science.smith.edu/departments/neurosci/courses/bio330/squid.html Videos of giant squid experiments]<br /> ''Suggested Further Reading''<br /> :*[http://ccn.ucla.edu/wiki/images/5/5a/CAVEAT_LECTOR.pdf Caveat Lector - the misuse of neuroimaging]<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Protected/Kosslyn1999.pdf &quot;If Neuroimaging is the Answer, What is the Question?&quot; Kosslyn, 1999]<br /> :*[http://da.biostr.washington.edu:80/cgi-bin/DA/PageMaster?atlas:NeuroSyllabus+ffpathIndex/Splash^Page^Syllabus+2 Neuroanatomy Programmed Learning]<br /> :*[http://www.amazon.com/Fundamental-Neuroscience-Second-Larry-Squire/dp/0126603030 Squire, Fundamentals of Neuroscience]<br /> :*[http://www.amazon.com/Principles-Neural-Science-Eric-Kandel/dp/0838577016 Kandel, et al., &quot;Principles of Neural Science&quot;]<br /> :This paper, by Malhi, is a nice orientation in methods of neuroimaging. *[http://www.ccn.ucla.edu/wiki/images/f/f2/Malhi2007.pdf Making sense of neuroimaging in psychiatry]<br /> :*[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1359308/pdf/jphysiol01232-0142.pdf Replacement of the axoplasm of giant nerve fibres with artificial solutions]<br /> <br /> =Week 2: Linear Systems, Convolution, Fourier Transforms=<br /> ==''Wednesday 10/3/16''==<br /> <br /> ===- Linear Systems I. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ==''Wednesday 10/5/16''==<br /> ===- Linear Systems II. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> Why the emphasis on Linear Systems? Because they are actually ''easy'' (as compared to non-linear systems, which are not.) As we go through this course, we will see many ways in which linear systems theory is applied to:<br /> :Modeling of Neural Systems<br /> :Extraction of Signal from Noise<br /> :Design of Circuits<br /> :Image Enhancement<br /> :Understanding of Image artifacts, and others.<br /> <br /> Linear systems analysis is one of the great technologies of the 20th and 21st century. It is now the basis for virtually all electronics design, and its extension into the discrete (digital) domain is the basis for most of modern signal processing. <br /> <br /> In our specific case, we will use these few basic principles of linear systems to understand both the instruments we use and the neuroimaging signals we collect. When you have mastered this material, you should be in a much better position to model the systems that you study in order to develop an approach to studying them.<br /> <br /> Here is [http://www.brainmapping.org/NITP/PNA/Readings/ImaginaryNumbers.pdf A primer I wrote on imaginary numbers] that might be a helpful review.<br /> <br /> There is a nice [http://en.wikibooks.org/wiki/Calculus Wikibook on Calculus].<br /> <br /> ''Required Readings''<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapter 1<br /> :*[[media:MathematicalTools2016.pdf | Mathematical Tools]]<br /> <br /> ''Suggested Further Reading''<br /> :'''Introduction to matlab'''<br /> <br /> ''Slides shown in class''<br /> :[[media:LinearityM284A-2016.pdf | Linearity and the Fourier Transform]]<br /> <br /> Please see [http://www.brainmapping.org/NITP/PNA/html/Linearity.html MATLAB linearity demo]<br /> <br /> If you are the type who sees beauty in mathematics, the Euler identity may be one of the most beautiful pieces of math in the world.<br /> <br /> =Week 3: Math &amp; Circuits I=<br /> <br /> ==''Monday 10/10/16''==<br /> === - Circuits I. Speaker: Cameron Rodriguez ===<br /> :*[[media:Circuits_2016.pdf | Circuit slides shown in class]]<br /> <br /> ==''Wednesday 10/12/16''==<br /> === - Circuits II. ''Speaker'': Cameron Rodriguez ===<br /> <br /> Why circuits?<br /> :(Virtually) Every device you use in your research is electronic. You access your primary data only indirectly<br /> :The device you ''really'' want in your lab doesn't exist. You very well may have to make it.<br /> :There are electronic analogs to most of the linear systems that you have so far studied (and ''vice versa'' - the tools you now understand can be used to analyze and predict circuit behavior).<br /> <br /> :If you have not had any of this background, you might want to have a look at this handout, [[Media:Electricity.pdf|Electrical Circuits]], in advance. There are near infinite numbers of resources on the web that cover similar material (near enough to infinite that by the time you read all of them, there would be a whole new set.) I have recently come across a link to [http://www.allaboutcircuits.com/ Online Books: All About Circuits] ''IF'' you want practical hands-on knowledge about this material, my all-time favorite text is [http://www.google.com/products/catalog?hl=en&amp;client=safari&amp;rls=en-us&amp;ei=uVSPSfaxE5nMsAPf-tmSCQ&amp;resnum=1&amp;q=art+of+electronics&amp;um=1&amp;ie=UTF-8&amp;cid=8820839049329255765#ps-sellers &quot;Horowitz and Hill: ''The Art of Electronics.''&quot;] The latest edition, however, is dated 1989 and a new third edition is promised. I have therefore stopped short of recommending a purchase unless your need to make circuits is immediate. In this book, you will find an excellent education on the fundamental principles of electrical circuits and an incredible compendium of practical data, such as how to assemble circuit boards, how to make measurements, etc...) <br /> <br /> Readings:<br /> :*[http://www.ti.com/lit/an/sloa093/sloa093.pdf Filter Design in 30 Seconds]<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapter 2 and 10<br /> :*[https://www.circuitlab.com/ Circuit Lab ] A Free Circuit Web Base Simulator<br /> **You may or may not find this comprehensible without chapters 5 through 9.<br /> <br /> We will discuss:<br /> :*Passive Circuit Elements: Resistors, Capacitors, Inductors<br /> :*Gain<br /> :*Transformers<br /> :*Rectifiers<br /> :*Active Elements<br /> ::- ''Amplifiers''<br /> ::- ''Transistors''<br /> ::- ''Op Amps''<br /> :*Solutions with Matrices<br /> ''Suggested Further Reading''<br /> :*[http://video.google.com/videoplay?docid=5645396659673218353&amp;q=Physics+for+Future+Presidents+Electricity&amp;total=5&amp;start=0&amp;num=10&amp;so=0&amp;type=search&amp;plindex=0#0h20m30s Video intro lecture on charge, current and voltage].<br /> :*[[media:Electricity_Basics.pdf‎ | Well organized text on electrical concepts by Tony R. Kuphaldt]]<br /> :*[http://en.wikibooks.org/wiki/Circuit_Theory Circuit Theory - Wikibook]<br /> <br /> '''Suggested, Optional Readings from [http://www.dspguide.com DSPguide.com]:'''<br /> :*[http://www.dspguide.com/CH5.PDF Linear Systems]<br /> :*[http://www.dspguide.com/CH6.PDF Convolution]<br /> :*[http://www.dspguide.com/CH8.PDF Discrete Fourier Transform (DFT)]<br /> :''Note: These chapters are light on math and try to focus on a conceptual understanding''<br /> <br /> Time and Frequency / Spectral Filters<br /> <br /> ----<br /> Practice using the Fourier transform:<br /> :[http://www.brainmapping.org/NITP/PNA/ConvFThtml/ConvolutionWorksheet.pdf Fourier transform and Convolution Worksheet]. &lt;!-- [http://www.brainmapping.org/NITP/PNA/ConvFThtml/ConvFT.html (''Solutions'').] --&gt;<br /> :[http://www.brainmapping.org/NITP/PNA/ConvFThtml/Something.wav Sound file for worksheet above.]<br /> <br /> Other<br /> :*[http://www.youtube.com/watch?feature=player_embedded&amp;v=7uHfjpU3OH0#! Getting Started with Arduino]<br /> <br /> =Week 4 Signal Processing=<br /> <br /> ==''Monday 10/17/16''==<br /> === - Signal Processing. ''Speaker'': Cameron Rodriguez ===<br /> <br /> :*[[media:Signals1-PreProcessing1_2016.pdf | Signal Pre Processing Slide Set 1 shown in class]]<br /> <br /> <br /> ==''Wednesday 10/19/16''==<br /> === - Noise ''Speaker'': Cameron Rodriguez ===<br /> <br /> :*[[media:OneD_signal_Filter_Ex.pdf | Matlab Signal Tutorial printout]]<br /> <br /> It is what you ''don't'' want - usually - but things change in quantized systems<br /> :Additive noise<br /> :White Noise<br /> :Boltzmann noise<br /> :Colored Noise<br /> :Gaussian Noise<br /> :Coherent noise<br /> :Sampling Errors<br /> :Aliasing<br /> :Quantization noise<br /> :Spectral filtering<br /> <br /> Noise comes in all shapes and colors. It is present in every measurement we make, from an EEG voltage to an estimate of the effects of dopamine on forebrain signal. Our best weapons are an understanding of the statistical properties of noise, the sources of noise and the ways to control it. Noise in the discrete digital domain is special, as it is both ''created'' by digitization and amplified by sampling.<br /> <br /> Readings:<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapters 2 through 4<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Noise.pdf Noise Slides]<br /> <br /> =Week 5 - Information &amp; Statistical Theory =<br /> <br /> ==''Monday 10/24/16 - in C8-177''==<br /> === Noise Cont' &amp; Information Theory Speakers: Cameron Rodriguez &amp; John Villasenor===<br /> <br /> ==''Wednesday 10/26/16''==<br /> ===Statistical Theory ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> We will consider the general problems of statistical inference, with a concentration on developing an intuitive understanding of statistical concepts.<br /> [[Image:MeasureForMeasure.jpg|right]]<br /> <br /> Suggested reading<br /> :*[http://www.statsoft.com/textbook/stbasic.html Statsoft online text (''free'')]<br /> :*[http://www.amazon.com/Cartoon-Guide-Statistics-Larry-Gonick/dp/0062731025 The Cartoon Guide to Statistics - Gonick $17.95 new]<br /> :The latter teaches stats at what I feel to be the right level - developing intuitions about the kinds of questions that can be answered using stats and about the statistical tests and measures<br /> <br /> :'''Problem Set 3 - Statistics in matlab'''<br /> ::[[media: Problem_Set_1.doc|Problem set using stats and MATLAB]]<br /> ::[[media: Problem_Set_1B.doc|More practice with stats and MATLAB]]<br /> <br /> <br /> =MIDTERM=<br /> <br /> <br /> =Week 6 - Neurophysiology=<br /> ==''Monday 10/31/16''==<br /> === Electrophysiology I ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> We will examine our first imaging modality, EEG (and MEG).<br /> <br /> ::[[media: EEGNeurophys_161102.pdf|Slides for Neurophysiology Lectures]]<br /> <br /> ==''Wednesday 11/2/16''==<br /> === Electrophysiology II ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> Continuing with neurophysiology. ERPs &amp; Source Localization.<br /> <br /> =Week 7 - Neurophysiology =<br /> ==''Monday 11/7/16''==<br /> === Electrophysiology III ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> Continuing with neurophysiology. Connectivity &amp; Oscillations.<br /> <br /> ==''Wednesday 11/9/16''==<br /> ===Intracranial Recordings ''Speaker'': [http://neurosurgery.ucla.edu/neuromodulation-neuroimaging-lab Nanthia Suthana]===<br /> <br /> ::[[media: Suthana.pdf|Slides for Intracranial Lecture]]<br /> ::[[media: LachauxIntracranial.pdf|Lachaux et al Reading]]<br /> ::[[media: HarrisIntracranial.pdf|Harris et al Reading]]<br /> <br /> =Week 8 - Practical Neurophysiology=<br /> ==''Monday 11/14/16''==<br /> ===Building an EEG System - Circuit 1 - Going over circuit ''Speaker'': Cameron Rodriguez===<br /> <br /> :*[https://learn.sparkfun.com/tutorials/how-to-solder Through holesoldering].<br /> :*[https://learn.sparkfun.com/tutorials/pcb-basics Printed Circuit Board Basics].<br /> :*[https://learn.sparkfun.com/tutorials/how-to-install-and-setup-eagle Installing and Setting Up Eagle].<br /> :*[https://learn.sparkfun.com/tutorials/using-eagle-schematic Using Eagle Schematic].<br /> :*[https://learn.sparkfun.com/tutorials/using-eagle-board-layoutc Using Eagle Board Layout].<br /> <br /> :*[https://www.sparkfun.com/videos#pete/NJKZZArjdg8 PCB Layout 1 - According to Pete];<br /> :*[https://www.sparkfun.com/videos#pete/JANZsjRiM3w PCB Layout 2 - According to Pete];<br /> <br /> ==''Wednesday 11/16/16''==<br /> ===Data Processing Hands On ''Speaker'': Cameron Rodriguez===<br /> <br /> =Week 9 - Data Acquisition =<br /> ==''Monday 11/21/16 - in C8-177''==<br /> ===Data Acquisition ''Speaker'': Agatha Lenartowicz===<br /> <br /> ==''Wednesday 11/23/16''==<br /> ===Thankgiving Wed - No Class ===<br /> <br /> =Week 10 - Data Processing =<br /> ==''Monday 11/28/16''==<br /> ===Midterm Review, Data Processing ''Speaker'': Cameron Rodriguez/Agatha Lenartowicz===<br /> <br /> ==''Wednesady 11/30/16''==<br /> ===Data Processing &quot;Speaker&quot;: Cameron Rodriguez/Agatha Lenartowicz===<br /> <br /> <br /> =Week 11 - Finals Week=<br /> ==''Monday 11/5/16''==<br /> ===Final Exam===<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!--<br /> SPARE CONTENT / PREVIOUS YEARS<br /> <br /> =Week 3: Stats begins!=<br /> <br /> ==''Monday 10/12/15''==<br /> <br /> ===- Statistical Fundamentals. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> :*[[media: CohenClassIntroStats11_25_13.pdf‎ | Slides used in class (set 1)]]<br /> <br /> ''Review of'':<br /> :*Descriptive Statistics: mean, mode, variance, standard deviation<br /> :*Statistical Inference. The Binomial and Normal Distribution<br /> :*Basic Tests: t-test, linear correlation<br /> :*Modeling and non-linear relations<br /> :*Bayes rule<br /> <br /> <br /> ==''Wednesday 10/14/15''==<br /> ===- Statistical Fundamentals II. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> =Week 4 - Stats and circuits=<br /> ==''Monday 10/19/15''==<br /> ===- Statistical Fundamentals III. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> ==''Wednesday 10/21/15''==<br /> ===Circuits continued: Active devices. [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ===Optics I. ''Speaker'': [mailto:zdeis@seas.ucla.edu Zachary Taylor]===<br /> [[Image:Reflection.jpg|right]]<br /> The overall goal of this lecture is to establish that:<br /> ''- Physical constants have tangible meanings''<br /> ''- Plane waves form a physically unrealizable but extremely good approximation to real systems''<br /> ''- Boundaries bend light''<br /> ''- Physical constants, plane wave mechanics, and boundaries can be used to describe the operation of a lens''<br /> ''- The PSF gives a good indication of the overall performance of an imaging system''<br /> ''- All of these concepts have analogues in other areas of engineering (ie circuits, mechanical vibrations, etc.)''''<br /> <br /> '''Outline:'''<br /> :* Constitutive parameters (ε, μ, η, n, etc.)<br /> :* Plane wave basics<br /> :* Plane waves at boundaries<br /> :* Lenses<br /> :* Advanced imaging properties of lenses<br /> :* Point spread function.<br /> <br /> ''Required Readings''<br /> Zach has very kindly agreed to post his [http://www.brainmapping.org/NITP/PNA/Readings/OpticsTaylor3-10-10.pdf Optics lecture notes].<br /> ''Suggested Further Reading''<br /> <br /> <br /> <br /> <br /> ==''Wednesday 11/19/14''== <br /> ===Optogenetics. ''Speaker'': [http://faculty.neuroscience.ucla.edu/institution/personnel?personnel_id=47031 Tom Otis]===<br /> <br /> <br /> =Week 8: =<br /> ==''Monday 12/1/14 ''==<br /> ===- Statistics for Imaging. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> [[image:BVTradeoff.jpg|right]]<br /> ''Required Readings''<br /> :*[[media: Mumford_stat_modeling.pdf | Statistical Modeling and Inference (pdf)]]<br /> :*[[media: CohenClassSlides12_2_13.pdf | Slides used in class 12/2/14 and 12/4/14]]<br /> <br /> :*The General Linear Model<br /> *Linear Algebra applied to Statistical Solutions<br /> :*Analysis of Variance<br /> <br /> ==''Wednesday 11/20/14 ...''==<br /> === - Statistics for Imaging II. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> *Fixed and Random Effects<br /> *Repeated measures<br /> :*Bonferroni and Other Corrections<br /> *Non-Parametric Methods<br /> *Autocorrelation<br /> *Unknown Distributions<br /> <br /> ''Required Readings''<br /> :*[[media: CohenClassSlides12_2_13.pdf‎ | same as 12/2/14]]<br /> <br /> ''Suggested Further Reading''<br /> <br /> :*[http://ccn.ucla.edu/media/PNI20121121.mp4 Lecture Video - To be Posted Shortly - Please Right Click and Save rather than Stream - Thank you Alan and Edward!]<br /> <br /> =Week 9: =<br /> ==''Monday 11/25/14''==<br /> === - TBD. [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> ==''Wednesday 11/27/14''==<br /> ===- The Organization of the Human Brain. ''Speaker'': [http://ccn.ucla.edu/bmcweb/bmc_bios/SusanBookheimer/ Susan Bookheimer]===<br /> <br /> We will discuss the general organization of the human brain, and the regional specialization of cortical areas. The emphasis will be on understanding principles of organization:<br /> *Phylogenetic Layering<br /> *Functional Specialization<br /> *Principles Divisions of the Brain<br /> *Brain Systems<br /> <br /> ''Required Readings''<br /> :*[http://da.biostr.washington.edu:80/cgi-bin/DA/PageMaster?atlas:NeuroSyllabus+ffpathIndex/Splash^Page^Syllabus+2 Neuroanatomy Programmed Learning]<br /> :*[[media:Cognitive_science_and_neuro_2012.pdf | Slides shown in Class UPDATED 2012/10/17]]<br /> ''Suggested Further Reading''<br /> <br /> :*[[media:PNIA_2012_PS1_Soln.pdf‎ | Solution to Problem Set 1]]<br /> ===- Human Electrophysiology ''Speakers'': [http://greenlab.npih.ucla.edu/ROSTER.html Agatha Lenartowicz], [http://dgsom.healthsciences.ucla.edu/institution/personnel?personnel_id=9140 John Stern]===<br /> <br /> ''Evoked Responses'' - Guest Lecturer: [http://greenlab.npih.ucla.edu/ROSTER.html Jonathan Wynn]<br /> *A look at real EEG data<br /> *Preprocessing:<br /> **filtering<br /> **artifact detection/removal<br /> *averaging<br /> *single events<br /> *interpretation<br /> <br /> ''Clinical EEG'' - Guest Lecturer: [http://dgsom.healthsciences.ucla.edu/institution/personnel?personnel_id=9140 John Stern]<br /> *Normal and Abnormal EEG<br /> *EEG as a marker for brain state<br /> **sleep staging<br /> **alpha and relaxation<br /> *Neurofeedback??? --&gt;</div>

Alenartowicz https://www.ccn.ucla.edu/wiki/index.php?title=Principles_of_Neuroimaging_A_-_2016&diff=3216 2016-11-16T21:26:22Z

<p>Alenartowicz: </p> <hr /> <div>Principles of Neuroimaging A, Fall, 2016 - Class Schedule and Syllabus<br /> <br /> <br /> ==='''Neuroimaging journal Club (''required for NITP certificate'')'''===<br /> Contact: Katherine Lawrence (katherine.E.Lawrence@ucla.edu) or Janelle Liu (janelle.j.liu@ucla.edu), Faculty Sponsor: Jamie Feusner (JFeusner@mednet.ucla.edu)<br /> <br /> ==This schedule ''will'' change!==<br /> <br /> :'''[[Principles_of_Neuroimaging_-_2016-2017 | Back to main course page for Principles of Neuroimaging]]'''<br /> <br /> :'''Coming up next!: [[Principles_of_Neuroimaging_B_-_2017 | M284B Principles of Neuroimaging B]]'''<br /> <br /> =Course Reading=<br /> ===Required Reading===<br /> :'''Signal Processing for Neuroscientists''' by ''Wim van Drongelen''<br /> ::This can be found as a PDF on scribd.com, for a small fee of $8.99<br /> <br /> ===Supplemental Reading===<br /> :'''Matlab for Neuroscientists'''<br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/88212458/Matlab-Matlab-for-Neuroscientists<br /> <br /> :'''Cartoon Guide to Statistics''' <br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/148072668/Cartoon-Guide-to-Statistics<br /> <br /> :'''NOTE:''' if you subscribe for a Scribd account for a day, you can download as many documents as you like for one fee.<br /> <br /> <br /> =Week 1: Orientation to Neuro-imaging, Neurons, Brains=<br /> <br /> ==''Monday 9/26/16'' ==<br /> ===- Images. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284AImages-intro.pdf | Lecture Slides Images]]<br /> <br /> ==''Wednesday 9/28/16''==<br /> ===- Neurons &amp; Signaling. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> In this first class we will review the basics of neurophysiology with an eye towards what signals of brain function might be visible to the neuroimager. We will discuss information coding, energetics, size and time scales.<br /> [[Image:Neurons.jpg|right]]<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:TheActiveBrain.pdf | The Active Brain]]<br /> :*[[media:NeuronFunctionM284-2016_SM.pdf | Neuron function slides shown in class]]<br /> :*[http://www.science.smith.edu/departments/neurosci/courses/bio330/squid.html Videos of giant squid experiments]<br /> ''Suggested Further Reading''<br /> :*[http://ccn.ucla.edu/wiki/images/5/5a/CAVEAT_LECTOR.pdf Caveat Lector - the misuse of neuroimaging]<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Protected/Kosslyn1999.pdf &quot;If Neuroimaging is the Answer, What is the Question?&quot; Kosslyn, 1999]<br /> :*[http://da.biostr.washington.edu:80/cgi-bin/DA/PageMaster?atlas:NeuroSyllabus+ffpathIndex/Splash^Page^Syllabus+2 Neuroanatomy Programmed Learning]<br /> :*[http://www.amazon.com/Fundamental-Neuroscience-Second-Larry-Squire/dp/0126603030 Squire, Fundamentals of Neuroscience]<br /> :*[http://www.amazon.com/Principles-Neural-Science-Eric-Kandel/dp/0838577016 Kandel, et al., &quot;Principles of Neural Science&quot;]<br /> :This paper, by Malhi, is a nice orientation in methods of neuroimaging. *[http://www.ccn.ucla.edu/wiki/images/f/f2/Malhi2007.pdf Making sense of neuroimaging in psychiatry]<br /> :*[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1359308/pdf/jphysiol01232-0142.pdf Replacement of the axoplasm of giant nerve fibres with artificial solutions]<br /> <br /> =Week 2: Linear Systems, Convolution, Fourier Transforms=<br /> ==''Wednesday 10/3/16''==<br /> <br /> ===- Linear Systems I. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ==''Wednesday 10/5/16''==<br /> ===- Linear Systems II. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> Why the emphasis on Linear Systems? Because they are actually ''easy'' (as compared to non-linear systems, which are not.) As we go through this course, we will see many ways in which linear systems theory is applied to:<br /> :Modeling of Neural Systems<br /> :Extraction of Signal from Noise<br /> :Design of Circuits<br /> :Image Enhancement<br /> :Understanding of Image artifacts, and others.<br /> <br /> Linear systems analysis is one of the great technologies of the 20th and 21st century. It is now the basis for virtually all electronics design, and its extension into the discrete (digital) domain is the basis for most of modern signal processing. <br /> <br /> In our specific case, we will use these few basic principles of linear systems to understand both the instruments we use and the neuroimaging signals we collect. When you have mastered this material, you should be in a much better position to model the systems that you study in order to develop an approach to studying them.<br /> <br /> Here is [http://www.brainmapping.org/NITP/PNA/Readings/ImaginaryNumbers.pdf A primer I wrote on imaginary numbers] that might be a helpful review.<br /> <br /> There is a nice [http://en.wikibooks.org/wiki/Calculus Wikibook on Calculus].<br /> <br /> ''Required Readings''<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapter 1<br /> :*[[media:MathematicalTools2016.pdf | Mathematical Tools]]<br /> <br /> ''Suggested Further Reading''<br /> :'''Introduction to matlab'''<br /> <br /> ''Slides shown in class''<br /> :[[media:LinearityM284A-2016.pdf | Linearity and the Fourier Transform]]<br /> <br /> Please see [http://www.brainmapping.org/NITP/PNA/html/Linearity.html MATLAB linearity demo]<br /> <br /> If you are the type who sees beauty in mathematics, the Euler identity may be one of the most beautiful pieces of math in the world.<br /> <br /> =Week 3: Math &amp; Circuits I=<br /> <br /> ==''Monday 10/10/16''==<br /> === - Circuits I. Speaker: Cameron Rodriguez ===<br /> :*[[media:Circuits_2016.pdf | Circuit slides shown in class]]<br /> <br /> ==''Wednesday 10/12/16''==<br /> === - Circuits II. ''Speaker'': Cameron Rodriguez ===<br /> <br /> Why circuits?<br /> :(Virtually) Every device you use in your research is electronic. You access your primary data only indirectly<br /> :The device you ''really'' want in your lab doesn't exist. You very well may have to make it.<br /> :There are electronic analogs to most of the linear systems that you have so far studied (and ''vice versa'' - the tools you now understand can be used to analyze and predict circuit behavior).<br /> <br /> :If you have not had any of this background, you might want to have a look at this handout, [[Media:Electricity.pdf|Electrical Circuits]], in advance. There are near infinite numbers of resources on the web that cover similar material (near enough to infinite that by the time you read all of them, there would be a whole new set.) I have recently come across a link to [http://www.allaboutcircuits.com/ Online Books: All About Circuits] ''IF'' you want practical hands-on knowledge about this material, my all-time favorite text is [http://www.google.com/products/catalog?hl=en&amp;client=safari&amp;rls=en-us&amp;ei=uVSPSfaxE5nMsAPf-tmSCQ&amp;resnum=1&amp;q=art+of+electronics&amp;um=1&amp;ie=UTF-8&amp;cid=8820839049329255765#ps-sellers &quot;Horowitz and Hill: ''The Art of Electronics.''&quot;] The latest edition, however, is dated 1989 and a new third edition is promised. I have therefore stopped short of recommending a purchase unless your need to make circuits is immediate. In this book, you will find an excellent education on the fundamental principles of electrical circuits and an incredible compendium of practical data, such as how to assemble circuit boards, how to make measurements, etc...) <br /> <br /> Readings:<br /> :*[http://www.ti.com/lit/an/sloa093/sloa093.pdf Filter Design in 30 Seconds]<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapter 2 and 10<br /> :*[https://www.circuitlab.com/ Circuit Lab ] A Free Circuit Web Base Simulator<br /> **You may or may not find this comprehensible without chapters 5 through 9.<br /> <br /> We will discuss:<br /> :*Passive Circuit Elements: Resistors, Capacitors, Inductors<br /> :*Gain<br /> :*Transformers<br /> :*Rectifiers<br /> :*Active Elements<br /> ::- ''Amplifiers''<br /> ::- ''Transistors''<br /> ::- ''Op Amps''<br /> :*Solutions with Matrices<br /> ''Suggested Further Reading''<br /> :*[http://video.google.com/videoplay?docid=5645396659673218353&amp;q=Physics+for+Future+Presidents+Electricity&amp;total=5&amp;start=0&amp;num=10&amp;so=0&amp;type=search&amp;plindex=0#0h20m30s Video intro lecture on charge, current and voltage].<br /> :*[[media:Electricity_Basics.pdf‎ | Well organized text on electrical concepts by Tony R. Kuphaldt]]<br /> :*[http://en.wikibooks.org/wiki/Circuit_Theory Circuit Theory - Wikibook]<br /> <br /> '''Suggested, Optional Readings from [http://www.dspguide.com DSPguide.com]:'''<br /> :*[http://www.dspguide.com/CH5.PDF Linear Systems]<br /> :*[http://www.dspguide.com/CH6.PDF Convolution]<br /> :*[http://www.dspguide.com/CH8.PDF Discrete Fourier Transform (DFT)]<br /> :''Note: These chapters are light on math and try to focus on a conceptual understanding''<br /> <br /> Time and Frequency / Spectral Filters<br /> <br /> ----<br /> Practice using the Fourier transform:<br /> :[http://www.brainmapping.org/NITP/PNA/ConvFThtml/ConvolutionWorksheet.pdf Fourier transform and Convolution Worksheet]. &lt;!-- [http://www.brainmapping.org/NITP/PNA/ConvFThtml/ConvFT.html (''Solutions'').] --&gt;<br /> :[http://www.brainmapping.org/NITP/PNA/ConvFThtml/Something.wav Sound file for worksheet above.]<br /> <br /> Other<br /> :*[http://www.youtube.com/watch?feature=player_embedded&amp;v=7uHfjpU3OH0#! Getting Started with Arduino]<br /> <br /> =Week 4 Signal Processing=<br /> <br /> ==''Monday 10/17/16''==<br /> === - Signal Processing. ''Speaker'': Cameron Rodriguez ===<br /> <br /> :*[[media:Signals1-PreProcessing1_2016.pdf | Signal Pre Processing Slide Set 1 shown in class]]<br /> <br /> <br /> ==''Wednesday 10/19/16''==<br /> === - Noise ''Speaker'': Cameron Rodriguez ===<br /> <br /> :*[[media:OneD_signal_Filter_Ex.pdf | Matlab Signal Tutorial printout]]<br /> <br /> It is what you ''don't'' want - usually - but things change in quantized systems<br /> :Additive noise<br /> :White Noise<br /> :Boltzmann noise<br /> :Colored Noise<br /> :Gaussian Noise<br /> :Coherent noise<br /> :Sampling Errors<br /> :Aliasing<br /> :Quantization noise<br /> :Spectral filtering<br /> <br /> Noise comes in all shapes and colors. It is present in every measurement we make, from an EEG voltage to an estimate of the effects of dopamine on forebrain signal. Our best weapons are an understanding of the statistical properties of noise, the sources of noise and the ways to control it. Noise in the discrete digital domain is special, as it is both ''created'' by digitization and amplified by sampling.<br /> <br /> Readings:<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapters 2 through 4<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Noise.pdf Noise Slides]<br /> <br /> =Week 5 - Information &amp; Statistical Theory =<br /> <br /> ==''Monday 10/24/16 - in C8-177''==<br /> === Noise Cont' &amp; Information Theory Speakers: Cameron Rodriguez &amp; John Villasenor===<br /> <br /> ==''Wednesday 10/26/16''==<br /> ===Statistical Theory ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> We will consider the general problems of statistical inference, with a concentration on developing an intuitive understanding of statistical concepts.<br /> [[Image:MeasureForMeasure.jpg|right]]<br /> <br /> Suggested reading<br /> :*[http://www.statsoft.com/textbook/stbasic.html Statsoft online text (''free'')]<br /> :*[http://www.amazon.com/Cartoon-Guide-Statistics-Larry-Gonick/dp/0062731025 The Cartoon Guide to Statistics - Gonick $17.95 new]<br /> :The latter teaches stats at what I feel to be the right level - developing intuitions about the kinds of questions that can be answered using stats and about the statistical tests and measures<br /> <br /> :'''Problem Set 3 - Statistics in matlab'''<br /> ::[[media: Problem_Set_1.doc|Problem set using stats and MATLAB]]<br /> ::[[media: Problem_Set_1B.doc|More practice with stats and MATLAB]]<br /> <br /> <br /> =MIDTERM=<br /> <br /> <br /> =Week 6 - Neurophysiology=<br /> ==''Monday 10/31/16''==<br /> === Electrophysiology I ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> We will examine our first imaging modality, EEG (and MEG).<br /> <br /> ::[[media: EEGNeurophys_161102.pdf|Slides for Neurophysiology Lectures]]<br /> <br /> ==''Wednesday 11/2/16''==<br /> === Electrophysiology II ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> Continuing with neurophysiology. ERPs &amp; Source Localization.<br /> <br /> =Week 7 - Neurophysiology =<br /> ==''Monday 11/7/16''==<br /> === Electrophysiology III ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> Continuing with neurophysiology. Connectivity &amp; Oscillations.<br /> <br /> ==''Wednesday 11/9/16''==<br /> ===Intracranial Recordings ''Speaker'': [http://neurosurgery.ucla.edu/neuromodulation-neuroimaging-lab Nanthia Suthana]===<br /> <br /> ::[[media: Suthana.pdf|Slides for Intracranial Lecture]]<br /> ::[[media: LachauxIntracranial.pdf|Lachaux et al Reading]]<br /> ::[[media: HarrisIntracranial.pdf|Harris et al Reading]]<br /> <br /> =Week 8 - Practical Neurophysiology=<br /> ==''Monday 11/14/16''==<br /> ===Building an EEG System - Circuit 1 - Going over circuit ''Speaker'': Cameron Rodriguez===<br /> <br /> :*[https://learn.sparkfun.com/tutorials/how-to-solder Through holesoldering].<br /> :*[https://learn.sparkfun.com/tutorials/pcb-basics Printed Circuit Board Basics].<br /> :*[https://learn.sparkfun.com/tutorials/how-to-install-and-setup-eagle Installing and Setting Up Eagle].<br /> :*[https://learn.sparkfun.com/tutorials/using-eagle-schematic Using Eagle Schematic].<br /> :*[https://learn.sparkfun.com/tutorials/using-eagle-board-layoutc Using Eagle Board Layout].<br /> <br /> :*[https://www.sparkfun.com/videos#pete/NJKZZArjdg8 PCB Layout 1 - According to Pete];<br /> :*[https://www.sparkfun.com/videos#pete/JANZsjRiM3w PCB Layout 2 - According to Pete];<br /> <br /> ==''Wednesday 11/16/16''==<br /> ===Data Processing Hands On ''Speaker'': Cameron Rodriguez===<br /> <br /> =Week 9 - Magnetic Resonance Fundamentals =<br /> ==''Monday 11/21/16 - in C8-177''==<br /> ===Data Acquisition ''Speaker'': Agatha Lenartowicz===<br /> <br /> ==''Wednesday 11/23/16''==<br /> ===Thankgiving Wed - No Class ===<br /> <br /> <br /> =Week 10 - Data Processing =<br /> ==''Monday 11/28/16''==<br /> ===Midterm Review, Data Processing ''Speaker'': Cameron Rodriguez/Agatha Lenartowicz===<br /> <br /> ==''Wednesady 11/30/16''==<br /> ===Data Processing &quot;Speaker&quot;: Cameron Rodriguez/Agatha Lenartowicz===<br /> <br /> <br /> =Week 11 - Finals Week=<br /> ==''Monday 11/5/16''==<br /> ===Final Exam===<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!--<br /> SPARE CONTENT / PREVIOUS YEARS<br /> <br /> =Week 3: Stats begins!=<br /> <br /> ==''Monday 10/12/15''==<br /> <br /> ===- Statistical Fundamentals. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> :*[[media: CohenClassIntroStats11_25_13.pdf‎ | Slides used in class (set 1)]]<br /> <br /> ''Review of'':<br /> :*Descriptive Statistics: mean, mode, variance, standard deviation<br /> :*Statistical Inference. The Binomial and Normal Distribution<br /> :*Basic Tests: t-test, linear correlation<br /> :*Modeling and non-linear relations<br /> :*Bayes rule<br /> <br /> <br /> ==''Wednesday 10/14/15''==<br /> ===- Statistical Fundamentals II. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> =Week 4 - Stats and circuits=<br /> ==''Monday 10/19/15''==<br /> ===- Statistical Fundamentals III. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> ==''Wednesday 10/21/15''==<br /> ===Circuits continued: Active devices. [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ===Optics I. ''Speaker'': [mailto:zdeis@seas.ucla.edu Zachary Taylor]===<br /> [[Image:Reflection.jpg|right]]<br /> The overall goal of this lecture is to establish that:<br /> ''- Physical constants have tangible meanings''<br /> ''- Plane waves form a physically unrealizable but extremely good approximation to real systems''<br /> ''- Boundaries bend light''<br /> ''- Physical constants, plane wave mechanics, and boundaries can be used to describe the operation of a lens''<br /> ''- The PSF gives a good indication of the overall performance of an imaging system''<br /> ''- All of these concepts have analogues in other areas of engineering (ie circuits, mechanical vibrations, etc.)''''<br /> <br /> '''Outline:'''<br /> :* Constitutive parameters (ε, μ, η, n, etc.)<br /> :* Plane wave basics<br /> :* Plane waves at boundaries<br /> :* Lenses<br /> :* Advanced imaging properties of lenses<br /> :* Point spread function.<br /> <br /> ''Required Readings''<br /> Zach has very kindly agreed to post his [http://www.brainmapping.org/NITP/PNA/Readings/OpticsTaylor3-10-10.pdf Optics lecture notes].<br /> ''Suggested Further Reading''<br /> <br /> <br /> <br /> <br /> ==''Wednesday 11/19/14''== <br /> ===Optogenetics. ''Speaker'': [http://faculty.neuroscience.ucla.edu/institution/personnel?personnel_id=47031 Tom Otis]===<br /> <br /> <br /> =Week 8: =<br /> ==''Monday 12/1/14 ''==<br /> ===- Statistics for Imaging. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> [[image:BVTradeoff.jpg|right]]<br /> ''Required Readings''<br /> :*[[media: Mumford_stat_modeling.pdf | Statistical Modeling and Inference (pdf)]]<br /> :*[[media: CohenClassSlides12_2_13.pdf | Slides used in class 12/2/14 and 12/4/14]]<br /> <br /> :*The General Linear Model<br /> *Linear Algebra applied to Statistical Solutions<br /> :*Analysis of Variance<br /> <br /> ==''Wednesday 11/20/14 ...''==<br /> === - Statistics for Imaging II. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> *Fixed and Random Effects<br /> *Repeated measures<br /> :*Bonferroni and Other Corrections<br /> *Non-Parametric Methods<br /> *Autocorrelation<br /> *Unknown Distributions<br /> <br /> ''Required Readings''<br /> :*[[media: CohenClassSlides12_2_13.pdf‎ | same as 12/2/14]]<br /> <br /> ''Suggested Further Reading''<br /> <br /> :*[http://ccn.ucla.edu/media/PNI20121121.mp4 Lecture Video - To be Posted Shortly - Please Right Click and Save rather than Stream - Thank you Alan and Edward!]<br /> <br /> =Week 9: =<br /> ==''Monday 11/25/14''==<br /> === - TBD. [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> ==''Wednesday 11/27/14''==<br /> ===- The Organization of the Human Brain. ''Speaker'': [http://ccn.ucla.edu/bmcweb/bmc_bios/SusanBookheimer/ Susan Bookheimer]===<br /> <br /> We will discuss the general organization of the human brain, and the regional specialization of cortical areas. The emphasis will be on understanding principles of organization:<br /> *Phylogenetic Layering<br /> *Functional Specialization<br /> *Principles Divisions of the Brain<br /> *Brain Systems<br /> <br /> ''Required Readings''<br /> :*[http://da.biostr.washington.edu:80/cgi-bin/DA/PageMaster?atlas:NeuroSyllabus+ffpathIndex/Splash^Page^Syllabus+2 Neuroanatomy Programmed Learning]<br /> :*[[media:Cognitive_science_and_neuro_2012.pdf | Slides shown in Class UPDATED 2012/10/17]]<br /> ''Suggested Further Reading''<br /> <br /> :*[[media:PNIA_2012_PS1_Soln.pdf‎ | Solution to Problem Set 1]]<br /> ===- Human Electrophysiology ''Speakers'': [http://greenlab.npih.ucla.edu/ROSTER.html Agatha Lenartowicz], [http://dgsom.healthsciences.ucla.edu/institution/personnel?personnel_id=9140 John Stern]===<br /> <br /> ''Evoked Responses'' - Guest Lecturer: [http://greenlab.npih.ucla.edu/ROSTER.html Jonathan Wynn]<br /> *A look at real EEG data<br /> *Preprocessing:<br /> **filtering<br /> **artifact detection/removal<br /> *averaging<br /> *single events<br /> *interpretation<br /> <br /> ''Clinical EEG'' - Guest Lecturer: [http://dgsom.healthsciences.ucla.edu/institution/personnel?personnel_id=9140 John Stern]<br /> *Normal and Abnormal EEG<br /> *EEG as a marker for brain state<br /> **sleep staging<br /> **alpha and relaxation<br /> *Neurofeedback??? --&gt;</div>

Alenartowicz https://www.ccn.ucla.edu/wiki/index.php?title=Principles_of_Neuroimaging_A_-_2016&diff=3215 2016-11-14T03:55:02Z

<p>Alenartowicz: /* Intracranial Recordings Speaker: Nanthia Sultana */</p> <hr /> <div>Principles of Neuroimaging A, Fall, 2016 - Class Schedule and Syllabus<br /> <br /> <br /> ==='''Neuroimaging journal Club (''required for NITP certificate'')'''===<br /> Contact: Katherine Lawrence (katherine.E.Lawrence@ucla.edu) or Janelle Liu (janelle.j.liu@ucla.edu), Faculty Sponsor: Jamie Feusner (JFeusner@mednet.ucla.edu)<br /> <br /> ==This schedule ''will'' change!==<br /> <br /> :'''[[Principles_of_Neuroimaging_-_2016-2017 | Back to main course page for Principles of Neuroimaging]]'''<br /> <br /> :'''Coming up next!: [[Principles_of_Neuroimaging_B_-_2017 | M284B Principles of Neuroimaging B]]'''<br /> <br /> =Course Reading=<br /> ===Required Reading===<br /> :'''Signal Processing for Neuroscientists''' by ''Wim van Drongelen''<br /> ::This can be found as a PDF on scribd.com, for a small fee of $8.99<br /> <br /> ===Supplemental Reading===<br /> :'''Matlab for Neuroscientists'''<br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/88212458/Matlab-Matlab-for-Neuroscientists<br /> <br /> :'''Cartoon Guide to Statistics''' <br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/148072668/Cartoon-Guide-to-Statistics<br /> <br /> :'''NOTE:''' if you subscribe for a Scribd account for a day, you can download as many documents as you like for one fee.<br /> <br /> <br /> =Week 1: Orientation to Neuro-imaging, Neurons, Brains=<br /> <br /> ==''Monday 9/26/16'' ==<br /> ===- Images. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284AImages-intro.pdf | Lecture Slides Images]]<br /> <br /> ==''Wednesday 9/28/16''==<br /> ===- Neurons &amp; Signaling. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> In this first class we will review the basics of neurophysiology with an eye towards what signals of brain function might be visible to the neuroimager. We will discuss information coding, energetics, size and time scales.<br /> [[Image:Neurons.jpg|right]]<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:TheActiveBrain.pdf | The Active Brain]]<br /> :*[[media:NeuronFunctionM284-2016_SM.pdf | Neuron function slides shown in class]]<br /> :*[http://www.science.smith.edu/departments/neurosci/courses/bio330/squid.html Videos of giant squid experiments]<br /> ''Suggested Further Reading''<br /> :*[http://ccn.ucla.edu/wiki/images/5/5a/CAVEAT_LECTOR.pdf Caveat Lector - the misuse of neuroimaging]<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Protected/Kosslyn1999.pdf &quot;If Neuroimaging is the Answer, What is the Question?&quot; Kosslyn, 1999]<br /> :*[http://da.biostr.washington.edu:80/cgi-bin/DA/PageMaster?atlas:NeuroSyllabus+ffpathIndex/Splash^Page^Syllabus+2 Neuroanatomy Programmed Learning]<br /> :*[http://www.amazon.com/Fundamental-Neuroscience-Second-Larry-Squire/dp/0126603030 Squire, Fundamentals of Neuroscience]<br /> :*[http://www.amazon.com/Principles-Neural-Science-Eric-Kandel/dp/0838577016 Kandel, et al., &quot;Principles of Neural Science&quot;]<br /> :This paper, by Malhi, is a nice orientation in methods of neuroimaging. *[http://www.ccn.ucla.edu/wiki/images/f/f2/Malhi2007.pdf Making sense of neuroimaging in psychiatry]<br /> :*[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1359308/pdf/jphysiol01232-0142.pdf Replacement of the axoplasm of giant nerve fibres with artificial solutions]<br /> <br /> =Week 2: Linear Systems, Convolution, Fourier Transforms=<br /> ==''Wednesday 10/3/16''==<br /> <br /> ===- Linear Systems I. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ==''Wednesday 10/5/16''==<br /> ===- Linear Systems II. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> Why the emphasis on Linear Systems? Because they are actually ''easy'' (as compared to non-linear systems, which are not.) As we go through this course, we will see many ways in which linear systems theory is applied to:<br /> :Modeling of Neural Systems<br /> :Extraction of Signal from Noise<br /> :Design of Circuits<br /> :Image Enhancement<br /> :Understanding of Image artifacts, and others.<br /> <br /> Linear systems analysis is one of the great technologies of the 20th and 21st century. It is now the basis for virtually all electronics design, and its extension into the discrete (digital) domain is the basis for most of modern signal processing. <br /> <br /> In our specific case, we will use these few basic principles of linear systems to understand both the instruments we use and the neuroimaging signals we collect. When you have mastered this material, you should be in a much better position to model the systems that you study in order to develop an approach to studying them.<br /> <br /> Here is [http://www.brainmapping.org/NITP/PNA/Readings/ImaginaryNumbers.pdf A primer I wrote on imaginary numbers] that might be a helpful review.<br /> <br /> There is a nice [http://en.wikibooks.org/wiki/Calculus Wikibook on Calculus].<br /> <br /> ''Required Readings''<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapter 1<br /> :*[[media:MathematicalTools2016.pdf | Mathematical Tools]]<br /> <br /> ''Suggested Further Reading''<br /> :'''Introduction to matlab'''<br /> <br /> ''Slides shown in class''<br /> :[[media:LinearityM284A-2016.pdf | Linearity and the Fourier Transform]]<br /> <br /> Please see [http://www.brainmapping.org/NITP/PNA/html/Linearity.html MATLAB linearity demo]<br /> <br /> If you are the type who sees beauty in mathematics, the Euler identity may be one of the most beautiful pieces of math in the world.<br /> <br /> =Week 3: Math &amp; Circuits I=<br /> <br /> ==''Monday 10/10/16''==<br /> === - Circuits I. Speaker: Cameron Rodriguez ===<br /> :*[[media:Circuits_2016.pdf | Circuit slides shown in class]]<br /> <br /> ==''Wednesday 10/12/16''==<br /> === - Circuits II. ''Speaker'': Cameron Rodriguez ===<br /> <br /> Why circuits?<br /> :(Virtually) Every device you use in your research is electronic. You access your primary data only indirectly<br /> :The device you ''really'' want in your lab doesn't exist. You very well may have to make it.<br /> :There are electronic analogs to most of the linear systems that you have so far studied (and ''vice versa'' - the tools you now understand can be used to analyze and predict circuit behavior).<br /> <br /> :If you have not had any of this background, you might want to have a look at this handout, [[Media:Electricity.pdf|Electrical Circuits]], in advance. There are near infinite numbers of resources on the web that cover similar material (near enough to infinite that by the time you read all of them, there would be a whole new set.) I have recently come across a link to [http://www.allaboutcircuits.com/ Online Books: All About Circuits] ''IF'' you want practical hands-on knowledge about this material, my all-time favorite text is [http://www.google.com/products/catalog?hl=en&amp;client=safari&amp;rls=en-us&amp;ei=uVSPSfaxE5nMsAPf-tmSCQ&amp;resnum=1&amp;q=art+of+electronics&amp;um=1&amp;ie=UTF-8&amp;cid=8820839049329255765#ps-sellers &quot;Horowitz and Hill: ''The Art of Electronics.''&quot;] The latest edition, however, is dated 1989 and a new third edition is promised. I have therefore stopped short of recommending a purchase unless your need to make circuits is immediate. In this book, you will find an excellent education on the fundamental principles of electrical circuits and an incredible compendium of practical data, such as how to assemble circuit boards, how to make measurements, etc...) <br /> <br /> Readings:<br /> :*[http://www.ti.com/lit/an/sloa093/sloa093.pdf Filter Design in 30 Seconds]<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapter 2 and 10<br /> :*[https://www.circuitlab.com/ Circuit Lab ] A Free Circuit Web Base Simulator<br /> **You may or may not find this comprehensible without chapters 5 through 9.<br /> <br /> We will discuss:<br /> :*Passive Circuit Elements: Resistors, Capacitors, Inductors<br /> :*Gain<br /> :*Transformers<br /> :*Rectifiers<br /> :*Active Elements<br /> ::- ''Amplifiers''<br /> ::- ''Transistors''<br /> ::- ''Op Amps''<br /> :*Solutions with Matrices<br /> ''Suggested Further Reading''<br /> :*[http://video.google.com/videoplay?docid=5645396659673218353&amp;q=Physics+for+Future+Presidents+Electricity&amp;total=5&amp;start=0&amp;num=10&amp;so=0&amp;type=search&amp;plindex=0#0h20m30s Video intro lecture on charge, current and voltage].<br /> :*[[media:Electricity_Basics.pdf‎ | Well organized text on electrical concepts by Tony R. Kuphaldt]]<br /> :*[http://en.wikibooks.org/wiki/Circuit_Theory Circuit Theory - Wikibook]<br /> <br /> '''Suggested, Optional Readings from [http://www.dspguide.com DSPguide.com]:'''<br /> :*[http://www.dspguide.com/CH5.PDF Linear Systems]<br /> :*[http://www.dspguide.com/CH6.PDF Convolution]<br /> :*[http://www.dspguide.com/CH8.PDF Discrete Fourier Transform (DFT)]<br /> :''Note: These chapters are light on math and try to focus on a conceptual understanding''<br /> <br /> Time and Frequency / Spectral Filters<br /> <br /> ----<br /> Practice using the Fourier transform:<br /> :[http://www.brainmapping.org/NITP/PNA/ConvFThtml/ConvolutionWorksheet.pdf Fourier transform and Convolution Worksheet]. &lt;!-- [http://www.brainmapping.org/NITP/PNA/ConvFThtml/ConvFT.html (''Solutions'').] --&gt;<br /> :[http://www.brainmapping.org/NITP/PNA/ConvFThtml/Something.wav Sound file for worksheet above.]<br /> <br /> Other<br /> :*[http://www.youtube.com/watch?feature=player_embedded&amp;v=7uHfjpU3OH0#! Getting Started with Arduino]<br /> <br /> =Week 4 Signal Processing=<br /> <br /> ==''Monday 10/17/16''==<br /> === - Signal Processing. ''Speaker'': Cameron Rodriguez ===<br /> <br /> :*[[media:Signals1-PreProcessing1_2016.pdf | Signal Pre Processing Slide Set 1 shown in class]]<br /> <br /> <br /> ==''Wednesday 10/19/16''==<br /> === - Noise ''Speaker'': Cameron Rodriguez ===<br /> <br /> :*[[media:OneD_signal_Filter_Ex.pdf | Matlab Signal Tutorial printout]]<br /> <br /> It is what you ''don't'' want - usually - but things change in quantized systems<br /> :Additive noise<br /> :White Noise<br /> :Boltzmann noise<br /> :Colored Noise<br /> :Gaussian Noise<br /> :Coherent noise<br /> :Sampling Errors<br /> :Aliasing<br /> :Quantization noise<br /> :Spectral filtering<br /> <br /> Noise comes in all shapes and colors. It is present in every measurement we make, from an EEG voltage to an estimate of the effects of dopamine on forebrain signal. Our best weapons are an understanding of the statistical properties of noise, the sources of noise and the ways to control it. Noise in the discrete digital domain is special, as it is both ''created'' by digitization and amplified by sampling.<br /> <br /> Readings:<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapters 2 through 4<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Noise.pdf Noise Slides]<br /> <br /> =Week 5 - Information &amp; Statistical Theory =<br /> <br /> ==''Monday 10/24/16 - in C8-177''==<br /> === Noise Cont' &amp; Information Theory Speakers: Cameron Rodriguez &amp; John Villasenor===<br /> <br /> ==''Wednesday 10/26/16''==<br /> ===Statistical Theory ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> We will consider the general problems of statistical inference, with a concentration on developing an intuitive understanding of statistical concepts.<br /> [[Image:MeasureForMeasure.jpg|right]]<br /> <br /> Suggested reading<br /> :*[http://www.statsoft.com/textbook/stbasic.html Statsoft online text (''free'')]<br /> :*[http://www.amazon.com/Cartoon-Guide-Statistics-Larry-Gonick/dp/0062731025 The Cartoon Guide to Statistics - Gonick $17.95 new]<br /> :The latter teaches stats at what I feel to be the right level - developing intuitions about the kinds of questions that can be answered using stats and about the statistical tests and measures<br /> <br /> :'''Problem Set 3 - Statistics in matlab'''<br /> ::[[media: Problem_Set_1.doc|Problem set using stats and MATLAB]]<br /> ::[[media: Problem_Set_1B.doc|More practice with stats and MATLAB]]<br /> <br /> <br /> =MIDTERM=<br /> <br /> <br /> =Week 6 - Neurophysiology=<br /> ==''Monday 10/31/16''==<br /> === Electrophysiology I ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> We will examine our first imaging modality, EEG (and MEG).<br /> <br /> ::[[media: EEGNeurophys_161102.pdf|Slides for Neurophysiology Lectures]]<br /> <br /> ==''Wednesday 11/2/16''==<br /> === Electrophysiology II ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> Continuing with neurophysiology. ERPs &amp; Source Localization.<br /> <br /> =Week 7 - Neurophysiology =<br /> ==''Monday 11/7/16''==<br /> === Electrophysiology III ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> Continuing with neurophysiology. Connectivity &amp; Oscillations.<br /> <br /> ==''Wednesday 11/9/16''==<br /> ===Intracranial Recordings ''Speaker'': [http://neurosurgery.ucla.edu/neuromodulation-neuroimaging-lab Nanthia Suthana]===<br /> <br /> ::[[media: Suthana.pdf|Slides for Intracranial Lecture]]<br /> ::[[media: LachauxIntracranial.pdf|Lachaux et al Reading]]<br /> ::[[media: HarrisIntracranial.pdf|Harris et al Reading]]<br /> <br /> =Week 8 - Practical Neurophysiology=<br /> ==''Monday 11/14/16''==<br /> ===Building an EEG System - Circuit 1 ''Speaker'': Cameron Rodriguez===<br /> <br /> ==''Wednesday 11/16/16''==<br /> ===Building an EEG System - Circuit 2 ''Speaker'': Cameron Rodriguez===<br /> <br /> :*[https://learn.sparkfun.com/tutorials/how-to-solder Through holesoldering].<br /> :*[https://learn.sparkfun.com/tutorials/pcb-basics Printed Circuit Board Basics].<br /> :*[https://learn.sparkfun.com/tutorials/how-to-install-and-setup-eagle Installing and Setting Up Eagle].<br /> :*[https://learn.sparkfun.com/tutorials/using-eagle-schematic Using Eagle Schematic].<br /> :*[https://learn.sparkfun.com/tutorials/using-eagle-board-layoutc Using Eagle Board Layout].<br /> <br /> :*[https://www.sparkfun.com/videos#pete/NJKZZArjdg8 PCB Layout 1 - According to Pete];<br /> :*[https://www.sparkfun.com/videos#pete/JANZsjRiM3w PCB Layout 2 - According to Pete];<br /> <br /> <br /> =Week 9 - Magnetic Resonance Fundamentals =<br /> ==''Monday 11/21/16 - in C8-177''==<br /> ===MR Signal Origin ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ==''Wednesday 11/23/16''==<br /> ===Thankgiving Wed - No Class ===<br /> <br /> Magnetic Resonance Imaging (MRI) is probably the most influential and most flexible current means of imaging the human brain. It features a vast number of separable contrast mechanisms, and a near ideal combination of non-invasiveness, safety, resolution and metric accuracy. However, it is extraordinarily expensive and has limited temporal resolution, especially for functional studies<br /> <br /> OUTLINE<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> :*[http://www.cis.rit.edu/htbooks/mri/ These notes] by Joseph Hornak are highly professional and complete coverage of MRI.<br /> :*[http://www.imaios.com/en/e-Courses/e-MRI eMRI] is another excellent online MRI learning resource<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Hahn1950.pdf Erwin Hahn - Spin Echoes: ''Essential reading for the MRI community'']<br /> [[Image:HahnFig1.png]]<br /> ''above'': Figure 1 from Hahn, 1950<br /> <br /> ''Suggested Further Reading''<br /> [[image:PSatSeq.jpg|right]]<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> <br /> =Week 10 - Magnetic Resonance Fundamentals =<br /> ==''Monday 11/28/16''==<br /> ===MR Physics ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ==''Wednesady 11/30/16''==<br /> ===TBD/Q&amp;A===<br /> <br /> <br /> =Week 11 - Finals Week=<br /> ==''Monday 11/5/16''==<br /> ===Final Exam===<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!--<br /> SPARE CONTENT / PREVIOUS YEARS<br /> <br /> =Week 3: Stats begins!=<br /> <br /> ==''Monday 10/12/15''==<br /> <br /> ===- Statistical Fundamentals. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> :*[[media: CohenClassIntroStats11_25_13.pdf‎ | Slides used in class (set 1)]]<br /> <br /> ''Review of'':<br /> :*Descriptive Statistics: mean, mode, variance, standard deviation<br /> :*Statistical Inference. The Binomial and Normal Distribution<br /> :*Basic Tests: t-test, linear correlation<br /> :*Modeling and non-linear relations<br /> :*Bayes rule<br /> <br /> <br /> ==''Wednesday 10/14/15''==<br /> ===- Statistical Fundamentals II. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> =Week 4 - Stats and circuits=<br /> ==''Monday 10/19/15''==<br /> ===- Statistical Fundamentals III. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> ==''Wednesday 10/21/15''==<br /> ===Circuits continued: Active devices. [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ===Optics I. ''Speaker'': [mailto:zdeis@seas.ucla.edu Zachary Taylor]===<br /> [[Image:Reflection.jpg|right]]<br /> The overall goal of this lecture is to establish that:<br /> ''- Physical constants have tangible meanings''<br /> ''- Plane waves form a physically unrealizable but extremely good approximation to real systems''<br /> ''- Boundaries bend light''<br /> ''- Physical constants, plane wave mechanics, and boundaries can be used to describe the operation of a lens''<br /> ''- The PSF gives a good indication of the overall performance of an imaging system''<br /> ''- All of these concepts have analogues in other areas of engineering (ie circuits, mechanical vibrations, etc.)''''<br /> <br /> '''Outline:'''<br /> :* Constitutive parameters (ε, μ, η, n, etc.)<br /> :* Plane wave basics<br /> :* Plane waves at boundaries<br /> :* Lenses<br /> :* Advanced imaging properties of lenses<br /> :* Point spread function.<br /> <br /> ''Required Readings''<br /> Zach has very kindly agreed to post his [http://www.brainmapping.org/NITP/PNA/Readings/OpticsTaylor3-10-10.pdf Optics lecture notes].<br /> ''Suggested Further Reading''<br /> <br /> <br /> <br /> <br /> ==''Wednesday 11/19/14''== <br /> ===Optogenetics. ''Speaker'': [http://faculty.neuroscience.ucla.edu/institution/personnel?personnel_id=47031 Tom Otis]===<br /> <br /> <br /> =Week 8: =<br /> ==''Monday 12/1/14 ''==<br /> ===- Statistics for Imaging. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> [[image:BVTradeoff.jpg|right]]<br /> ''Required Readings''<br /> :*[[media: Mumford_stat_modeling.pdf | Statistical Modeling and Inference (pdf)]]<br /> :*[[media: CohenClassSlides12_2_13.pdf | Slides used in class 12/2/14 and 12/4/14]]<br /> <br /> :*The General Linear Model<br /> *Linear Algebra applied to Statistical Solutions<br /> :*Analysis of Variance<br /> <br /> ==''Wednesday 11/20/14 ...''==<br /> === - Statistics for Imaging II. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> *Fixed and Random Effects<br /> *Repeated measures<br /> :*Bonferroni and Other Corrections<br /> *Non-Parametric Methods<br /> *Autocorrelation<br /> *Unknown Distributions<br /> <br /> ''Required Readings''<br /> :*[[media: CohenClassSlides12_2_13.pdf‎ | same as 12/2/14]]<br /> <br /> ''Suggested Further Reading''<br /> <br /> :*[http://ccn.ucla.edu/media/PNI20121121.mp4 Lecture Video - To be Posted Shortly - Please Right Click and Save rather than Stream - Thank you Alan and Edward!]<br /> <br /> =Week 9: =<br /> ==''Monday 11/25/14''==<br /> === - TBD. [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> ==''Wednesday 11/27/14''==<br /> ===- The Organization of the Human Brain. ''Speaker'': [http://ccn.ucla.edu/bmcweb/bmc_bios/SusanBookheimer/ Susan Bookheimer]===<br /> <br /> We will discuss the general organization of the human brain, and the regional specialization of cortical areas. The emphasis will be on understanding principles of organization:<br /> *Phylogenetic Layering<br /> *Functional Specialization<br /> *Principles Divisions of the Brain<br /> *Brain Systems<br /> <br /> ''Required Readings''<br /> :*[http://da.biostr.washington.edu:80/cgi-bin/DA/PageMaster?atlas:NeuroSyllabus+ffpathIndex/Splash^Page^Syllabus+2 Neuroanatomy Programmed Learning]<br /> :*[[media:Cognitive_science_and_neuro_2012.pdf | Slides shown in Class UPDATED 2012/10/17]]<br /> ''Suggested Further Reading''<br /> <br /> :*[[media:PNIA_2012_PS1_Soln.pdf‎ | Solution to Problem Set 1]]<br /> ===- Human Electrophysiology ''Speakers'': [http://greenlab.npih.ucla.edu/ROSTER.html Agatha Lenartowicz], [http://dgsom.healthsciences.ucla.edu/institution/personnel?personnel_id=9140 John Stern]===<br /> <br /> ''Evoked Responses'' - Guest Lecturer: [http://greenlab.npih.ucla.edu/ROSTER.html Jonathan Wynn]<br /> *A look at real EEG data<br /> *Preprocessing:<br /> **filtering<br /> **artifact detection/removal<br /> *averaging<br /> *single events<br /> *interpretation<br /> <br /> ''Clinical EEG'' - Guest Lecturer: [http://dgsom.healthsciences.ucla.edu/institution/personnel?personnel_id=9140 John Stern]<br /> *Normal and Abnormal EEG<br /> *EEG as a marker for brain state<br /> **sleep staging<br /> **alpha and relaxation<br /> *Neurofeedback??? --&gt;</div>

Alenartowicz https://www.ccn.ucla.edu/wiki/index.php?title=Principles_of_Neuroimaging_A_-_2016&diff=3214 2016-11-14T03:54:39Z

<p>Alenartowicz: /* Intracranial Recordings Speaker: Nanthia Sultana */</p> <hr /> <div>Principles of Neuroimaging A, Fall, 2016 - Class Schedule and Syllabus<br /> <br /> <br /> ==='''Neuroimaging journal Club (''required for NITP certificate'')'''===<br /> Contact: Katherine Lawrence (katherine.E.Lawrence@ucla.edu) or Janelle Liu (janelle.j.liu@ucla.edu), Faculty Sponsor: Jamie Feusner (JFeusner@mednet.ucla.edu)<br /> <br /> ==This schedule ''will'' change!==<br /> <br /> :'''[[Principles_of_Neuroimaging_-_2016-2017 | Back to main course page for Principles of Neuroimaging]]'''<br /> <br /> :'''Coming up next!: [[Principles_of_Neuroimaging_B_-_2017 | M284B Principles of Neuroimaging B]]'''<br /> <br /> =Course Reading=<br /> ===Required Reading===<br /> :'''Signal Processing for Neuroscientists''' by ''Wim van Drongelen''<br /> ::This can be found as a PDF on scribd.com, for a small fee of $8.99<br /> <br /> ===Supplemental Reading===<br /> :'''Matlab for Neuroscientists'''<br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/88212458/Matlab-Matlab-for-Neuroscientists<br /> <br /> :'''Cartoon Guide to Statistics''' <br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/148072668/Cartoon-Guide-to-Statistics<br /> <br /> :'''NOTE:''' if you subscribe for a Scribd account for a day, you can download as many documents as you like for one fee.<br /> <br /> <br /> =Week 1: Orientation to Neuro-imaging, Neurons, Brains=<br /> <br /> ==''Monday 9/26/16'' ==<br /> ===- Images. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284AImages-intro.pdf | Lecture Slides Images]]<br /> <br /> ==''Wednesday 9/28/16''==<br /> ===- Neurons &amp; Signaling. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> In this first class we will review the basics of neurophysiology with an eye towards what signals of brain function might be visible to the neuroimager. We will discuss information coding, energetics, size and time scales.<br /> [[Image:Neurons.jpg|right]]<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:TheActiveBrain.pdf | The Active Brain]]<br /> :*[[media:NeuronFunctionM284-2016_SM.pdf | Neuron function slides shown in class]]<br /> :*[http://www.science.smith.edu/departments/neurosci/courses/bio330/squid.html Videos of giant squid experiments]<br /> ''Suggested Further Reading''<br /> :*[http://ccn.ucla.edu/wiki/images/5/5a/CAVEAT_LECTOR.pdf Caveat Lector - the misuse of neuroimaging]<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Protected/Kosslyn1999.pdf &quot;If Neuroimaging is the Answer, What is the Question?&quot; Kosslyn, 1999]<br /> :*[http://da.biostr.washington.edu:80/cgi-bin/DA/PageMaster?atlas:NeuroSyllabus+ffpathIndex/Splash^Page^Syllabus+2 Neuroanatomy Programmed Learning]<br /> :*[http://www.amazon.com/Fundamental-Neuroscience-Second-Larry-Squire/dp/0126603030 Squire, Fundamentals of Neuroscience]<br /> :*[http://www.amazon.com/Principles-Neural-Science-Eric-Kandel/dp/0838577016 Kandel, et al., &quot;Principles of Neural Science&quot;]<br /> :This paper, by Malhi, is a nice orientation in methods of neuroimaging. *[http://www.ccn.ucla.edu/wiki/images/f/f2/Malhi2007.pdf Making sense of neuroimaging in psychiatry]<br /> :*[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1359308/pdf/jphysiol01232-0142.pdf Replacement of the axoplasm of giant nerve fibres with artificial solutions]<br /> <br /> =Week 2: Linear Systems, Convolution, Fourier Transforms=<br /> ==''Wednesday 10/3/16''==<br /> <br /> ===- Linear Systems I. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ==''Wednesday 10/5/16''==<br /> ===- Linear Systems II. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> Why the emphasis on Linear Systems? Because they are actually ''easy'' (as compared to non-linear systems, which are not.) As we go through this course, we will see many ways in which linear systems theory is applied to:<br /> :Modeling of Neural Systems<br /> :Extraction of Signal from Noise<br /> :Design of Circuits<br /> :Image Enhancement<br /> :Understanding of Image artifacts, and others.<br /> <br /> Linear systems analysis is one of the great technologies of the 20th and 21st century. It is now the basis for virtually all electronics design, and its extension into the discrete (digital) domain is the basis for most of modern signal processing. <br /> <br /> In our specific case, we will use these few basic principles of linear systems to understand both the instruments we use and the neuroimaging signals we collect. When you have mastered this material, you should be in a much better position to model the systems that you study in order to develop an approach to studying them.<br /> <br /> Here is [http://www.brainmapping.org/NITP/PNA/Readings/ImaginaryNumbers.pdf A primer I wrote on imaginary numbers] that might be a helpful review.<br /> <br /> There is a nice [http://en.wikibooks.org/wiki/Calculus Wikibook on Calculus].<br /> <br /> ''Required Readings''<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapter 1<br /> :*[[media:MathematicalTools2016.pdf | Mathematical Tools]]<br /> <br /> ''Suggested Further Reading''<br /> :'''Introduction to matlab'''<br /> <br /> ''Slides shown in class''<br /> :[[media:LinearityM284A-2016.pdf | Linearity and the Fourier Transform]]<br /> <br /> Please see [http://www.brainmapping.org/NITP/PNA/html/Linearity.html MATLAB linearity demo]<br /> <br /> If you are the type who sees beauty in mathematics, the Euler identity may be one of the most beautiful pieces of math in the world.<br /> <br /> =Week 3: Math &amp; Circuits I=<br /> <br /> ==''Monday 10/10/16''==<br /> === - Circuits I. Speaker: Cameron Rodriguez ===<br /> :*[[media:Circuits_2016.pdf | Circuit slides shown in class]]<br /> <br /> ==''Wednesday 10/12/16''==<br /> === - Circuits II. ''Speaker'': Cameron Rodriguez ===<br /> <br /> Why circuits?<br /> :(Virtually) Every device you use in your research is electronic. You access your primary data only indirectly<br /> :The device you ''really'' want in your lab doesn't exist. You very well may have to make it.<br /> :There are electronic analogs to most of the linear systems that you have so far studied (and ''vice versa'' - the tools you now understand can be used to analyze and predict circuit behavior).<br /> <br /> :If you have not had any of this background, you might want to have a look at this handout, [[Media:Electricity.pdf|Electrical Circuits]], in advance. There are near infinite numbers of resources on the web that cover similar material (near enough to infinite that by the time you read all of them, there would be a whole new set.) I have recently come across a link to [http://www.allaboutcircuits.com/ Online Books: All About Circuits] ''IF'' you want practical hands-on knowledge about this material, my all-time favorite text is [http://www.google.com/products/catalog?hl=en&amp;client=safari&amp;rls=en-us&amp;ei=uVSPSfaxE5nMsAPf-tmSCQ&amp;resnum=1&amp;q=art+of+electronics&amp;um=1&amp;ie=UTF-8&amp;cid=8820839049329255765#ps-sellers &quot;Horowitz and Hill: ''The Art of Electronics.''&quot;] The latest edition, however, is dated 1989 and a new third edition is promised. I have therefore stopped short of recommending a purchase unless your need to make circuits is immediate. In this book, you will find an excellent education on the fundamental principles of electrical circuits and an incredible compendium of practical data, such as how to assemble circuit boards, how to make measurements, etc...) <br /> <br /> Readings:<br /> :*[http://www.ti.com/lit/an/sloa093/sloa093.pdf Filter Design in 30 Seconds]<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapter 2 and 10<br /> :*[https://www.circuitlab.com/ Circuit Lab ] A Free Circuit Web Base Simulator<br /> **You may or may not find this comprehensible without chapters 5 through 9.<br /> <br /> We will discuss:<br /> :*Passive Circuit Elements: Resistors, Capacitors, Inductors<br /> :*Gain<br /> :*Transformers<br /> :*Rectifiers<br /> :*Active Elements<br /> ::- ''Amplifiers''<br /> ::- ''Transistors''<br /> ::- ''Op Amps''<br /> :*Solutions with Matrices<br /> ''Suggested Further Reading''<br /> :*[http://video.google.com/videoplay?docid=5645396659673218353&amp;q=Physics+for+Future+Presidents+Electricity&amp;total=5&amp;start=0&amp;num=10&amp;so=0&amp;type=search&amp;plindex=0#0h20m30s Video intro lecture on charge, current and voltage].<br /> :*[[media:Electricity_Basics.pdf‎ | Well organized text on electrical concepts by Tony R. Kuphaldt]]<br /> :*[http://en.wikibooks.org/wiki/Circuit_Theory Circuit Theory - Wikibook]<br /> <br /> '''Suggested, Optional Readings from [http://www.dspguide.com DSPguide.com]:'''<br /> :*[http://www.dspguide.com/CH5.PDF Linear Systems]<br /> :*[http://www.dspguide.com/CH6.PDF Convolution]<br /> :*[http://www.dspguide.com/CH8.PDF Discrete Fourier Transform (DFT)]<br /> :''Note: These chapters are light on math and try to focus on a conceptual understanding''<br /> <br /> Time and Frequency / Spectral Filters<br /> <br /> ----<br /> Practice using the Fourier transform:<br /> :[http://www.brainmapping.org/NITP/PNA/ConvFThtml/ConvolutionWorksheet.pdf Fourier transform and Convolution Worksheet]. &lt;!-- [http://www.brainmapping.org/NITP/PNA/ConvFThtml/ConvFT.html (''Solutions'').] --&gt;<br /> :[http://www.brainmapping.org/NITP/PNA/ConvFThtml/Something.wav Sound file for worksheet above.]<br /> <br /> Other<br /> :*[http://www.youtube.com/watch?feature=player_embedded&amp;v=7uHfjpU3OH0#! Getting Started with Arduino]<br /> <br /> =Week 4 Signal Processing=<br /> <br /> ==''Monday 10/17/16''==<br /> === - Signal Processing. ''Speaker'': Cameron Rodriguez ===<br /> <br /> :*[[media:Signals1-PreProcessing1_2016.pdf | Signal Pre Processing Slide Set 1 shown in class]]<br /> <br /> <br /> ==''Wednesday 10/19/16''==<br /> === - Noise ''Speaker'': Cameron Rodriguez ===<br /> <br /> :*[[media:OneD_signal_Filter_Ex.pdf | Matlab Signal Tutorial printout]]<br /> <br /> It is what you ''don't'' want - usually - but things change in quantized systems<br /> :Additive noise<br /> :White Noise<br /> :Boltzmann noise<br /> :Colored Noise<br /> :Gaussian Noise<br /> :Coherent noise<br /> :Sampling Errors<br /> :Aliasing<br /> :Quantization noise<br /> :Spectral filtering<br /> <br /> Noise comes in all shapes and colors. It is present in every measurement we make, from an EEG voltage to an estimate of the effects of dopamine on forebrain signal. Our best weapons are an understanding of the statistical properties of noise, the sources of noise and the ways to control it. Noise in the discrete digital domain is special, as it is both ''created'' by digitization and amplified by sampling.<br /> <br /> Readings:<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapters 2 through 4<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Noise.pdf Noise Slides]<br /> <br /> =Week 5 - Information &amp; Statistical Theory =<br /> <br /> ==''Monday 10/24/16 - in C8-177''==<br /> === Noise Cont' &amp; Information Theory Speakers: Cameron Rodriguez &amp; John Villasenor===<br /> <br /> ==''Wednesday 10/26/16''==<br /> ===Statistical Theory ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> We will consider the general problems of statistical inference, with a concentration on developing an intuitive understanding of statistical concepts.<br /> [[Image:MeasureForMeasure.jpg|right]]<br /> <br /> Suggested reading<br /> :*[http://www.statsoft.com/textbook/stbasic.html Statsoft online text (''free'')]<br /> :*[http://www.amazon.com/Cartoon-Guide-Statistics-Larry-Gonick/dp/0062731025 The Cartoon Guide to Statistics - Gonick $17.95 new]<br /> :The latter teaches stats at what I feel to be the right level - developing intuitions about the kinds of questions that can be answered using stats and about the statistical tests and measures<br /> <br /> :'''Problem Set 3 - Statistics in matlab'''<br /> ::[[media: Problem_Set_1.doc|Problem set using stats and MATLAB]]<br /> ::[[media: Problem_Set_1B.doc|More practice with stats and MATLAB]]<br /> <br /> <br /> =MIDTERM=<br /> <br /> <br /> =Week 6 - Neurophysiology=<br /> ==''Monday 10/31/16''==<br /> === Electrophysiology I ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> We will examine our first imaging modality, EEG (and MEG).<br /> <br /> ::[[media: EEGNeurophys_161102.pdf|Slides for Neurophysiology Lectures]]<br /> <br /> ==''Wednesday 11/2/16''==<br /> === Electrophysiology II ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> Continuing with neurophysiology. ERPs &amp; Source Localization.<br /> <br /> =Week 7 - Neurophysiology =<br /> ==''Monday 11/7/16''==<br /> === Electrophysiology III ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> Continuing with neurophysiology. Connectivity &amp; Oscillations.<br /> <br /> ==''Wednesday 11/9/16''==<br /> ===Intracranial Recordings ''Speaker'': [http://neurosurgery.ucla.edu/neuromodulation-neuroimaging-lab Nanthia Sultana]===<br /> <br /> ::[[media: Suthana.pdf|Slides for Intracranial Lecture]]<br /> ::[[media: LachauxIntracranial.pdf|Lachaux et al Reading]]<br /> ::[[media: HarrisIntracranial.pdf|Harris et al Reading]]<br /> <br /> =Week 8 - Practical Neurophysiology=<br /> ==''Monday 11/14/16''==<br /> ===Building an EEG System - Circuit 1 ''Speaker'': Cameron Rodriguez===<br /> <br /> ==''Wednesday 11/16/16''==<br /> ===Building an EEG System - Circuit 2 ''Speaker'': Cameron Rodriguez===<br /> <br /> :*[https://learn.sparkfun.com/tutorials/how-to-solder Through holesoldering].<br /> :*[https://learn.sparkfun.com/tutorials/pcb-basics Printed Circuit Board Basics].<br /> :*[https://learn.sparkfun.com/tutorials/how-to-install-and-setup-eagle Installing and Setting Up Eagle].<br /> :*[https://learn.sparkfun.com/tutorials/using-eagle-schematic Using Eagle Schematic].<br /> :*[https://learn.sparkfun.com/tutorials/using-eagle-board-layoutc Using Eagle Board Layout].<br /> <br /> :*[https://www.sparkfun.com/videos#pete/NJKZZArjdg8 PCB Layout 1 - According to Pete];<br /> :*[https://www.sparkfun.com/videos#pete/JANZsjRiM3w PCB Layout 2 - According to Pete];<br /> <br /> <br /> =Week 9 - Magnetic Resonance Fundamentals =<br /> ==''Monday 11/21/16 - in C8-177''==<br /> ===MR Signal Origin ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ==''Wednesday 11/23/16''==<br /> ===Thankgiving Wed - No Class ===<br /> <br /> Magnetic Resonance Imaging (MRI) is probably the most influential and most flexible current means of imaging the human brain. It features a vast number of separable contrast mechanisms, and a near ideal combination of non-invasiveness, safety, resolution and metric accuracy. However, it is extraordinarily expensive and has limited temporal resolution, especially for functional studies<br /> <br /> OUTLINE<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> :*[http://www.cis.rit.edu/htbooks/mri/ These notes] by Joseph Hornak are highly professional and complete coverage of MRI.<br /> :*[http://www.imaios.com/en/e-Courses/e-MRI eMRI] is another excellent online MRI learning resource<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Hahn1950.pdf Erwin Hahn - Spin Echoes: ''Essential reading for the MRI community'']<br /> [[Image:HahnFig1.png]]<br /> ''above'': Figure 1 from Hahn, 1950<br /> <br /> ''Suggested Further Reading''<br /> [[image:PSatSeq.jpg|right]]<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> <br /> =Week 10 - Magnetic Resonance Fundamentals =<br /> ==''Monday 11/28/16''==<br /> ===MR Physics ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ==''Wednesady 11/30/16''==<br /> ===TBD/Q&amp;A===<br /> <br /> <br /> =Week 11 - Finals Week=<br /> ==''Monday 11/5/16''==<br /> ===Final Exam===<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!--<br /> SPARE CONTENT / PREVIOUS YEARS<br /> <br /> =Week 3: Stats begins!=<br /> <br /> ==''Monday 10/12/15''==<br /> <br /> ===- Statistical Fundamentals. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> :*[[media: CohenClassIntroStats11_25_13.pdf‎ | Slides used in class (set 1)]]<br /> <br /> ''Review of'':<br /> :*Descriptive Statistics: mean, mode, variance, standard deviation<br /> :*Statistical Inference. The Binomial and Normal Distribution<br /> :*Basic Tests: t-test, linear correlation<br /> :*Modeling and non-linear relations<br /> :*Bayes rule<br /> <br /> <br /> ==''Wednesday 10/14/15''==<br /> ===- Statistical Fundamentals II. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> =Week 4 - Stats and circuits=<br /> ==''Monday 10/19/15''==<br /> ===- Statistical Fundamentals III. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> ==''Wednesday 10/21/15''==<br /> ===Circuits continued: Active devices. [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ===Optics I. ''Speaker'': [mailto:zdeis@seas.ucla.edu Zachary Taylor]===<br /> [[Image:Reflection.jpg|right]]<br /> The overall goal of this lecture is to establish that:<br /> ''- Physical constants have tangible meanings''<br /> ''- Plane waves form a physically unrealizable but extremely good approximation to real systems''<br /> ''- Boundaries bend light''<br /> ''- Physical constants, plane wave mechanics, and boundaries can be used to describe the operation of a lens''<br /> ''- The PSF gives a good indication of the overall performance of an imaging system''<br /> ''- All of these concepts have analogues in other areas of engineering (ie circuits, mechanical vibrations, etc.)''''<br /> <br /> '''Outline:'''<br /> :* Constitutive parameters (ε, μ, η, n, etc.)<br /> :* Plane wave basics<br /> :* Plane waves at boundaries<br /> :* Lenses<br /> :* Advanced imaging properties of lenses<br /> :* Point spread function.<br /> <br /> ''Required Readings''<br /> Zach has very kindly agreed to post his [http://www.brainmapping.org/NITP/PNA/Readings/OpticsTaylor3-10-10.pdf Optics lecture notes].<br /> ''Suggested Further Reading''<br /> <br /> <br /> <br /> <br /> ==''Wednesday 11/19/14''== <br /> ===Optogenetics. ''Speaker'': [http://faculty.neuroscience.ucla.edu/institution/personnel?personnel_id=47031 Tom Otis]===<br /> <br /> <br /> =Week 8: =<br /> ==''Monday 12/1/14 ''==<br /> ===- Statistics for Imaging. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> [[image:BVTradeoff.jpg|right]]<br /> ''Required Readings''<br /> :*[[media: Mumford_stat_modeling.pdf | Statistical Modeling and Inference (pdf)]]<br /> :*[[media: CohenClassSlides12_2_13.pdf | Slides used in class 12/2/14 and 12/4/14]]<br /> <br /> :*The General Linear Model<br /> *Linear Algebra applied to Statistical Solutions<br /> :*Analysis of Variance<br /> <br /> ==''Wednesday 11/20/14 ...''==<br /> === - Statistics for Imaging II. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> *Fixed and Random Effects<br /> *Repeated measures<br /> :*Bonferroni and Other Corrections<br /> *Non-Parametric Methods<br /> *Autocorrelation<br /> *Unknown Distributions<br /> <br /> ''Required Readings''<br /> :*[[media: CohenClassSlides12_2_13.pdf‎ | same as 12/2/14]]<br /> <br /> ''Suggested Further Reading''<br /> <br /> :*[http://ccn.ucla.edu/media/PNI20121121.mp4 Lecture Video - To be Posted Shortly - Please Right Click and Save rather than Stream - Thank you Alan and Edward!]<br /> <br /> =Week 9: =<br /> ==''Monday 11/25/14''==<br /> === - TBD. [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> ==''Wednesday 11/27/14''==<br /> ===- The Organization of the Human Brain. ''Speaker'': [http://ccn.ucla.edu/bmcweb/bmc_bios/SusanBookheimer/ Susan Bookheimer]===<br /> <br /> We will discuss the general organization of the human brain, and the regional specialization of cortical areas. The emphasis will be on understanding principles of organization:<br /> *Phylogenetic Layering<br /> *Functional Specialization<br /> *Principles Divisions of the Brain<br /> *Brain Systems<br /> <br /> ''Required Readings''<br /> :*[http://da.biostr.washington.edu:80/cgi-bin/DA/PageMaster?atlas:NeuroSyllabus+ffpathIndex/Splash^Page^Syllabus+2 Neuroanatomy Programmed Learning]<br /> :*[[media:Cognitive_science_and_neuro_2012.pdf | Slides shown in Class UPDATED 2012/10/17]]<br /> ''Suggested Further Reading''<br /> <br /> :*[[media:PNIA_2012_PS1_Soln.pdf‎ | Solution to Problem Set 1]]<br /> ===- Human Electrophysiology ''Speakers'': [http://greenlab.npih.ucla.edu/ROSTER.html Agatha Lenartowicz], [http://dgsom.healthsciences.ucla.edu/institution/personnel?personnel_id=9140 John Stern]===<br /> <br /> ''Evoked Responses'' - Guest Lecturer: [http://greenlab.npih.ucla.edu/ROSTER.html Jonathan Wynn]<br /> *A look at real EEG data<br /> *Preprocessing:<br /> **filtering<br /> **artifact detection/removal<br /> *averaging<br /> *single events<br /> *interpretation<br /> <br /> ''Clinical EEG'' - Guest Lecturer: [http://dgsom.healthsciences.ucla.edu/institution/personnel?personnel_id=9140 John Stern]<br /> *Normal and Abnormal EEG<br /> *EEG as a marker for brain state<br /> **sleep staging<br /> **alpha and relaxation<br /> *Neurofeedback??? --&gt;</div>

Alenartowicz https://www.ccn.ucla.edu/wiki/index.php?title=Principles_of_Neuroimaging_A_-_2016&diff=3213 2016-11-14T03:54:21Z

<p>Alenartowicz: /* Intracranial Recordings Speaker: Nanthia Sultana */</p> <hr /> <div>Principles of Neuroimaging A, Fall, 2016 - Class Schedule and Syllabus<br /> <br /> <br /> ==='''Neuroimaging journal Club (''required for NITP certificate'')'''===<br /> Contact: Katherine Lawrence (katherine.E.Lawrence@ucla.edu) or Janelle Liu (janelle.j.liu@ucla.edu), Faculty Sponsor: Jamie Feusner (JFeusner@mednet.ucla.edu)<br /> <br /> ==This schedule ''will'' change!==<br /> <br /> :'''[[Principles_of_Neuroimaging_-_2016-2017 | Back to main course page for Principles of Neuroimaging]]'''<br /> <br /> :'''Coming up next!: [[Principles_of_Neuroimaging_B_-_2017 | M284B Principles of Neuroimaging B]]'''<br /> <br /> =Course Reading=<br /> ===Required Reading===<br /> :'''Signal Processing for Neuroscientists''' by ''Wim van Drongelen''<br /> ::This can be found as a PDF on scribd.com, for a small fee of $8.99<br /> <br /> ===Supplemental Reading===<br /> :'''Matlab for Neuroscientists'''<br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/88212458/Matlab-Matlab-for-Neuroscientists<br /> <br /> :'''Cartoon Guide to Statistics''' <br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/148072668/Cartoon-Guide-to-Statistics<br /> <br /> :'''NOTE:''' if you subscribe for a Scribd account for a day, you can download as many documents as you like for one fee.<br /> <br /> <br /> =Week 1: Orientation to Neuro-imaging, Neurons, Brains=<br /> <br /> ==''Monday 9/26/16'' ==<br /> ===- Images. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284AImages-intro.pdf | Lecture Slides Images]]<br /> <br /> ==''Wednesday 9/28/16''==<br /> ===- Neurons &amp; Signaling. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> In this first class we will review the basics of neurophysiology with an eye towards what signals of brain function might be visible to the neuroimager. We will discuss information coding, energetics, size and time scales.<br /> [[Image:Neurons.jpg|right]]<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:TheActiveBrain.pdf | The Active Brain]]<br /> :*[[media:NeuronFunctionM284-2016_SM.pdf | Neuron function slides shown in class]]<br /> :*[http://www.science.smith.edu/departments/neurosci/courses/bio330/squid.html Videos of giant squid experiments]<br /> ''Suggested Further Reading''<br /> :*[http://ccn.ucla.edu/wiki/images/5/5a/CAVEAT_LECTOR.pdf Caveat Lector - the misuse of neuroimaging]<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Protected/Kosslyn1999.pdf &quot;If Neuroimaging is the Answer, What is the Question?&quot; Kosslyn, 1999]<br /> :*[http://da.biostr.washington.edu:80/cgi-bin/DA/PageMaster?atlas:NeuroSyllabus+ffpathIndex/Splash^Page^Syllabus+2 Neuroanatomy Programmed Learning]<br /> :*[http://www.amazon.com/Fundamental-Neuroscience-Second-Larry-Squire/dp/0126603030 Squire, Fundamentals of Neuroscience]<br /> :*[http://www.amazon.com/Principles-Neural-Science-Eric-Kandel/dp/0838577016 Kandel, et al., &quot;Principles of Neural Science&quot;]<br /> :This paper, by Malhi, is a nice orientation in methods of neuroimaging. *[http://www.ccn.ucla.edu/wiki/images/f/f2/Malhi2007.pdf Making sense of neuroimaging in psychiatry]<br /> :*[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1359308/pdf/jphysiol01232-0142.pdf Replacement of the axoplasm of giant nerve fibres with artificial solutions]<br /> <br /> =Week 2: Linear Systems, Convolution, Fourier Transforms=<br /> ==''Wednesday 10/3/16''==<br /> <br /> ===- Linear Systems I. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ==''Wednesday 10/5/16''==<br /> ===- Linear Systems II. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> Why the emphasis on Linear Systems? Because they are actually ''easy'' (as compared to non-linear systems, which are not.) As we go through this course, we will see many ways in which linear systems theory is applied to:<br /> :Modeling of Neural Systems<br /> :Extraction of Signal from Noise<br /> :Design of Circuits<br /> :Image Enhancement<br /> :Understanding of Image artifacts, and others.<br /> <br /> Linear systems analysis is one of the great technologies of the 20th and 21st century. It is now the basis for virtually all electronics design, and its extension into the discrete (digital) domain is the basis for most of modern signal processing. <br /> <br /> In our specific case, we will use these few basic principles of linear systems to understand both the instruments we use and the neuroimaging signals we collect. When you have mastered this material, you should be in a much better position to model the systems that you study in order to develop an approach to studying them.<br /> <br /> Here is [http://www.brainmapping.org/NITP/PNA/Readings/ImaginaryNumbers.pdf A primer I wrote on imaginary numbers] that might be a helpful review.<br /> <br /> There is a nice [http://en.wikibooks.org/wiki/Calculus Wikibook on Calculus].<br /> <br /> ''Required Readings''<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapter 1<br /> :*[[media:MathematicalTools2016.pdf | Mathematical Tools]]<br /> <br /> ''Suggested Further Reading''<br /> :'''Introduction to matlab'''<br /> <br /> ''Slides shown in class''<br /> :[[media:LinearityM284A-2016.pdf | Linearity and the Fourier Transform]]<br /> <br /> Please see [http://www.brainmapping.org/NITP/PNA/html/Linearity.html MATLAB linearity demo]<br /> <br /> If you are the type who sees beauty in mathematics, the Euler identity may be one of the most beautiful pieces of math in the world.<br /> <br /> =Week 3: Math &amp; Circuits I=<br /> <br /> ==''Monday 10/10/16''==<br /> === - Circuits I. Speaker: Cameron Rodriguez ===<br /> :*[[media:Circuits_2016.pdf | Circuit slides shown in class]]<br /> <br /> ==''Wednesday 10/12/16''==<br /> === - Circuits II. ''Speaker'': Cameron Rodriguez ===<br /> <br /> Why circuits?<br /> :(Virtually) Every device you use in your research is electronic. You access your primary data only indirectly<br /> :The device you ''really'' want in your lab doesn't exist. You very well may have to make it.<br /> :There are electronic analogs to most of the linear systems that you have so far studied (and ''vice versa'' - the tools you now understand can be used to analyze and predict circuit behavior).<br /> <br /> :If you have not had any of this background, you might want to have a look at this handout, [[Media:Electricity.pdf|Electrical Circuits]], in advance. There are near infinite numbers of resources on the web that cover similar material (near enough to infinite that by the time you read all of them, there would be a whole new set.) I have recently come across a link to [http://www.allaboutcircuits.com/ Online Books: All About Circuits] ''IF'' you want practical hands-on knowledge about this material, my all-time favorite text is [http://www.google.com/products/catalog?hl=en&amp;client=safari&amp;rls=en-us&amp;ei=uVSPSfaxE5nMsAPf-tmSCQ&amp;resnum=1&amp;q=art+of+electronics&amp;um=1&amp;ie=UTF-8&amp;cid=8820839049329255765#ps-sellers &quot;Horowitz and Hill: ''The Art of Electronics.''&quot;] The latest edition, however, is dated 1989 and a new third edition is promised. I have therefore stopped short of recommending a purchase unless your need to make circuits is immediate. In this book, you will find an excellent education on the fundamental principles of electrical circuits and an incredible compendium of practical data, such as how to assemble circuit boards, how to make measurements, etc...) <br /> <br /> Readings:<br /> :*[http://www.ti.com/lit/an/sloa093/sloa093.pdf Filter Design in 30 Seconds]<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapter 2 and 10<br /> :*[https://www.circuitlab.com/ Circuit Lab ] A Free Circuit Web Base Simulator<br /> **You may or may not find this comprehensible without chapters 5 through 9.<br /> <br /> We will discuss:<br /> :*Passive Circuit Elements: Resistors, Capacitors, Inductors<br /> :*Gain<br /> :*Transformers<br /> :*Rectifiers<br /> :*Active Elements<br /> ::- ''Amplifiers''<br /> ::- ''Transistors''<br /> ::- ''Op Amps''<br /> :*Solutions with Matrices<br /> ''Suggested Further Reading''<br /> :*[http://video.google.com/videoplay?docid=5645396659673218353&amp;q=Physics+for+Future+Presidents+Electricity&amp;total=5&amp;start=0&amp;num=10&amp;so=0&amp;type=search&amp;plindex=0#0h20m30s Video intro lecture on charge, current and voltage].<br /> :*[[media:Electricity_Basics.pdf‎ | Well organized text on electrical concepts by Tony R. Kuphaldt]]<br /> :*[http://en.wikibooks.org/wiki/Circuit_Theory Circuit Theory - Wikibook]<br /> <br /> '''Suggested, Optional Readings from [http://www.dspguide.com DSPguide.com]:'''<br /> :*[http://www.dspguide.com/CH5.PDF Linear Systems]<br /> :*[http://www.dspguide.com/CH6.PDF Convolution]<br /> :*[http://www.dspguide.com/CH8.PDF Discrete Fourier Transform (DFT)]<br /> :''Note: These chapters are light on math and try to focus on a conceptual understanding''<br /> <br /> Time and Frequency / Spectral Filters<br /> <br /> ----<br /> Practice using the Fourier transform:<br /> :[http://www.brainmapping.org/NITP/PNA/ConvFThtml/ConvolutionWorksheet.pdf Fourier transform and Convolution Worksheet]. &lt;!-- [http://www.brainmapping.org/NITP/PNA/ConvFThtml/ConvFT.html (''Solutions'').] --&gt;<br /> :[http://www.brainmapping.org/NITP/PNA/ConvFThtml/Something.wav Sound file for worksheet above.]<br /> <br /> Other<br /> :*[http://www.youtube.com/watch?feature=player_embedded&amp;v=7uHfjpU3OH0#! Getting Started with Arduino]<br /> <br /> =Week 4 Signal Processing=<br /> <br /> ==''Monday 10/17/16''==<br /> === - Signal Processing. ''Speaker'': Cameron Rodriguez ===<br /> <br /> :*[[media:Signals1-PreProcessing1_2016.pdf | Signal Pre Processing Slide Set 1 shown in class]]<br /> <br /> <br /> ==''Wednesday 10/19/16''==<br /> === - Noise ''Speaker'': Cameron Rodriguez ===<br /> <br /> :*[[media:OneD_signal_Filter_Ex.pdf | Matlab Signal Tutorial printout]]<br /> <br /> It is what you ''don't'' want - usually - but things change in quantized systems<br /> :Additive noise<br /> :White Noise<br /> :Boltzmann noise<br /> :Colored Noise<br /> :Gaussian Noise<br /> :Coherent noise<br /> :Sampling Errors<br /> :Aliasing<br /> :Quantization noise<br /> :Spectral filtering<br /> <br /> Noise comes in all shapes and colors. It is present in every measurement we make, from an EEG voltage to an estimate of the effects of dopamine on forebrain signal. Our best weapons are an understanding of the statistical properties of noise, the sources of noise and the ways to control it. Noise in the discrete digital domain is special, as it is both ''created'' by digitization and amplified by sampling.<br /> <br /> Readings:<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapters 2 through 4<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Noise.pdf Noise Slides]<br /> <br /> =Week 5 - Information &amp; Statistical Theory =<br /> <br /> ==''Monday 10/24/16 - in C8-177''==<br /> === Noise Cont' &amp; Information Theory Speakers: Cameron Rodriguez &amp; John Villasenor===<br /> <br /> ==''Wednesday 10/26/16''==<br /> ===Statistical Theory ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> We will consider the general problems of statistical inference, with a concentration on developing an intuitive understanding of statistical concepts.<br /> [[Image:MeasureForMeasure.jpg|right]]<br /> <br /> Suggested reading<br /> :*[http://www.statsoft.com/textbook/stbasic.html Statsoft online text (''free'')]<br /> :*[http://www.amazon.com/Cartoon-Guide-Statistics-Larry-Gonick/dp/0062731025 The Cartoon Guide to Statistics - Gonick $17.95 new]<br /> :The latter teaches stats at what I feel to be the right level - developing intuitions about the kinds of questions that can be answered using stats and about the statistical tests and measures<br /> <br /> :'''Problem Set 3 - Statistics in matlab'''<br /> ::[[media: Problem_Set_1.doc|Problem set using stats and MATLAB]]<br /> ::[[media: Problem_Set_1B.doc|More practice with stats and MATLAB]]<br /> <br /> <br /> =MIDTERM=<br /> <br /> <br /> =Week 6 - Neurophysiology=<br /> ==''Monday 10/31/16''==<br /> === Electrophysiology I ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> We will examine our first imaging modality, EEG (and MEG).<br /> <br /> ::[[media: EEGNeurophys_161102.pdf|Slides for Neurophysiology Lectures]]<br /> <br /> ==''Wednesday 11/2/16''==<br /> === Electrophysiology II ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> Continuing with neurophysiology. ERPs &amp; Source Localization.<br /> <br /> =Week 7 - Neurophysiology =<br /> ==''Monday 11/7/16''==<br /> === Electrophysiology III ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> Continuing with neurophysiology. Connectivity &amp; Oscillations.<br /> <br /> ==''Wednesday 11/9/16''==<br /> ===Intracranial Recordings ''Speaker'': [http://neurosurgery.ucla.edu/neuromodulation-neuroimaging-lab Nanthia Sultana]===<br /> &lt;br&gt;<br /> <br /> ::[[media: Suthana.pdf|Slides for Intracranial Lecture]]<br /> ::[[media: LachauxIntracranial.pdf|Lachaux et al Reading]]<br /> ::[[media: HarrisIntracranial.pdf|Harris et al Reading]]<br /> <br /> =Week 8 - Practical Neurophysiology=<br /> ==''Monday 11/14/16''==<br /> ===Building an EEG System - Circuit 1 ''Speaker'': Cameron Rodriguez===<br /> <br /> ==''Wednesday 11/16/16''==<br /> ===Building an EEG System - Circuit 2 ''Speaker'': Cameron Rodriguez===<br /> <br /> :*[https://learn.sparkfun.com/tutorials/how-to-solder Through holesoldering].<br /> :*[https://learn.sparkfun.com/tutorials/pcb-basics Printed Circuit Board Basics].<br /> :*[https://learn.sparkfun.com/tutorials/how-to-install-and-setup-eagle Installing and Setting Up Eagle].<br /> :*[https://learn.sparkfun.com/tutorials/using-eagle-schematic Using Eagle Schematic].<br /> :*[https://learn.sparkfun.com/tutorials/using-eagle-board-layoutc Using Eagle Board Layout].<br /> <br /> :*[https://www.sparkfun.com/videos#pete/NJKZZArjdg8 PCB Layout 1 - According to Pete];<br /> :*[https://www.sparkfun.com/videos#pete/JANZsjRiM3w PCB Layout 2 - According to Pete];<br /> <br /> <br /> =Week 9 - Magnetic Resonance Fundamentals =<br /> ==''Monday 11/21/16 - in C8-177''==<br /> ===MR Signal Origin ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ==''Wednesday 11/23/16''==<br /> ===Thankgiving Wed - No Class ===<br /> <br /> Magnetic Resonance Imaging (MRI) is probably the most influential and most flexible current means of imaging the human brain. It features a vast number of separable contrast mechanisms, and a near ideal combination of non-invasiveness, safety, resolution and metric accuracy. However, it is extraordinarily expensive and has limited temporal resolution, especially for functional studies<br /> <br /> OUTLINE<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> :*[http://www.cis.rit.edu/htbooks/mri/ These notes] by Joseph Hornak are highly professional and complete coverage of MRI.<br /> :*[http://www.imaios.com/en/e-Courses/e-MRI eMRI] is another excellent online MRI learning resource<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Hahn1950.pdf Erwin Hahn - Spin Echoes: ''Essential reading for the MRI community'']<br /> [[Image:HahnFig1.png]]<br /> ''above'': Figure 1 from Hahn, 1950<br /> <br /> ''Suggested Further Reading''<br /> [[image:PSatSeq.jpg|right]]<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> <br /> =Week 10 - Magnetic Resonance Fundamentals =<br /> ==''Monday 11/28/16''==<br /> ===MR Physics ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ==''Wednesady 11/30/16''==<br /> ===TBD/Q&amp;A===<br /> <br /> <br /> =Week 11 - Finals Week=<br /> ==''Monday 11/5/16''==<br /> ===Final Exam===<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!--<br /> SPARE CONTENT / PREVIOUS YEARS<br /> <br /> =Week 3: Stats begins!=<br /> <br /> ==''Monday 10/12/15''==<br /> <br /> ===- Statistical Fundamentals. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> :*[[media: CohenClassIntroStats11_25_13.pdf‎ | Slides used in class (set 1)]]<br /> <br /> ''Review of'':<br /> :*Descriptive Statistics: mean, mode, variance, standard deviation<br /> :*Statistical Inference. The Binomial and Normal Distribution<br /> :*Basic Tests: t-test, linear correlation<br /> :*Modeling and non-linear relations<br /> :*Bayes rule<br /> <br /> <br /> ==''Wednesday 10/14/15''==<br /> ===- Statistical Fundamentals II. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> =Week 4 - Stats and circuits=<br /> ==''Monday 10/19/15''==<br /> ===- Statistical Fundamentals III. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> ==''Wednesday 10/21/15''==<br /> ===Circuits continued: Active devices. [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ===Optics I. ''Speaker'': [mailto:zdeis@seas.ucla.edu Zachary Taylor]===<br /> [[Image:Reflection.jpg|right]]<br /> The overall goal of this lecture is to establish that:<br /> ''- Physical constants have tangible meanings''<br /> ''- Plane waves form a physically unrealizable but extremely good approximation to real systems''<br /> ''- Boundaries bend light''<br /> ''- Physical constants, plane wave mechanics, and boundaries can be used to describe the operation of a lens''<br /> ''- The PSF gives a good indication of the overall performance of an imaging system''<br /> ''- All of these concepts have analogues in other areas of engineering (ie circuits, mechanical vibrations, etc.)''''<br /> <br /> '''Outline:'''<br /> :* Constitutive parameters (ε, μ, η, n, etc.)<br /> :* Plane wave basics<br /> :* Plane waves at boundaries<br /> :* Lenses<br /> :* Advanced imaging properties of lenses<br /> :* Point spread function.<br /> <br /> ''Required Readings''<br /> Zach has very kindly agreed to post his [http://www.brainmapping.org/NITP/PNA/Readings/OpticsTaylor3-10-10.pdf Optics lecture notes].<br /> ''Suggested Further Reading''<br /> <br /> <br /> <br /> <br /> ==''Wednesday 11/19/14''== <br /> ===Optogenetics. ''Speaker'': [http://faculty.neuroscience.ucla.edu/institution/personnel?personnel_id=47031 Tom Otis]===<br /> <br /> <br /> =Week 8: =<br /> ==''Monday 12/1/14 ''==<br /> ===- Statistics for Imaging. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> [[image:BVTradeoff.jpg|right]]<br /> ''Required Readings''<br /> :*[[media: Mumford_stat_modeling.pdf | Statistical Modeling and Inference (pdf)]]<br /> :*[[media: CohenClassSlides12_2_13.pdf | Slides used in class 12/2/14 and 12/4/14]]<br /> <br /> :*The General Linear Model<br /> *Linear Algebra applied to Statistical Solutions<br /> :*Analysis of Variance<br /> <br /> ==''Wednesday 11/20/14 ...''==<br /> === - Statistics for Imaging II. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> *Fixed and Random Effects<br /> *Repeated measures<br /> :*Bonferroni and Other Corrections<br /> *Non-Parametric Methods<br /> *Autocorrelation<br /> *Unknown Distributions<br /> <br /> ''Required Readings''<br /> :*[[media: CohenClassSlides12_2_13.pdf‎ | same as 12/2/14]]<br /> <br /> ''Suggested Further Reading''<br /> <br /> :*[http://ccn.ucla.edu/media/PNI20121121.mp4 Lecture Video - To be Posted Shortly - Please Right Click and Save rather than Stream - Thank you Alan and Edward!]<br /> <br /> =Week 9: =<br /> ==''Monday 11/25/14''==<br /> === - TBD. [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> ==''Wednesday 11/27/14''==<br /> ===- The Organization of the Human Brain. ''Speaker'': [http://ccn.ucla.edu/bmcweb/bmc_bios/SusanBookheimer/ Susan Bookheimer]===<br /> <br /> We will discuss the general organization of the human brain, and the regional specialization of cortical areas. The emphasis will be on understanding principles of organization:<br /> *Phylogenetic Layering<br /> *Functional Specialization<br /> *Principles Divisions of the Brain<br /> *Brain Systems<br /> <br /> ''Required Readings''<br /> :*[http://da.biostr.washington.edu:80/cgi-bin/DA/PageMaster?atlas:NeuroSyllabus+ffpathIndex/Splash^Page^Syllabus+2 Neuroanatomy Programmed Learning]<br /> :*[[media:Cognitive_science_and_neuro_2012.pdf | Slides shown in Class UPDATED 2012/10/17]]<br /> ''Suggested Further Reading''<br /> <br /> :*[[media:PNIA_2012_PS1_Soln.pdf‎ | Solution to Problem Set 1]]<br /> ===- Human Electrophysiology ''Speakers'': [http://greenlab.npih.ucla.edu/ROSTER.html Agatha Lenartowicz], [http://dgsom.healthsciences.ucla.edu/institution/personnel?personnel_id=9140 John Stern]===<br /> <br /> ''Evoked Responses'' - Guest Lecturer: [http://greenlab.npih.ucla.edu/ROSTER.html Jonathan Wynn]<br /> *A look at real EEG data<br /> *Preprocessing:<br /> **filtering<br /> **artifact detection/removal<br /> *averaging<br /> *single events<br /> *interpretation<br /> <br /> ''Clinical EEG'' - Guest Lecturer: [http://dgsom.healthsciences.ucla.edu/institution/personnel?personnel_id=9140 John Stern]<br /> *Normal and Abnormal EEG<br /> *EEG as a marker for brain state<br /> **sleep staging<br /> **alpha and relaxation<br /> *Neurofeedback??? --&gt;</div>

Alenartowicz https://www.ccn.ucla.edu/wiki/index.php?title=File:Suthana.pdf&diff=3212 2016-11-14T03:52:32Z

<p>Alenartowicz: </p> <hr /> <div></div>

Alenartowicz https://www.ccn.ucla.edu/wiki/index.php?title=File:HarrisIntracranial.pdf&diff=3211 2016-11-14T03:51:38Z

<p>Alenartowicz: </p> <hr /> <div></div>

Alenartowicz https://www.ccn.ucla.edu/wiki/index.php?title=File:LachauxIntracranial.pdf&diff=3210 2016-11-14T03:50:50Z

<p>Alenartowicz: </p> <hr /> <div></div>

Alenartowicz https://www.ccn.ucla.edu/wiki/index.php?title=Principles_of_Neuroimaging_A_-_2016&diff=3209 2016-11-03T16:08:49Z

<p>Alenartowicz: /* Electrophysiology I Speaker: Agatha Lenartowicz */</p> <hr /> <div>Principles of Neuroimaging A, Fall, 2016 - Class Schedule and Syllabus<br /> <br /> <br /> ==='''Neuroimaging journal Club (''required for NITP certificate'')'''===<br /> Contact: Katherine Lawrence (katherine.E.Lawrence@ucla.edu) or Janelle Liu (janelle.j.liu@ucla.edu), Faculty Sponsor: Jamie Feusner (JFeusner@mednet.ucla.edu)<br /> <br /> ==This schedule ''will'' change!==<br /> <br /> :'''[[Principles_of_Neuroimaging_-_2016-2017 | Back to main course page for Principles of Neuroimaging]]'''<br /> <br /> :'''Coming up next!: [[Principles_of_Neuroimaging_B_-_2017 | M284B Principles of Neuroimaging B]]'''<br /> <br /> =Course Reading=<br /> ===Required Reading===<br /> :'''Signal Processing for Neuroscientists''' by ''Wim van Drongelen''<br /> ::This can be found as a PDF on scribd.com, for a small fee of $8.99<br /> <br /> ===Supplemental Reading===<br /> :'''Matlab for Neuroscientists'''<br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/88212458/Matlab-Matlab-for-Neuroscientists<br /> <br /> :'''Cartoon Guide to Statistics''' <br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/148072668/Cartoon-Guide-to-Statistics<br /> <br /> :'''NOTE:''' if you subscribe for a Scribd account for a day, you can download as many documents as you like for one fee.<br /> <br /> <br /> =Week 1: Orientation to Neuro-imaging, Neurons, Brains=<br /> <br /> ==''Monday 9/26/16'' ==<br /> ===- Images. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284AImages-intro.pdf | Lecture Slides Images]]<br /> <br /> ==''Wednesday 9/28/16''==<br /> ===- Neurons &amp; Signaling. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> In this first class we will review the basics of neurophysiology with an eye towards what signals of brain function might be visible to the neuroimager. We will discuss information coding, energetics, size and time scales.<br /> [[Image:Neurons.jpg|right]]<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:TheActiveBrain.pdf | The Active Brain]]<br /> :*[[media:NeuronFunctionM284-2016_SM.pdf | Neuron function slides shown in class]]<br /> :*[http://www.science.smith.edu/departments/neurosci/courses/bio330/squid.html Videos of giant squid experiments]<br /> ''Suggested Further Reading''<br /> :*[http://ccn.ucla.edu/wiki/images/5/5a/CAVEAT_LECTOR.pdf Caveat Lector - the misuse of neuroimaging]<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Protected/Kosslyn1999.pdf &quot;If Neuroimaging is the Answer, What is the Question?&quot; Kosslyn, 1999]<br /> :*[http://da.biostr.washington.edu:80/cgi-bin/DA/PageMaster?atlas:NeuroSyllabus+ffpathIndex/Splash^Page^Syllabus+2 Neuroanatomy Programmed Learning]<br /> :*[http://www.amazon.com/Fundamental-Neuroscience-Second-Larry-Squire/dp/0126603030 Squire, Fundamentals of Neuroscience]<br /> :*[http://www.amazon.com/Principles-Neural-Science-Eric-Kandel/dp/0838577016 Kandel, et al., &quot;Principles of Neural Science&quot;]<br /> :This paper, by Malhi, is a nice orientation in methods of neuroimaging. *[http://www.ccn.ucla.edu/wiki/images/f/f2/Malhi2007.pdf Making sense of neuroimaging in psychiatry]<br /> :*[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1359308/pdf/jphysiol01232-0142.pdf Replacement of the axoplasm of giant nerve fibres with artificial solutions]<br /> <br /> =Week 2: Linear Systems, Convolution, Fourier Transforms=<br /> ==''Wednesday 10/3/16''==<br /> <br /> ===- Linear Systems I. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ==''Wednesday 10/5/16''==<br /> ===- Linear Systems II. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> Why the emphasis on Linear Systems? Because they are actually ''easy'' (as compared to non-linear systems, which are not.) As we go through this course, we will see many ways in which linear systems theory is applied to:<br /> :Modeling of Neural Systems<br /> :Extraction of Signal from Noise<br /> :Design of Circuits<br /> :Image Enhancement<br /> :Understanding of Image artifacts, and others.<br /> <br /> Linear systems analysis is one of the great technologies of the 20th and 21st century. It is now the basis for virtually all electronics design, and its extension into the discrete (digital) domain is the basis for most of modern signal processing. <br /> <br /> In our specific case, we will use these few basic principles of linear systems to understand both the instruments we use and the neuroimaging signals we collect. When you have mastered this material, you should be in a much better position to model the systems that you study in order to develop an approach to studying them.<br /> <br /> Here is [http://www.brainmapping.org/NITP/PNA/Readings/ImaginaryNumbers.pdf A primer I wrote on imaginary numbers] that might be a helpful review.<br /> <br /> There is a nice [http://en.wikibooks.org/wiki/Calculus Wikibook on Calculus].<br /> <br /> ''Required Readings''<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapter 1<br /> :*[[media:MathematicalTools2016.pdf | Mathematical Tools]]<br /> <br /> ''Suggested Further Reading''<br /> :'''Introduction to matlab'''<br /> <br /> ''Slides shown in class''<br /> :[[media:LinearityM284A-2016.pdf | Linearity and the Fourier Transform]]<br /> <br /> Please see [http://www.brainmapping.org/NITP/PNA/html/Linearity.html MATLAB linearity demo]<br /> <br /> If you are the type who sees beauty in mathematics, the Euler identity may be one of the most beautiful pieces of math in the world.<br /> <br /> =Week 3: Math &amp; Circuits I=<br /> <br /> ==''Monday 10/10/16''==<br /> === - Circuits I. Speaker: Cameron Rodriguez ===<br /> :*[[media:Circuits_2016.pdf | Circuit slides shown in class]]<br /> <br /> ==''Wednesday 10/12/16''==<br /> === - Circuits II. ''Speaker'': Cameron Rodriguez ===<br /> <br /> Why circuits?<br /> :(Virtually) Every device you use in your research is electronic. You access your primary data only indirectly<br /> :The device you ''really'' want in your lab doesn't exist. You very well may have to make it.<br /> :There are electronic analogs to most of the linear systems that you have so far studied (and ''vice versa'' - the tools you now understand can be used to analyze and predict circuit behavior).<br /> <br /> :If you have not had any of this background, you might want to have a look at this handout, [[Media:Electricity.pdf|Electrical Circuits]], in advance. There are near infinite numbers of resources on the web that cover similar material (near enough to infinite that by the time you read all of them, there would be a whole new set.) I have recently come across a link to [http://www.allaboutcircuits.com/ Online Books: All About Circuits] ''IF'' you want practical hands-on knowledge about this material, my all-time favorite text is [http://www.google.com/products/catalog?hl=en&amp;client=safari&amp;rls=en-us&amp;ei=uVSPSfaxE5nMsAPf-tmSCQ&amp;resnum=1&amp;q=art+of+electronics&amp;um=1&amp;ie=UTF-8&amp;cid=8820839049329255765#ps-sellers &quot;Horowitz and Hill: ''The Art of Electronics.''&quot;] The latest edition, however, is dated 1989 and a new third edition is promised. I have therefore stopped short of recommending a purchase unless your need to make circuits is immediate. In this book, you will find an excellent education on the fundamental principles of electrical circuits and an incredible compendium of practical data, such as how to assemble circuit boards, how to make measurements, etc...) <br /> <br /> Readings:<br /> :*[http://www.ti.com/lit/an/sloa093/sloa093.pdf Filter Design in 30 Seconds]<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapter 2 and 10<br /> :*[https://www.circuitlab.com/ Circuit Lab ] A Free Circuit Web Base Simulator<br /> **You may or may not find this comprehensible without chapters 5 through 9.<br /> <br /> We will discuss:<br /> :*Passive Circuit Elements: Resistors, Capacitors, Inductors<br /> :*Gain<br /> :*Transformers<br /> :*Rectifiers<br /> :*Active Elements<br /> ::- ''Amplifiers''<br /> ::- ''Transistors''<br /> ::- ''Op Amps''<br /> :*Solutions with Matrices<br /> ''Suggested Further Reading''<br /> :*[http://video.google.com/videoplay?docid=5645396659673218353&amp;q=Physics+for+Future+Presidents+Electricity&amp;total=5&amp;start=0&amp;num=10&amp;so=0&amp;type=search&amp;plindex=0#0h20m30s Video intro lecture on charge, current and voltage].<br /> :*[[media:Electricity_Basics.pdf‎ | Well organized text on electrical concepts by Tony R. Kuphaldt]]<br /> :*[http://en.wikibooks.org/wiki/Circuit_Theory Circuit Theory - Wikibook]<br /> <br /> '''Suggested, Optional Readings from [http://www.dspguide.com DSPguide.com]:'''<br /> :*[http://www.dspguide.com/CH5.PDF Linear Systems]<br /> :*[http://www.dspguide.com/CH6.PDF Convolution]<br /> :*[http://www.dspguide.com/CH8.PDF Discrete Fourier Transform (DFT)]<br /> :''Note: These chapters are light on math and try to focus on a conceptual understanding''<br /> <br /> Time and Frequency / Spectral Filters<br /> <br /> ----<br /> Practice using the Fourier transform:<br /> :[http://www.brainmapping.org/NITP/PNA/ConvFThtml/ConvolutionWorksheet.pdf Fourier transform and Convolution Worksheet]. &lt;!-- [http://www.brainmapping.org/NITP/PNA/ConvFThtml/ConvFT.html (''Solutions'').] --&gt;<br /> :[http://www.brainmapping.org/NITP/PNA/ConvFThtml/Something.wav Sound file for worksheet above.]<br /> <br /> Other<br /> :*[http://www.youtube.com/watch?feature=player_embedded&amp;v=7uHfjpU3OH0#! Getting Started with Arduino]<br /> <br /> =Week 4 Signal Processing=<br /> <br /> ==''Monday 10/17/16''==<br /> === - Signal Processing. ''Speaker'': Cameron Rodriguez ===<br /> <br /> :*[[media:Signals1-PreProcessing1_2016.pdf | Signal Pre Processing Slide Set 1 shown in class]]<br /> <br /> <br /> ==''Wednesday 10/19/16''==<br /> === - Noise ''Speaker'': Cameron Rodriguez ===<br /> <br /> :*[[media:OneD_signal_Filter_Ex.pdf | Matlab Signal Tutorial printout]]<br /> <br /> It is what you ''don't'' want - usually - but things change in quantized systems<br /> :Additive noise<br /> :White Noise<br /> :Boltzmann noise<br /> :Colored Noise<br /> :Gaussian Noise<br /> :Coherent noise<br /> :Sampling Errors<br /> :Aliasing<br /> :Quantization noise<br /> :Spectral filtering<br /> <br /> Noise comes in all shapes and colors. It is present in every measurement we make, from an EEG voltage to an estimate of the effects of dopamine on forebrain signal. Our best weapons are an understanding of the statistical properties of noise, the sources of noise and the ways to control it. Noise in the discrete digital domain is special, as it is both ''created'' by digitization and amplified by sampling.<br /> <br /> Readings:<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapters 2 through 4<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Noise.pdf Noise Slides]<br /> <br /> =Week 5 - Information &amp; Statistical Theory =<br /> <br /> ==''Monday 10/24/16 - in C8-177''==<br /> === Noise Cont' &amp; Information Theory Speakers: Cameron Rodriguez &amp; John Villasenor===<br /> <br /> ==''Wednesday 10/26/16''==<br /> ===Statistical Theory ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> We will consider the general problems of statistical inference, with a concentration on developing an intuitive understanding of statistical concepts.<br /> [[Image:MeasureForMeasure.jpg|right]]<br /> <br /> Suggested reading<br /> :*[http://www.statsoft.com/textbook/stbasic.html Statsoft online text (''free'')]<br /> :*[http://www.amazon.com/Cartoon-Guide-Statistics-Larry-Gonick/dp/0062731025 The Cartoon Guide to Statistics - Gonick $17.95 new]<br /> :The latter teaches stats at what I feel to be the right level - developing intuitions about the kinds of questions that can be answered using stats and about the statistical tests and measures<br /> <br /> :'''Problem Set 3 - Statistics in matlab'''<br /> ::[[media: Problem_Set_1.doc|Problem set using stats and MATLAB]]<br /> ::[[media: Problem_Set_1B.doc|More practice with stats and MATLAB]]<br /> <br /> <br /> =MIDTERM=<br /> <br /> <br /> =Week 6 - Neurophysiology=<br /> ==''Monday 10/31/16''==<br /> === Electrophysiology I ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> We will examine our first imaging modality, EEG (and MEG).<br /> <br /> ::[[media: EEGNeurophys_161102.pdf|Slides for Neurophysiology Lectures]]<br /> <br /> ==''Wednesday 11/2/16''==<br /> === Electrophysiology II ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> Continuing with neurophysiology. ERPs &amp; Source Localization.<br /> <br /> =Week 7 - Neurophysiology =<br /> ==''Monday 11/7/16''==<br /> === Electrophysiology III ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> Continuing with neurophysiology. Connectivity &amp; Oscillations.<br /> <br /> ==''Wednesday 11/9/16''==<br /> ===Intracranial Recordings ''Speaker'': [http://neurosurgery.ucla.edu/neuromodulation-neuroimaging-lab Nanthia Sultana]===<br /> &lt;br&gt;<br /> <br /> =Week 8 - Practical Neurophysiology=<br /> ==''Monday 11/14/16''==<br /> ===Building an EEG System - Circuit 1 ''Speaker'': Cameron Rodriguez===<br /> <br /> ==''Wednesday 11/16/16''==<br /> ===Building an EEG System - Circuit 2 ''Speaker'': Cameron Rodriguez===<br /> <br /> :*[https://learn.sparkfun.com/tutorials/how-to-solder Through holesoldering].<br /> :*[https://learn.sparkfun.com/tutorials/pcb-basics Printed Circuit Board Basics].<br /> :*[https://learn.sparkfun.com/tutorials/how-to-install-and-setup-eagle Installing and Setting Up Eagle].<br /> :*[https://learn.sparkfun.com/tutorials/using-eagle-schematic Using Eagle Schematic].<br /> :*[https://learn.sparkfun.com/tutorials/using-eagle-board-layoutc Using Eagle Board Layout].<br /> <br /> :*[https://www.sparkfun.com/videos#pete/NJKZZArjdg8 PCB Layout 1 - According to Pete];<br /> :*[https://www.sparkfun.com/videos#pete/JANZsjRiM3w PCB Layout 2 - According to Pete];<br /> <br /> <br /> =Week 9 - Magnetic Resonance Fundamentals =<br /> ==''Monday 11/21/16 - in C8-177''==<br /> ===MR Signal Origin ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ==''Wednesday 11/23/16''==<br /> ===Thankgiving Wed - No Class ===<br /> <br /> Magnetic Resonance Imaging (MRI) is probably the most influential and most flexible current means of imaging the human brain. It features a vast number of separable contrast mechanisms, and a near ideal combination of non-invasiveness, safety, resolution and metric accuracy. However, it is extraordinarily expensive and has limited temporal resolution, especially for functional studies<br /> <br /> OUTLINE<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> :*[http://www.cis.rit.edu/htbooks/mri/ These notes] by Joseph Hornak are highly professional and complete coverage of MRI.<br /> :*[http://www.imaios.com/en/e-Courses/e-MRI eMRI] is another excellent online MRI learning resource<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Hahn1950.pdf Erwin Hahn - Spin Echoes: ''Essential reading for the MRI community'']<br /> [[Image:HahnFig1.png]]<br /> ''above'': Figure 1 from Hahn, 1950<br /> <br /> ''Suggested Further Reading''<br /> [[image:PSatSeq.jpg|right]]<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> <br /> =Week 10 - Magnetic Resonance Fundamentals =<br /> ==''Monday 11/28/16''==<br /> ===MR Physics ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ==''Wednesady 11/30/16''==<br /> ===TBD/Q&amp;A===<br /> <br /> <br /> =Week 11 - Finals Week=<br /> ==''Monday 11/5/16''==<br /> ===Final Exam===<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!--<br /> SPARE CONTENT / PREVIOUS YEARS<br /> <br /> =Week 3: Stats begins!=<br /> <br /> ==''Monday 10/12/15''==<br /> <br /> ===- Statistical Fundamentals. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> :*[[media: CohenClassIntroStats11_25_13.pdf‎ | Slides used in class (set 1)]]<br /> <br /> ''Review of'':<br /> :*Descriptive Statistics: mean, mode, variance, standard deviation<br /> :*Statistical Inference. The Binomial and Normal Distribution<br /> :*Basic Tests: t-test, linear correlation<br /> :*Modeling and non-linear relations<br /> :*Bayes rule<br /> <br /> <br /> ==''Wednesday 10/14/15''==<br /> ===- Statistical Fundamentals II. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> =Week 4 - Stats and circuits=<br /> ==''Monday 10/19/15''==<br /> ===- Statistical Fundamentals III. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> ==''Wednesday 10/21/15''==<br /> ===Circuits continued: Active devices. [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ===Optics I. ''Speaker'': [mailto:zdeis@seas.ucla.edu Zachary Taylor]===<br /> [[Image:Reflection.jpg|right]]<br /> The overall goal of this lecture is to establish that:<br /> ''- Physical constants have tangible meanings''<br /> ''- Plane waves form a physically unrealizable but extremely good approximation to real systems''<br /> ''- Boundaries bend light''<br /> ''- Physical constants, plane wave mechanics, and boundaries can be used to describe the operation of a lens''<br /> ''- The PSF gives a good indication of the overall performance of an imaging system''<br /> ''- All of these concepts have analogues in other areas of engineering (ie circuits, mechanical vibrations, etc.)''''<br /> <br /> '''Outline:'''<br /> :* Constitutive parameters (ε, μ, η, n, etc.)<br /> :* Plane wave basics<br /> :* Plane waves at boundaries<br /> :* Lenses<br /> :* Advanced imaging properties of lenses<br /> :* Point spread function.<br /> <br /> ''Required Readings''<br /> Zach has very kindly agreed to post his [http://www.brainmapping.org/NITP/PNA/Readings/OpticsTaylor3-10-10.pdf Optics lecture notes].<br /> ''Suggested Further Reading''<br /> <br /> <br /> <br /> <br /> ==''Wednesday 11/19/14''== <br /> ===Optogenetics. ''Speaker'': [http://faculty.neuroscience.ucla.edu/institution/personnel?personnel_id=47031 Tom Otis]===<br /> <br /> <br /> =Week 8: =<br /> ==''Monday 12/1/14 ''==<br /> ===- Statistics for Imaging. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> [[image:BVTradeoff.jpg|right]]<br /> ''Required Readings''<br /> :*[[media: Mumford_stat_modeling.pdf | Statistical Modeling and Inference (pdf)]]<br /> :*[[media: CohenClassSlides12_2_13.pdf | Slides used in class 12/2/14 and 12/4/14]]<br /> <br /> :*The General Linear Model<br /> *Linear Algebra applied to Statistical Solutions<br /> :*Analysis of Variance<br /> <br /> ==''Wednesday 11/20/14 ...''==<br /> === - Statistics for Imaging II. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> *Fixed and Random Effects<br /> *Repeated measures<br /> :*Bonferroni and Other Corrections<br /> *Non-Parametric Methods<br /> *Autocorrelation<br /> *Unknown Distributions<br /> <br /> ''Required Readings''<br /> :*[[media: CohenClassSlides12_2_13.pdf‎ | same as 12/2/14]]<br /> <br /> ''Suggested Further Reading''<br /> <br /> :*[http://ccn.ucla.edu/media/PNI20121121.mp4 Lecture Video - To be Posted Shortly - Please Right Click and Save rather than Stream - Thank you Alan and Edward!]<br /> <br /> =Week 9: =<br /> ==''Monday 11/25/14''==<br /> === - TBD. [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> ==''Wednesday 11/27/14''==<br /> ===- The Organization of the Human Brain. ''Speaker'': [http://ccn.ucla.edu/bmcweb/bmc_bios/SusanBookheimer/ Susan Bookheimer]===<br /> <br /> We will discuss the general organization of the human brain, and the regional specialization of cortical areas. The emphasis will be on understanding principles of organization:<br /> *Phylogenetic Layering<br /> *Functional Specialization<br /> *Principles Divisions of the Brain<br /> *Brain Systems<br /> <br /> ''Required Readings''<br /> :*[http://da.biostr.washington.edu:80/cgi-bin/DA/PageMaster?atlas:NeuroSyllabus+ffpathIndex/Splash^Page^Syllabus+2 Neuroanatomy Programmed Learning]<br /> :*[[media:Cognitive_science_and_neuro_2012.pdf | Slides shown in Class UPDATED 2012/10/17]]<br /> ''Suggested Further Reading''<br /> <br /> :*[[media:PNIA_2012_PS1_Soln.pdf‎ | Solution to Problem Set 1]]<br /> ===- Human Electrophysiology ''Speakers'': [http://greenlab.npih.ucla.edu/ROSTER.html Agatha Lenartowicz], [http://dgsom.healthsciences.ucla.edu/institution/personnel?personnel_id=9140 John Stern]===<br /> <br /> ''Evoked Responses'' - Guest Lecturer: [http://greenlab.npih.ucla.edu/ROSTER.html Jonathan Wynn]<br /> *A look at real EEG data<br /> *Preprocessing:<br /> **filtering<br /> **artifact detection/removal<br /> *averaging<br /> *single events<br /> *interpretation<br /> <br /> ''Clinical EEG'' - Guest Lecturer: [http://dgsom.healthsciences.ucla.edu/institution/personnel?personnel_id=9140 John Stern]<br /> *Normal and Abnormal EEG<br /> *EEG as a marker for brain state<br /> **sleep staging<br /> **alpha and relaxation<br /> *Neurofeedback??? --&gt;</div>

Alenartowicz https://www.ccn.ucla.edu/wiki/index.php?title=File:EEGNeurophys_161102.pdf&diff=3208 2016-11-03T16:08:20Z

<p>Alenartowicz: </p> <hr /> <div></div>

Alenartowicz https://www.ccn.ucla.edu/wiki/index.php?title=Principles_of_Neuroimaging_A_-_2016&diff=3207 2016-11-03T16:07:17Z

<p>Alenartowicz: </p> <hr /> <div>Principles of Neuroimaging A, Fall, 2016 - Class Schedule and Syllabus<br /> <br /> <br /> ==='''Neuroimaging journal Club (''required for NITP certificate'')'''===<br /> Contact: Katherine Lawrence (katherine.E.Lawrence@ucla.edu) or Janelle Liu (janelle.j.liu@ucla.edu), Faculty Sponsor: Jamie Feusner (JFeusner@mednet.ucla.edu)<br /> <br /> ==This schedule ''will'' change!==<br /> <br /> :'''[[Principles_of_Neuroimaging_-_2016-2017 | Back to main course page for Principles of Neuroimaging]]'''<br /> <br /> :'''Coming up next!: [[Principles_of_Neuroimaging_B_-_2017 | M284B Principles of Neuroimaging B]]'''<br /> <br /> =Course Reading=<br /> ===Required Reading===<br /> :'''Signal Processing for Neuroscientists''' by ''Wim van Drongelen''<br /> ::This can be found as a PDF on scribd.com, for a small fee of $8.99<br /> <br /> ===Supplemental Reading===<br /> :'''Matlab for Neuroscientists'''<br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/88212458/Matlab-Matlab-for-Neuroscientists<br /> <br /> :'''Cartoon Guide to Statistics''' <br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/148072668/Cartoon-Guide-to-Statistics<br /> <br /> :'''NOTE:''' if you subscribe for a Scribd account for a day, you can download as many documents as you like for one fee.<br /> <br /> <br /> =Week 1: Orientation to Neuro-imaging, Neurons, Brains=<br /> <br /> ==''Monday 9/26/16'' ==<br /> ===- Images. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284AImages-intro.pdf | Lecture Slides Images]]<br /> <br /> ==''Wednesday 9/28/16''==<br /> ===- Neurons &amp; Signaling. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> In this first class we will review the basics of neurophysiology with an eye towards what signals of brain function might be visible to the neuroimager. We will discuss information coding, energetics, size and time scales.<br /> [[Image:Neurons.jpg|right]]<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:TheActiveBrain.pdf | The Active Brain]]<br /> :*[[media:NeuronFunctionM284-2016_SM.pdf | Neuron function slides shown in class]]<br /> :*[http://www.science.smith.edu/departments/neurosci/courses/bio330/squid.html Videos of giant squid experiments]<br /> ''Suggested Further Reading''<br /> :*[http://ccn.ucla.edu/wiki/images/5/5a/CAVEAT_LECTOR.pdf Caveat Lector - the misuse of neuroimaging]<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Protected/Kosslyn1999.pdf &quot;If Neuroimaging is the Answer, What is the Question?&quot; Kosslyn, 1999]<br /> :*[http://da.biostr.washington.edu:80/cgi-bin/DA/PageMaster?atlas:NeuroSyllabus+ffpathIndex/Splash^Page^Syllabus+2 Neuroanatomy Programmed Learning]<br /> :*[http://www.amazon.com/Fundamental-Neuroscience-Second-Larry-Squire/dp/0126603030 Squire, Fundamentals of Neuroscience]<br /> :*[http://www.amazon.com/Principles-Neural-Science-Eric-Kandel/dp/0838577016 Kandel, et al., &quot;Principles of Neural Science&quot;]<br /> :This paper, by Malhi, is a nice orientation in methods of neuroimaging. *[http://www.ccn.ucla.edu/wiki/images/f/f2/Malhi2007.pdf Making sense of neuroimaging in psychiatry]<br /> :*[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1359308/pdf/jphysiol01232-0142.pdf Replacement of the axoplasm of giant nerve fibres with artificial solutions]<br /> <br /> =Week 2: Linear Systems, Convolution, Fourier Transforms=<br /> ==''Wednesday 10/3/16''==<br /> <br /> ===- Linear Systems I. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ==''Wednesday 10/5/16''==<br /> ===- Linear Systems II. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> Why the emphasis on Linear Systems? Because they are actually ''easy'' (as compared to non-linear systems, which are not.) As we go through this course, we will see many ways in which linear systems theory is applied to:<br /> :Modeling of Neural Systems<br /> :Extraction of Signal from Noise<br /> :Design of Circuits<br /> :Image Enhancement<br /> :Understanding of Image artifacts, and others.<br /> <br /> Linear systems analysis is one of the great technologies of the 20th and 21st century. It is now the basis for virtually all electronics design, and its extension into the discrete (digital) domain is the basis for most of modern signal processing. <br /> <br /> In our specific case, we will use these few basic principles of linear systems to understand both the instruments we use and the neuroimaging signals we collect. When you have mastered this material, you should be in a much better position to model the systems that you study in order to develop an approach to studying them.<br /> <br /> Here is [http://www.brainmapping.org/NITP/PNA/Readings/ImaginaryNumbers.pdf A primer I wrote on imaginary numbers] that might be a helpful review.<br /> <br /> There is a nice [http://en.wikibooks.org/wiki/Calculus Wikibook on Calculus].<br /> <br /> ''Required Readings''<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapter 1<br /> :*[[media:MathematicalTools2016.pdf | Mathematical Tools]]<br /> <br /> ''Suggested Further Reading''<br /> :'''Introduction to matlab'''<br /> <br /> ''Slides shown in class''<br /> :[[media:LinearityM284A-2016.pdf | Linearity and the Fourier Transform]]<br /> <br /> Please see [http://www.brainmapping.org/NITP/PNA/html/Linearity.html MATLAB linearity demo]<br /> <br /> If you are the type who sees beauty in mathematics, the Euler identity may be one of the most beautiful pieces of math in the world.<br /> <br /> =Week 3: Math &amp; Circuits I=<br /> <br /> ==''Monday 10/10/16''==<br /> === - Circuits I. Speaker: Cameron Rodriguez ===<br /> :*[[media:Circuits_2016.pdf | Circuit slides shown in class]]<br /> <br /> ==''Wednesday 10/12/16''==<br /> === - Circuits II. ''Speaker'': Cameron Rodriguez ===<br /> <br /> Why circuits?<br /> :(Virtually) Every device you use in your research is electronic. You access your primary data only indirectly<br /> :The device you ''really'' want in your lab doesn't exist. You very well may have to make it.<br /> :There are electronic analogs to most of the linear systems that you have so far studied (and ''vice versa'' - the tools you now understand can be used to analyze and predict circuit behavior).<br /> <br /> :If you have not had any of this background, you might want to have a look at this handout, [[Media:Electricity.pdf|Electrical Circuits]], in advance. There are near infinite numbers of resources on the web that cover similar material (near enough to infinite that by the time you read all of them, there would be a whole new set.) I have recently come across a link to [http://www.allaboutcircuits.com/ Online Books: All About Circuits] ''IF'' you want practical hands-on knowledge about this material, my all-time favorite text is [http://www.google.com/products/catalog?hl=en&amp;client=safari&amp;rls=en-us&amp;ei=uVSPSfaxE5nMsAPf-tmSCQ&amp;resnum=1&amp;q=art+of+electronics&amp;um=1&amp;ie=UTF-8&amp;cid=8820839049329255765#ps-sellers &quot;Horowitz and Hill: ''The Art of Electronics.''&quot;] The latest edition, however, is dated 1989 and a new third edition is promised. I have therefore stopped short of recommending a purchase unless your need to make circuits is immediate. In this book, you will find an excellent education on the fundamental principles of electrical circuits and an incredible compendium of practical data, such as how to assemble circuit boards, how to make measurements, etc...) <br /> <br /> Readings:<br /> :*[http://www.ti.com/lit/an/sloa093/sloa093.pdf Filter Design in 30 Seconds]<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapter 2 and 10<br /> :*[https://www.circuitlab.com/ Circuit Lab ] A Free Circuit Web Base Simulator<br /> **You may or may not find this comprehensible without chapters 5 through 9.<br /> <br /> We will discuss:<br /> :*Passive Circuit Elements: Resistors, Capacitors, Inductors<br /> :*Gain<br /> :*Transformers<br /> :*Rectifiers<br /> :*Active Elements<br /> ::- ''Amplifiers''<br /> ::- ''Transistors''<br /> ::- ''Op Amps''<br /> :*Solutions with Matrices<br /> ''Suggested Further Reading''<br /> :*[http://video.google.com/videoplay?docid=5645396659673218353&amp;q=Physics+for+Future+Presidents+Electricity&amp;total=5&amp;start=0&amp;num=10&amp;so=0&amp;type=search&amp;plindex=0#0h20m30s Video intro lecture on charge, current and voltage].<br /> :*[[media:Electricity_Basics.pdf‎ | Well organized text on electrical concepts by Tony R. Kuphaldt]]<br /> :*[http://en.wikibooks.org/wiki/Circuit_Theory Circuit Theory - Wikibook]<br /> <br /> '''Suggested, Optional Readings from [http://www.dspguide.com DSPguide.com]:'''<br /> :*[http://www.dspguide.com/CH5.PDF Linear Systems]<br /> :*[http://www.dspguide.com/CH6.PDF Convolution]<br /> :*[http://www.dspguide.com/CH8.PDF Discrete Fourier Transform (DFT)]<br /> :''Note: These chapters are light on math and try to focus on a conceptual understanding''<br /> <br /> Time and Frequency / Spectral Filters<br /> <br /> ----<br /> Practice using the Fourier transform:<br /> :[http://www.brainmapping.org/NITP/PNA/ConvFThtml/ConvolutionWorksheet.pdf Fourier transform and Convolution Worksheet]. &lt;!-- [http://www.brainmapping.org/NITP/PNA/ConvFThtml/ConvFT.html (''Solutions'').] --&gt;<br /> :[http://www.brainmapping.org/NITP/PNA/ConvFThtml/Something.wav Sound file for worksheet above.]<br /> <br /> Other<br /> :*[http://www.youtube.com/watch?feature=player_embedded&amp;v=7uHfjpU3OH0#! Getting Started with Arduino]<br /> <br /> =Week 4 Signal Processing=<br /> <br /> ==''Monday 10/17/16''==<br /> === - Signal Processing. ''Speaker'': Cameron Rodriguez ===<br /> <br /> :*[[media:Signals1-PreProcessing1_2016.pdf | Signal Pre Processing Slide Set 1 shown in class]]<br /> <br /> <br /> ==''Wednesday 10/19/16''==<br /> === - Noise ''Speaker'': Cameron Rodriguez ===<br /> <br /> :*[[media:OneD_signal_Filter_Ex.pdf | Matlab Signal Tutorial printout]]<br /> <br /> It is what you ''don't'' want - usually - but things change in quantized systems<br /> :Additive noise<br /> :White Noise<br /> :Boltzmann noise<br /> :Colored Noise<br /> :Gaussian Noise<br /> :Coherent noise<br /> :Sampling Errors<br /> :Aliasing<br /> :Quantization noise<br /> :Spectral filtering<br /> <br /> Noise comes in all shapes and colors. It is present in every measurement we make, from an EEG voltage to an estimate of the effects of dopamine on forebrain signal. Our best weapons are an understanding of the statistical properties of noise, the sources of noise and the ways to control it. Noise in the discrete digital domain is special, as it is both ''created'' by digitization and amplified by sampling.<br /> <br /> Readings:<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapters 2 through 4<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Noise.pdf Noise Slides]<br /> <br /> =Week 5 - Information &amp; Statistical Theory =<br /> <br /> ==''Monday 10/24/16 - in C8-177''==<br /> === Noise Cont' &amp; Information Theory Speakers: Cameron Rodriguez &amp; John Villasenor===<br /> <br /> ==''Wednesday 10/26/16''==<br /> ===Statistical Theory ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> We will consider the general problems of statistical inference, with a concentration on developing an intuitive understanding of statistical concepts.<br /> [[Image:MeasureForMeasure.jpg|right]]<br /> <br /> Suggested reading<br /> :*[http://www.statsoft.com/textbook/stbasic.html Statsoft online text (''free'')]<br /> :*[http://www.amazon.com/Cartoon-Guide-Statistics-Larry-Gonick/dp/0062731025 The Cartoon Guide to Statistics - Gonick $17.95 new]<br /> :The latter teaches stats at what I feel to be the right level - developing intuitions about the kinds of questions that can be answered using stats and about the statistical tests and measures<br /> <br /> :'''Problem Set 3 - Statistics in matlab'''<br /> ::[[media: Problem_Set_1.doc|Problem set using stats and MATLAB]]<br /> ::[[media: Problem_Set_1B.doc|More practice with stats and MATLAB]]<br /> <br /> <br /> =MIDTERM=<br /> <br /> <br /> =Week 6 - Neurophysiology=<br /> ==''Monday 10/31/16''==<br /> === Electrophysiology I ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> We will examine our first imaging modality, EEG (and MEG).<br /> <br /> ::[[media: File.doc|Slides for Neurophysiology Lectures]]<br /> <br /> ==''Wednesday 11/2/16''==<br /> === Electrophysiology II ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> Continuing with neurophysiology. ERPs &amp; Source Localization.<br /> <br /> =Week 7 - Neurophysiology =<br /> ==''Monday 11/7/16''==<br /> === Electrophysiology III ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> Continuing with neurophysiology. Connectivity &amp; Oscillations.<br /> <br /> ==''Wednesday 11/9/16''==<br /> ===Intracranial Recordings ''Speaker'': [http://neurosurgery.ucla.edu/neuromodulation-neuroimaging-lab Nanthia Sultana]===<br /> &lt;br&gt;<br /> <br /> =Week 8 - Practical Neurophysiology=<br /> ==''Monday 11/14/16''==<br /> ===Building an EEG System - Circuit 1 ''Speaker'': Cameron Rodriguez===<br /> <br /> ==''Wednesday 11/16/16''==<br /> ===Building an EEG System - Circuit 2 ''Speaker'': Cameron Rodriguez===<br /> <br /> :*[https://learn.sparkfun.com/tutorials/how-to-solder Through holesoldering].<br /> :*[https://learn.sparkfun.com/tutorials/pcb-basics Printed Circuit Board Basics].<br /> :*[https://learn.sparkfun.com/tutorials/how-to-install-and-setup-eagle Installing and Setting Up Eagle].<br /> :*[https://learn.sparkfun.com/tutorials/using-eagle-schematic Using Eagle Schematic].<br /> :*[https://learn.sparkfun.com/tutorials/using-eagle-board-layoutc Using Eagle Board Layout].<br /> <br /> :*[https://www.sparkfun.com/videos#pete/NJKZZArjdg8 PCB Layout 1 - According to Pete];<br /> :*[https://www.sparkfun.com/videos#pete/JANZsjRiM3w PCB Layout 2 - According to Pete];<br /> <br /> <br /> =Week 9 - Magnetic Resonance Fundamentals =<br /> ==''Monday 11/21/16 - in C8-177''==<br /> ===MR Signal Origin ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ==''Wednesday 11/23/16''==<br /> ===Thankgiving Wed - No Class ===<br /> <br /> Magnetic Resonance Imaging (MRI) is probably the most influential and most flexible current means of imaging the human brain. It features a vast number of separable contrast mechanisms, and a near ideal combination of non-invasiveness, safety, resolution and metric accuracy. However, it is extraordinarily expensive and has limited temporal resolution, especially for functional studies<br /> <br /> OUTLINE<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> :*[http://www.cis.rit.edu/htbooks/mri/ These notes] by Joseph Hornak are highly professional and complete coverage of MRI.<br /> :*[http://www.imaios.com/en/e-Courses/e-MRI eMRI] is another excellent online MRI learning resource<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Hahn1950.pdf Erwin Hahn - Spin Echoes: ''Essential reading for the MRI community'']<br /> [[Image:HahnFig1.png]]<br /> ''above'': Figure 1 from Hahn, 1950<br /> <br /> ''Suggested Further Reading''<br /> [[image:PSatSeq.jpg|right]]<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> <br /> =Week 10 - Magnetic Resonance Fundamentals =<br /> ==''Monday 11/28/16''==<br /> ===MR Physics ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ==''Wednesady 11/30/16''==<br /> ===TBD/Q&amp;A===<br /> <br /> <br /> =Week 11 - Finals Week=<br /> ==''Monday 11/5/16''==<br /> ===Final Exam===<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!--<br /> SPARE CONTENT / PREVIOUS YEARS<br /> <br /> =Week 3: Stats begins!=<br /> <br /> ==''Monday 10/12/15''==<br /> <br /> ===- Statistical Fundamentals. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> :*[[media: CohenClassIntroStats11_25_13.pdf‎ | Slides used in class (set 1)]]<br /> <br /> ''Review of'':<br /> :*Descriptive Statistics: mean, mode, variance, standard deviation<br /> :*Statistical Inference. The Binomial and Normal Distribution<br /> :*Basic Tests: t-test, linear correlation<br /> :*Modeling and non-linear relations<br /> :*Bayes rule<br /> <br /> <br /> ==''Wednesday 10/14/15''==<br /> ===- Statistical Fundamentals II. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> =Week 4 - Stats and circuits=<br /> ==''Monday 10/19/15''==<br /> ===- Statistical Fundamentals III. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> ==''Wednesday 10/21/15''==<br /> ===Circuits continued: Active devices. [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ===Optics I. ''Speaker'': [mailto:zdeis@seas.ucla.edu Zachary Taylor]===<br /> [[Image:Reflection.jpg|right]]<br /> The overall goal of this lecture is to establish that:<br /> ''- Physical constants have tangible meanings''<br /> ''- Plane waves form a physically unrealizable but extremely good approximation to real systems''<br /> ''- Boundaries bend light''<br /> ''- Physical constants, plane wave mechanics, and boundaries can be used to describe the operation of a lens''<br /> ''- The PSF gives a good indication of the overall performance of an imaging system''<br /> ''- All of these concepts have analogues in other areas of engineering (ie circuits, mechanical vibrations, etc.)''''<br /> <br /> '''Outline:'''<br /> :* Constitutive parameters (ε, μ, η, n, etc.)<br /> :* Plane wave basics<br /> :* Plane waves at boundaries<br /> :* Lenses<br /> :* Advanced imaging properties of lenses<br /> :* Point spread function.<br /> <br /> ''Required Readings''<br /> Zach has very kindly agreed to post his [http://www.brainmapping.org/NITP/PNA/Readings/OpticsTaylor3-10-10.pdf Optics lecture notes].<br /> ''Suggested Further Reading''<br /> <br /> <br /> <br /> <br /> ==''Wednesday 11/19/14''== <br /> ===Optogenetics. ''Speaker'': [http://faculty.neuroscience.ucla.edu/institution/personnel?personnel_id=47031 Tom Otis]===<br /> <br /> <br /> =Week 8: =<br /> ==''Monday 12/1/14 ''==<br /> ===- Statistics for Imaging. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> [[image:BVTradeoff.jpg|right]]<br /> ''Required Readings''<br /> :*[[media: Mumford_stat_modeling.pdf | Statistical Modeling and Inference (pdf)]]<br /> :*[[media: CohenClassSlides12_2_13.pdf | Slides used in class 12/2/14 and 12/4/14]]<br /> <br /> :*The General Linear Model<br /> *Linear Algebra applied to Statistical Solutions<br /> :*Analysis of Variance<br /> <br /> ==''Wednesday 11/20/14 ...''==<br /> === - Statistics for Imaging II. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> *Fixed and Random Effects<br /> *Repeated measures<br /> :*Bonferroni and Other Corrections<br /> *Non-Parametric Methods<br /> *Autocorrelation<br /> *Unknown Distributions<br /> <br /> ''Required Readings''<br /> :*[[media: CohenClassSlides12_2_13.pdf‎ | same as 12/2/14]]<br /> <br /> ''Suggested Further Reading''<br /> <br /> :*[http://ccn.ucla.edu/media/PNI20121121.mp4 Lecture Video - To be Posted Shortly - Please Right Click and Save rather than Stream - Thank you Alan and Edward!]<br /> <br /> =Week 9: =<br /> ==''Monday 11/25/14''==<br /> === - TBD. [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> ==''Wednesday 11/27/14''==<br /> ===- The Organization of the Human Brain. ''Speaker'': [http://ccn.ucla.edu/bmcweb/bmc_bios/SusanBookheimer/ Susan Bookheimer]===<br /> <br /> We will discuss the general organization of the human brain, and the regional specialization of cortical areas. The emphasis will be on understanding principles of organization:<br /> *Phylogenetic Layering<br /> *Functional Specialization<br /> *Principles Divisions of the Brain<br /> *Brain Systems<br /> <br /> ''Required Readings''<br /> :*[http://da.biostr.washington.edu:80/cgi-bin/DA/PageMaster?atlas:NeuroSyllabus+ffpathIndex/Splash^Page^Syllabus+2 Neuroanatomy Programmed Learning]<br /> :*[[media:Cognitive_science_and_neuro_2012.pdf | Slides shown in Class UPDATED 2012/10/17]]<br /> ''Suggested Further Reading''<br /> <br /> :*[[media:PNIA_2012_PS1_Soln.pdf‎ | Solution to Problem Set 1]]<br /> ===- Human Electrophysiology ''Speakers'': [http://greenlab.npih.ucla.edu/ROSTER.html Agatha Lenartowicz], [http://dgsom.healthsciences.ucla.edu/institution/personnel?personnel_id=9140 John Stern]===<br /> <br /> ''Evoked Responses'' - Guest Lecturer: [http://greenlab.npih.ucla.edu/ROSTER.html Jonathan Wynn]<br /> *A look at real EEG data<br /> *Preprocessing:<br /> **filtering<br /> **artifact detection/removal<br /> *averaging<br /> *single events<br /> *interpretation<br /> <br /> ''Clinical EEG'' - Guest Lecturer: [http://dgsom.healthsciences.ucla.edu/institution/personnel?personnel_id=9140 John Stern]<br /> *Normal and Abnormal EEG<br /> *EEG as a marker for brain state<br /> **sleep staging<br /> **alpha and relaxation<br /> *Neurofeedback??? --&gt;</div>

Alenartowicz https://www.ccn.ucla.edu/wiki/index.php?title=Principles_of_Neuroimaging_A_-_2016&diff=3206 2016-10-26T22:50:07Z

<p>Alenartowicz: /* Intracranial Recordings Speaker: Nanthia Sultana */</p> <hr /> <div>Principles of Neuroimaging A, Fall, 2016 - Class Schedule and Syllabus<br /> <br /> <br /> ==='''Neuroimaging journal Club (''required for NITP certificate'')'''===<br /> Contact: Katherine Lawrence (katherine.E.Lawrence@ucla.edu) or Janelle Liu (janelle.j.liu@ucla.edu), Faculty Sponsor: Jamie Feusner (JFeusner@mednet.ucla.edu)<br /> <br /> ==This schedule ''will'' change!==<br /> <br /> :'''[[Principles_of_Neuroimaging_-_2016-2017 | Back to main course page for Principles of Neuroimaging]]'''<br /> <br /> :'''Coming up next!: [[Principles_of_Neuroimaging_B_-_2017 | M284B Principles of Neuroimaging B]]'''<br /> <br /> =Course Reading=<br /> ===Required Reading===<br /> :'''Signal Processing for Neuroscientists''' by ''Wim van Drongelen''<br /> ::This can be found as a PDF on scribd.com, for a small fee of $8.99<br /> <br /> ===Supplemental Reading===<br /> :'''Matlab for Neuroscientists'''<br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/88212458/Matlab-Matlab-for-Neuroscientists<br /> <br /> :'''Cartoon Guide to Statistics''' <br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/148072668/Cartoon-Guide-to-Statistics<br /> <br /> :'''NOTE:''' if you subscribe for a Scribd account for a day, you can download as many documents as you like for one fee.<br /> <br /> <br /> =Week 1: Orientation to Neuro-imaging, Neurons, Brains=<br /> <br /> ==''Monday 9/26/16'' ==<br /> ===- Images. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284AImages-intro.pdf | Lecture Slides Images]]<br /> <br /> ==''Wednesday 9/28/16''==<br /> ===- Neurons &amp; Signaling. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> In this first class we will review the basics of neurophysiology with an eye towards what signals of brain function might be visible to the neuroimager. We will discuss information coding, energetics, size and time scales.<br /> [[Image:Neurons.jpg|right]]<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:TheActiveBrain.pdf | The Active Brain]]<br /> :*[[media:NeuronFunctionM284-2016_SM.pdf | Neuron function slides shown in class]]<br /> :*[http://www.science.smith.edu/departments/neurosci/courses/bio330/squid.html Videos of giant squid experiments]<br /> ''Suggested Further Reading''<br /> :*[http://ccn.ucla.edu/wiki/images/5/5a/CAVEAT_LECTOR.pdf Caveat Lector - the misuse of neuroimaging]<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Protected/Kosslyn1999.pdf &quot;If Neuroimaging is the Answer, What is the Question?&quot; Kosslyn, 1999]<br /> :*[http://da.biostr.washington.edu:80/cgi-bin/DA/PageMaster?atlas:NeuroSyllabus+ffpathIndex/Splash^Page^Syllabus+2 Neuroanatomy Programmed Learning]<br /> :*[http://www.amazon.com/Fundamental-Neuroscience-Second-Larry-Squire/dp/0126603030 Squire, Fundamentals of Neuroscience]<br /> :*[http://www.amazon.com/Principles-Neural-Science-Eric-Kandel/dp/0838577016 Kandel, et al., &quot;Principles of Neural Science&quot;]<br /> :This paper, by Malhi, is a nice orientation in methods of neuroimaging. *[http://www.ccn.ucla.edu/wiki/images/f/f2/Malhi2007.pdf Making sense of neuroimaging in psychiatry]<br /> :*[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1359308/pdf/jphysiol01232-0142.pdf Replacement of the axoplasm of giant nerve fibres with artificial solutions]<br /> <br /> =Week 2: Linear Systems, Convolution, Fourier Transforms=<br /> ==''Wednesday 10/3/16''==<br /> <br /> ===- Linear Systems I. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ==''Wednesday 10/5/16''==<br /> ===- Linear Systems II. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> Why the emphasis on Linear Systems? Because they are actually ''easy'' (as compared to non-linear systems, which are not.) As we go through this course, we will see many ways in which linear systems theory is applied to:<br /> :Modeling of Neural Systems<br /> :Extraction of Signal from Noise<br /> :Design of Circuits<br /> :Image Enhancement<br /> :Understanding of Image artifacts, and others.<br /> <br /> Linear systems analysis is one of the great technologies of the 20th and 21st century. It is now the basis for virtually all electronics design, and its extension into the discrete (digital) domain is the basis for most of modern signal processing. <br /> <br /> In our specific case, we will use these few basic principles of linear systems to understand both the instruments we use and the neuroimaging signals we collect. When you have mastered this material, you should be in a much better position to model the systems that you study in order to develop an approach to studying them.<br /> <br /> Here is [http://www.brainmapping.org/NITP/PNA/Readings/ImaginaryNumbers.pdf A primer I wrote on imaginary numbers] that might be a helpful review.<br /> <br /> There is a nice [http://en.wikibooks.org/wiki/Calculus Wikibook on Calculus].<br /> <br /> ''Required Readings''<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapter 1<br /> :*[[media:MathematicalTools2016.pdf | Mathematical Tools]]<br /> <br /> ''Suggested Further Reading''<br /> :'''Introduction to matlab'''<br /> <br /> ''Slides shown in class''<br /> :[[media:LinearityM284A-2016.pdf | Linearity and the Fourier Transform]]<br /> <br /> Please see [http://www.brainmapping.org/NITP/PNA/html/Linearity.html MATLAB linearity demo]<br /> <br /> If you are the type who sees beauty in mathematics, the Euler identity may be one of the most beautiful pieces of math in the world.<br /> <br /> =Week 3: Math &amp; Circuits I=<br /> <br /> ==''Monday 10/10/16''==<br /> === - Circuits I. Speaker: Cameron Rodriguez ===<br /> :*[[media:Circuits_2016.pdf | Circuit slides shown in class]]<br /> <br /> ==''Wednesday 10/12/16''==<br /> === - Circuits II. ''Speaker'': Cameron Rodriguez ===<br /> <br /> Why circuits?<br /> :(Virtually) Every device you use in your research is electronic. You access your primary data only indirectly<br /> :The device you ''really'' want in your lab doesn't exist. You very well may have to make it.<br /> :There are electronic analogs to most of the linear systems that you have so far studied (and ''vice versa'' - the tools you now understand can be used to analyze and predict circuit behavior).<br /> <br /> :If you have not had any of this background, you might want to have a look at this handout, [[Media:Electricity.pdf|Electrical Circuits]], in advance. There are near infinite numbers of resources on the web that cover similar material (near enough to infinite that by the time you read all of them, there would be a whole new set.) I have recently come across a link to [http://www.allaboutcircuits.com/ Online Books: All About Circuits] ''IF'' you want practical hands-on knowledge about this material, my all-time favorite text is [http://www.google.com/products/catalog?hl=en&amp;client=safari&amp;rls=en-us&amp;ei=uVSPSfaxE5nMsAPf-tmSCQ&amp;resnum=1&amp;q=art+of+electronics&amp;um=1&amp;ie=UTF-8&amp;cid=8820839049329255765#ps-sellers &quot;Horowitz and Hill: ''The Art of Electronics.''&quot;] The latest edition, however, is dated 1989 and a new third edition is promised. I have therefore stopped short of recommending a purchase unless your need to make circuits is immediate. In this book, you will find an excellent education on the fundamental principles of electrical circuits and an incredible compendium of practical data, such as how to assemble circuit boards, how to make measurements, etc...) <br /> <br /> Readings:<br /> :*[http://www.ti.com/lit/an/sloa093/sloa093.pdf Filter Design in 30 Seconds]<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapter 2 and 10<br /> :*[https://www.circuitlab.com/ Circuit Lab ] A Free Circuit Web Base Simulator<br /> **You may or may not find this comprehensible without chapters 5 through 9.<br /> <br /> We will discuss:<br /> :*Passive Circuit Elements: Resistors, Capacitors, Inductors<br /> :*Gain<br /> :*Transformers<br /> :*Rectifiers<br /> :*Active Elements<br /> ::- ''Amplifiers''<br /> ::- ''Transistors''<br /> ::- ''Op Amps''<br /> :*Solutions with Matrices<br /> ''Suggested Further Reading''<br /> :*[http://video.google.com/videoplay?docid=5645396659673218353&amp;q=Physics+for+Future+Presidents+Electricity&amp;total=5&amp;start=0&amp;num=10&amp;so=0&amp;type=search&amp;plindex=0#0h20m30s Video intro lecture on charge, current and voltage].<br /> :*[[media:Electricity_Basics.pdf‎ | Well organized text on electrical concepts by Tony R. Kuphaldt]]<br /> :*[http://en.wikibooks.org/wiki/Circuit_Theory Circuit Theory - Wikibook]<br /> <br /> '''Suggested, Optional Readings from [http://www.dspguide.com DSPguide.com]:'''<br /> :*[http://www.dspguide.com/CH5.PDF Linear Systems]<br /> :*[http://www.dspguide.com/CH6.PDF Convolution]<br /> :*[http://www.dspguide.com/CH8.PDF Discrete Fourier Transform (DFT)]<br /> :''Note: These chapters are light on math and try to focus on a conceptual understanding''<br /> <br /> Time and Frequency / Spectral Filters<br /> <br /> ----<br /> Practice using the Fourier transform:<br /> :[http://www.brainmapping.org/NITP/PNA/ConvFThtml/ConvolutionWorksheet.pdf Fourier transform and Convolution Worksheet]. &lt;!-- [http://www.brainmapping.org/NITP/PNA/ConvFThtml/ConvFT.html (''Solutions'').] --&gt;<br /> :[http://www.brainmapping.org/NITP/PNA/ConvFThtml/Something.wav Sound file for worksheet above.]<br /> <br /> Other<br /> :*[http://www.youtube.com/watch?feature=player_embedded&amp;v=7uHfjpU3OH0#! Getting Started with Arduino]<br /> <br /> =Week 4 Signal Processing=<br /> <br /> ==''Monday 10/17/16''==<br /> === - Signal Processing. ''Speaker'': Cameron Rodriguez ===<br /> <br /> :*[[media:Signals1-PreProcessing1_2016.pdf | Signal Pre Processing Slide Set 1 shown in class]]<br /> <br /> <br /> ==''Wednesday 10/19/16''==<br /> === - Noise ''Speaker'': Cameron Rodriguez ===<br /> <br /> :*[[media:OneD_signal_Filter_Ex.pdf | Matlab Signal Tutorial printout]]<br /> <br /> It is what you ''don't'' want - usually - but things change in quantized systems<br /> :Additive noise<br /> :White Noise<br /> :Boltzmann noise<br /> :Colored Noise<br /> :Gaussian Noise<br /> :Coherent noise<br /> :Sampling Errors<br /> :Aliasing<br /> :Quantization noise<br /> :Spectral filtering<br /> <br /> Noise comes in all shapes and colors. It is present in every measurement we make, from an EEG voltage to an estimate of the effects of dopamine on forebrain signal. Our best weapons are an understanding of the statistical properties of noise, the sources of noise and the ways to control it. Noise in the discrete digital domain is special, as it is both ''created'' by digitization and amplified by sampling.<br /> <br /> Readings:<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapters 2 through 4<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Noise.pdf Noise Slides]<br /> <br /> =Week 5 - Information &amp; Statistical Theory =<br /> <br /> ==''Monday 10/24/16 - in C8-177''==<br /> === Noise Cont' &amp; Information Theory Speakers: Cameron Rodriguez &amp; John Villasenor===<br /> <br /> ==''Wednesday 10/26/16''==<br /> ===Statistical Theory ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> We will consider the general problems of statistical inference, with a concentration on developing an intuitive understanding of statistical concepts.<br /> [[Image:MeasureForMeasure.jpg|right]]<br /> <br /> Suggested reading<br /> :*[http://www.statsoft.com/textbook/stbasic.html Statsoft online text (''free'')]<br /> :*[http://www.amazon.com/Cartoon-Guide-Statistics-Larry-Gonick/dp/0062731025 The Cartoon Guide to Statistics - Gonick $17.95 new]<br /> :The latter teaches stats at what I feel to be the right level - developing intuitions about the kinds of questions that can be answered using stats and about the statistical tests and measures<br /> <br /> :'''Problem Set 3 - Statistics in matlab'''<br /> ::[[media: Problem_Set_1.doc|Problem set using stats and MATLAB]]<br /> ::[[media: Problem_Set_1B.doc|More practice with stats and MATLAB]]<br /> <br /> <br /> =MIDTERM=<br /> <br /> <br /> =Week 6 - Neurophysiology=<br /> ==''Monday 10/31/16''==<br /> === Electrophysiology I ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> We will examine our first imaging modality, EEG (and MEG).<br /> <br /> ==''Wednesday 11/2/16''==<br /> === Electrophysiology II ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> Continuing with neurophysiology.<br /> <br /> =Week 7 - Neurophysiology =<br /> ==''Monday 11/7/16''==<br /> ===Midterm Review &amp; EEG Signal Processing ''Speaker'': Agatha Lenartowicz, Cameron Rodriguez===<br /> <br /> ==''Wednesday 11/9/16''==<br /> ===Intracranial Recordings ''Speaker'': [http://neurosurgery.ucla.edu/neuromodulation-neuroimaging-lab Nanthia Sultana]===<br /> &lt;br&gt;<br /> <br /> =Week 8 - Practical Neurophysiology=<br /> ==''Monday 11/14/16''==<br /> ===Building an EEG System - Circuit 1 ''Speaker'': Cameron Rodriguez===<br /> <br /> ==''Wednesday 11/16/16''==<br /> ===Building an EEG System - Circuit 2 ''Speaker'': Cameron Rodriguez===<br /> <br /> :*[https://learn.sparkfun.com/tutorials/how-to-solder Through holesoldering].<br /> :*[https://learn.sparkfun.com/tutorials/pcb-basics Printed Circuit Board Basics].<br /> :*[https://learn.sparkfun.com/tutorials/how-to-install-and-setup-eagle Installing and Setting Up Eagle].<br /> :*[https://learn.sparkfun.com/tutorials/using-eagle-schematic Using Eagle Schematic].<br /> :*[https://learn.sparkfun.com/tutorials/using-eagle-board-layoutc Using Eagle Board Layout].<br /> <br /> :*[https://www.sparkfun.com/videos#pete/NJKZZArjdg8 PCB Layout 1 - According to Pete];<br /> :*[https://www.sparkfun.com/videos#pete/JANZsjRiM3w PCB Layout 2 - According to Pete];<br /> <br /> <br /> =Week 9 - Magnetic Resonance Fundamentals =<br /> ==''Monday 11/21/16 - in C8-177''==<br /> ===MR Signal Origin ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ==''Wednesday 11/23/16''==<br /> ===Thankgiving Wed - No Class ===<br /> <br /> Magnetic Resonance Imaging (MRI) is probably the most influential and most flexible current means of imaging the human brain. It features a vast number of separable contrast mechanisms, and a near ideal combination of non-invasiveness, safety, resolution and metric accuracy. However, it is extraordinarily expensive and has limited temporal resolution, especially for functional studies<br /> <br /> OUTLINE<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> :*[http://www.cis.rit.edu/htbooks/mri/ These notes] by Joseph Hornak are highly professional and complete coverage of MRI.<br /> :*[http://www.imaios.com/en/e-Courses/e-MRI eMRI] is another excellent online MRI learning resource<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Hahn1950.pdf Erwin Hahn - Spin Echoes: ''Essential reading for the MRI community'']<br /> [[Image:HahnFig1.png]]<br /> ''above'': Figure 1 from Hahn, 1950<br /> <br /> ''Suggested Further Reading''<br /> [[image:PSatSeq.jpg|right]]<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> <br /> =Week 10 - Magnetic Resonance Fundamentals =<br /> ==''Monday 11/28/16''==<br /> ===MR Physics ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ==''Wednesady 11/30/16''==<br /> ===TBD/Q&amp;A===<br /> <br /> <br /> =Week 11 - Finals Week=<br /> ==''Monday 11/5/16''==<br /> ===Final Exam===<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!--<br /> SPARE CONTENT / PREVIOUS YEARS<br /> <br /> =Week 3: Stats begins!=<br /> <br /> ==''Monday 10/12/15''==<br /> <br /> ===- Statistical Fundamentals. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> :*[[media: CohenClassIntroStats11_25_13.pdf‎ | Slides used in class (set 1)]]<br /> <br /> ''Review of'':<br /> :*Descriptive Statistics: mean, mode, variance, standard deviation<br /> :*Statistical Inference. The Binomial and Normal Distribution<br /> :*Basic Tests: t-test, linear correlation<br /> :*Modeling and non-linear relations<br /> :*Bayes rule<br /> <br /> <br /> ==''Wednesday 10/14/15''==<br /> ===- Statistical Fundamentals II. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> =Week 4 - Stats and circuits=<br /> ==''Monday 10/19/15''==<br /> ===- Statistical Fundamentals III. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> ==''Wednesday 10/21/15''==<br /> ===Circuits continued: Active devices. [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ===Optics I. ''Speaker'': [mailto:zdeis@seas.ucla.edu Zachary Taylor]===<br /> [[Image:Reflection.jpg|right]]<br /> The overall goal of this lecture is to establish that:<br /> ''- Physical constants have tangible meanings''<br /> ''- Plane waves form a physically unrealizable but extremely good approximation to real systems''<br /> ''- Boundaries bend light''<br /> ''- Physical constants, plane wave mechanics, and boundaries can be used to describe the operation of a lens''<br /> ''- The PSF gives a good indication of the overall performance of an imaging system''<br /> ''- All of these concepts have analogues in other areas of engineering (ie circuits, mechanical vibrations, etc.)''''<br /> <br /> '''Outline:'''<br /> :* Constitutive parameters (ε, μ, η, n, etc.)<br /> :* Plane wave basics<br /> :* Plane waves at boundaries<br /> :* Lenses<br /> :* Advanced imaging properties of lenses<br /> :* Point spread function.<br /> <br /> ''Required Readings''<br /> Zach has very kindly agreed to post his [http://www.brainmapping.org/NITP/PNA/Readings/OpticsTaylor3-10-10.pdf Optics lecture notes].<br /> ''Suggested Further Reading''<br /> <br /> <br /> <br /> <br /> ==''Wednesday 11/19/14''== <br /> ===Optogenetics. ''Speaker'': [http://faculty.neuroscience.ucla.edu/institution/personnel?personnel_id=47031 Tom Otis]===<br /> <br /> <br /> =Week 8: =<br /> ==''Monday 12/1/14 ''==<br /> ===- Statistics for Imaging. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> [[image:BVTradeoff.jpg|right]]<br /> ''Required Readings''<br /> :*[[media: Mumford_stat_modeling.pdf | Statistical Modeling and Inference (pdf)]]<br /> :*[[media: CohenClassSlides12_2_13.pdf | Slides used in class 12/2/14 and 12/4/14]]<br /> <br /> :*The General Linear Model<br /> *Linear Algebra applied to Statistical Solutions<br /> :*Analysis of Variance<br /> <br /> ==''Wednesday 11/20/14 ...''==<br /> === - Statistics for Imaging II. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> *Fixed and Random Effects<br /> *Repeated measures<br /> :*Bonferroni and Other Corrections<br /> *Non-Parametric Methods<br /> *Autocorrelation<br /> *Unknown Distributions<br /> <br /> ''Required Readings''<br /> :*[[media: CohenClassSlides12_2_13.pdf‎ | same as 12/2/14]]<br /> <br /> ''Suggested Further Reading''<br /> <br /> :*[http://ccn.ucla.edu/media/PNI20121121.mp4 Lecture Video - To be Posted Shortly - Please Right Click and Save rather than Stream - Thank you Alan and Edward!]<br /> <br /> =Week 9: =<br /> ==''Monday 11/25/14''==<br /> === - TBD. [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> ==''Wednesday 11/27/14''==<br /> ===- The Organization of the Human Brain. ''Speaker'': [http://ccn.ucla.edu/bmcweb/bmc_bios/SusanBookheimer/ Susan Bookheimer]===<br /> <br /> We will discuss the general organization of the human brain, and the regional specialization of cortical areas. The emphasis will be on understanding principles of organization:<br /> *Phylogenetic Layering<br /> *Functional Specialization<br /> *Principles Divisions of the Brain<br /> *Brain Systems<br /> <br /> ''Required Readings''<br /> :*[http://da.biostr.washington.edu:80/cgi-bin/DA/PageMaster?atlas:NeuroSyllabus+ffpathIndex/Splash^Page^Syllabus+2 Neuroanatomy Programmed Learning]<br /> :*[[media:Cognitive_science_and_neuro_2012.pdf | Slides shown in Class UPDATED 2012/10/17]]<br /> ''Suggested Further Reading''<br /> <br /> :*[[media:PNIA_2012_PS1_Soln.pdf‎ | Solution to Problem Set 1]]<br /> ===- Human Electrophysiology ''Speakers'': [http://greenlab.npih.ucla.edu/ROSTER.html Agatha Lenartowicz], [http://dgsom.healthsciences.ucla.edu/institution/personnel?personnel_id=9140 John Stern]===<br /> <br /> ''Evoked Responses'' - Guest Lecturer: [http://greenlab.npih.ucla.edu/ROSTER.html Jonathan Wynn]<br /> *A look at real EEG data<br /> *Preprocessing:<br /> **filtering<br /> **artifact detection/removal<br /> *averaging<br /> *single events<br /> *interpretation<br /> <br /> ''Clinical EEG'' - Guest Lecturer: [http://dgsom.healthsciences.ucla.edu/institution/personnel?personnel_id=9140 John Stern]<br /> *Normal and Abnormal EEG<br /> *EEG as a marker for brain state<br /> **sleep staging<br /> **alpha and relaxation<br /> *Neurofeedback??? --&gt;</div>

Alenartowicz https://www.ccn.ucla.edu/wiki/index.php?title=Principles_of_Neuroimaging_A_-_2016&diff=3205 2016-10-26T22:43:46Z

<p>Alenartowicz: </p> <hr /> <div>Principles of Neuroimaging A, Fall, 2016 - Class Schedule and Syllabus<br /> <br /> <br /> ==='''Neuroimaging journal Club (''required for NITP certificate'')'''===<br /> Contact: Katherine Lawrence (katherine.E.Lawrence@ucla.edu) or Janelle Liu (janelle.j.liu@ucla.edu), Faculty Sponsor: Jamie Feusner (JFeusner@mednet.ucla.edu)<br /> <br /> ==This schedule ''will'' change!==<br /> <br /> :'''[[Principles_of_Neuroimaging_-_2016-2017 | Back to main course page for Principles of Neuroimaging]]'''<br /> <br /> :'''Coming up next!: [[Principles_of_Neuroimaging_B_-_2017 | M284B Principles of Neuroimaging B]]'''<br /> <br /> =Course Reading=<br /> ===Required Reading===<br /> :'''Signal Processing for Neuroscientists''' by ''Wim van Drongelen''<br /> ::This can be found as a PDF on scribd.com, for a small fee of $8.99<br /> <br /> ===Supplemental Reading===<br /> :'''Matlab for Neuroscientists'''<br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/88212458/Matlab-Matlab-for-Neuroscientists<br /> <br /> :'''Cartoon Guide to Statistics''' <br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/148072668/Cartoon-Guide-to-Statistics<br /> <br /> :'''NOTE:''' if you subscribe for a Scribd account for a day, you can download as many documents as you like for one fee.<br /> <br /> <br /> =Week 1: Orientation to Neuro-imaging, Neurons, Brains=<br /> <br /> ==''Monday 9/26/16'' ==<br /> ===- Images. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284AImages-intro.pdf | Lecture Slides Images]]<br /> <br /> ==''Wednesday 9/28/16''==<br /> ===- Neurons &amp; Signaling. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> In this first class we will review the basics of neurophysiology with an eye towards what signals of brain function might be visible to the neuroimager. We will discuss information coding, energetics, size and time scales.<br /> [[Image:Neurons.jpg|right]]<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:TheActiveBrain.pdf | The Active Brain]]<br /> :*[[media:NeuronFunctionM284-2016_SM.pdf | Neuron function slides shown in class]]<br /> :*[http://www.science.smith.edu/departments/neurosci/courses/bio330/squid.html Videos of giant squid experiments]<br /> ''Suggested Further Reading''<br /> :*[http://ccn.ucla.edu/wiki/images/5/5a/CAVEAT_LECTOR.pdf Caveat Lector - the misuse of neuroimaging]<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Protected/Kosslyn1999.pdf &quot;If Neuroimaging is the Answer, What is the Question?&quot; Kosslyn, 1999]<br /> :*[http://da.biostr.washington.edu:80/cgi-bin/DA/PageMaster?atlas:NeuroSyllabus+ffpathIndex/Splash^Page^Syllabus+2 Neuroanatomy Programmed Learning]<br /> :*[http://www.amazon.com/Fundamental-Neuroscience-Second-Larry-Squire/dp/0126603030 Squire, Fundamentals of Neuroscience]<br /> :*[http://www.amazon.com/Principles-Neural-Science-Eric-Kandel/dp/0838577016 Kandel, et al., &quot;Principles of Neural Science&quot;]<br /> :This paper, by Malhi, is a nice orientation in methods of neuroimaging. *[http://www.ccn.ucla.edu/wiki/images/f/f2/Malhi2007.pdf Making sense of neuroimaging in psychiatry]<br /> :*[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1359308/pdf/jphysiol01232-0142.pdf Replacement of the axoplasm of giant nerve fibres with artificial solutions]<br /> <br /> =Week 2: Linear Systems, Convolution, Fourier Transforms=<br /> ==''Wednesday 10/3/16''==<br /> <br /> ===- Linear Systems I. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ==''Wednesday 10/5/16''==<br /> ===- Linear Systems II. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> Why the emphasis on Linear Systems? Because they are actually ''easy'' (as compared to non-linear systems, which are not.) As we go through this course, we will see many ways in which linear systems theory is applied to:<br /> :Modeling of Neural Systems<br /> :Extraction of Signal from Noise<br /> :Design of Circuits<br /> :Image Enhancement<br /> :Understanding of Image artifacts, and others.<br /> <br /> Linear systems analysis is one of the great technologies of the 20th and 21st century. It is now the basis for virtually all electronics design, and its extension into the discrete (digital) domain is the basis for most of modern signal processing. <br /> <br /> In our specific case, we will use these few basic principles of linear systems to understand both the instruments we use and the neuroimaging signals we collect. When you have mastered this material, you should be in a much better position to model the systems that you study in order to develop an approach to studying them.<br /> <br /> Here is [http://www.brainmapping.org/NITP/PNA/Readings/ImaginaryNumbers.pdf A primer I wrote on imaginary numbers] that might be a helpful review.<br /> <br /> There is a nice [http://en.wikibooks.org/wiki/Calculus Wikibook on Calculus].<br /> <br /> ''Required Readings''<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapter 1<br /> :*[[media:MathematicalTools2016.pdf | Mathematical Tools]]<br /> <br /> ''Suggested Further Reading''<br /> :'''Introduction to matlab'''<br /> <br /> ''Slides shown in class''<br /> :[[media:LinearityM284A-2016.pdf | Linearity and the Fourier Transform]]<br /> <br /> Please see [http://www.brainmapping.org/NITP/PNA/html/Linearity.html MATLAB linearity demo]<br /> <br /> If you are the type who sees beauty in mathematics, the Euler identity may be one of the most beautiful pieces of math in the world.<br /> <br /> =Week 3: Math &amp; Circuits I=<br /> <br /> ==''Monday 10/10/16''==<br /> === - Circuits I. Speaker: Cameron Rodriguez ===<br /> :*[[media:Circuits_2016.pdf | Circuit slides shown in class]]<br /> <br /> ==''Wednesday 10/12/16''==<br /> === - Circuits II. ''Speaker'': Cameron Rodriguez ===<br /> <br /> Why circuits?<br /> :(Virtually) Every device you use in your research is electronic. You access your primary data only indirectly<br /> :The device you ''really'' want in your lab doesn't exist. You very well may have to make it.<br /> :There are electronic analogs to most of the linear systems that you have so far studied (and ''vice versa'' - the tools you now understand can be used to analyze and predict circuit behavior).<br /> <br /> :If you have not had any of this background, you might want to have a look at this handout, [[Media:Electricity.pdf|Electrical Circuits]], in advance. There are near infinite numbers of resources on the web that cover similar material (near enough to infinite that by the time you read all of them, there would be a whole new set.) I have recently come across a link to [http://www.allaboutcircuits.com/ Online Books: All About Circuits] ''IF'' you want practical hands-on knowledge about this material, my all-time favorite text is [http://www.google.com/products/catalog?hl=en&amp;client=safari&amp;rls=en-us&amp;ei=uVSPSfaxE5nMsAPf-tmSCQ&amp;resnum=1&amp;q=art+of+electronics&amp;um=1&amp;ie=UTF-8&amp;cid=8820839049329255765#ps-sellers &quot;Horowitz and Hill: ''The Art of Electronics.''&quot;] The latest edition, however, is dated 1989 and a new third edition is promised. I have therefore stopped short of recommending a purchase unless your need to make circuits is immediate. In this book, you will find an excellent education on the fundamental principles of electrical circuits and an incredible compendium of practical data, such as how to assemble circuit boards, how to make measurements, etc...) <br /> <br /> Readings:<br /> :*[http://www.ti.com/lit/an/sloa093/sloa093.pdf Filter Design in 30 Seconds]<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapter 2 and 10<br /> :*[https://www.circuitlab.com/ Circuit Lab ] A Free Circuit Web Base Simulator<br /> **You may or may not find this comprehensible without chapters 5 through 9.<br /> <br /> We will discuss:<br /> :*Passive Circuit Elements: Resistors, Capacitors, Inductors<br /> :*Gain<br /> :*Transformers<br /> :*Rectifiers<br /> :*Active Elements<br /> ::- ''Amplifiers''<br /> ::- ''Transistors''<br /> ::- ''Op Amps''<br /> :*Solutions with Matrices<br /> ''Suggested Further Reading''<br /> :*[http://video.google.com/videoplay?docid=5645396659673218353&amp;q=Physics+for+Future+Presidents+Electricity&amp;total=5&amp;start=0&amp;num=10&amp;so=0&amp;type=search&amp;plindex=0#0h20m30s Video intro lecture on charge, current and voltage].<br /> :*[[media:Electricity_Basics.pdf‎ | Well organized text on electrical concepts by Tony R. Kuphaldt]]<br /> :*[http://en.wikibooks.org/wiki/Circuit_Theory Circuit Theory - Wikibook]<br /> <br /> '''Suggested, Optional Readings from [http://www.dspguide.com DSPguide.com]:'''<br /> :*[http://www.dspguide.com/CH5.PDF Linear Systems]<br /> :*[http://www.dspguide.com/CH6.PDF Convolution]<br /> :*[http://www.dspguide.com/CH8.PDF Discrete Fourier Transform (DFT)]<br /> :''Note: These chapters are light on math and try to focus on a conceptual understanding''<br /> <br /> Time and Frequency / Spectral Filters<br /> <br /> ----<br /> Practice using the Fourier transform:<br /> :[http://www.brainmapping.org/NITP/PNA/ConvFThtml/ConvolutionWorksheet.pdf Fourier transform and Convolution Worksheet]. &lt;!-- [http://www.brainmapping.org/NITP/PNA/ConvFThtml/ConvFT.html (''Solutions'').] --&gt;<br /> :[http://www.brainmapping.org/NITP/PNA/ConvFThtml/Something.wav Sound file for worksheet above.]<br /> <br /> Other<br /> :*[http://www.youtube.com/watch?feature=player_embedded&amp;v=7uHfjpU3OH0#! Getting Started with Arduino]<br /> <br /> =Week 4 Signal Processing=<br /> <br /> ==''Monday 10/17/16''==<br /> === - Signal Processing. ''Speaker'': Cameron Rodriguez ===<br /> <br /> :*[[media:Signals1-PreProcessing1_2016.pdf | Signal Pre Processing Slide Set 1 shown in class]]<br /> <br /> <br /> ==''Wednesday 10/19/16''==<br /> === - Noise ''Speaker'': Cameron Rodriguez ===<br /> <br /> :*[[media:OneD_signal_Filter_Ex.pdf | Matlab Signal Tutorial printout]]<br /> <br /> It is what you ''don't'' want - usually - but things change in quantized systems<br /> :Additive noise<br /> :White Noise<br /> :Boltzmann noise<br /> :Colored Noise<br /> :Gaussian Noise<br /> :Coherent noise<br /> :Sampling Errors<br /> :Aliasing<br /> :Quantization noise<br /> :Spectral filtering<br /> <br /> Noise comes in all shapes and colors. It is present in every measurement we make, from an EEG voltage to an estimate of the effects of dopamine on forebrain signal. Our best weapons are an understanding of the statistical properties of noise, the sources of noise and the ways to control it. Noise in the discrete digital domain is special, as it is both ''created'' by digitization and amplified by sampling.<br /> <br /> Readings:<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapters 2 through 4<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Noise.pdf Noise Slides]<br /> <br /> =Week 5 - Information &amp; Statistical Theory =<br /> <br /> ==''Monday 10/24/16 - in C8-177''==<br /> === Noise Cont' &amp; Information Theory Speakers: Cameron Rodriguez &amp; John Villasenor===<br /> <br /> ==''Wednesday 10/26/16''==<br /> ===Statistical Theory ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> We will consider the general problems of statistical inference, with a concentration on developing an intuitive understanding of statistical concepts.<br /> [[Image:MeasureForMeasure.jpg|right]]<br /> <br /> Suggested reading<br /> :*[http://www.statsoft.com/textbook/stbasic.html Statsoft online text (''free'')]<br /> :*[http://www.amazon.com/Cartoon-Guide-Statistics-Larry-Gonick/dp/0062731025 The Cartoon Guide to Statistics - Gonick $17.95 new]<br /> :The latter teaches stats at what I feel to be the right level - developing intuitions about the kinds of questions that can be answered using stats and about the statistical tests and measures<br /> <br /> :'''Problem Set 3 - Statistics in matlab'''<br /> ::[[media: Problem_Set_1.doc|Problem set using stats and MATLAB]]<br /> ::[[media: Problem_Set_1B.doc|More practice with stats and MATLAB]]<br /> <br /> <br /> =MIDTERM=<br /> <br /> <br /> =Week 6 - Neurophysiology=<br /> ==''Monday 10/31/16''==<br /> === Electrophysiology I ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> We will examine our first imaging modality, EEG (and MEG).<br /> <br /> ==''Wednesday 11/2/16''==<br /> === Electrophysiology II ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> Continuing with neurophysiology.<br /> <br /> =Week 7 - Neurophysiology =<br /> ==''Monday 11/7/16''==<br /> ===Midterm Review &amp; EEG Signal Processing ''Speaker'': Agatha Lenartowicz, Cameron Rodriguez===<br /> <br /> ==''Wednesday 11/9/16''==<br /> ===Intracranial Recordings ''Speaker'': [http://neurosurgery.ucla.edu/neuromodulation-neuroimaging-lab Nanthia Sultana]===<br /> <br /> =Week 8 - Practical Neurophysiology=<br /> ==''Monday 11/14/16''==<br /> ===Building an EEG System - Circuit 1 ''Speaker'': Cameron Rodriguez===<br /> <br /> ==''Wednesday 11/16/16''==<br /> ===Building an EEG System - Circuit 2 ''Speaker'': Cameron Rodriguez===<br /> <br /> :*[https://learn.sparkfun.com/tutorials/how-to-solder Through holesoldering].<br /> :*[https://learn.sparkfun.com/tutorials/pcb-basics Printed Circuit Board Basics].<br /> :*[https://learn.sparkfun.com/tutorials/how-to-install-and-setup-eagle Installing and Setting Up Eagle].<br /> :*[https://learn.sparkfun.com/tutorials/using-eagle-schematic Using Eagle Schematic].<br /> :*[https://learn.sparkfun.com/tutorials/using-eagle-board-layoutc Using Eagle Board Layout].<br /> <br /> :*[https://www.sparkfun.com/videos#pete/NJKZZArjdg8 PCB Layout 1 - According to Pete];<br /> :*[https://www.sparkfun.com/videos#pete/JANZsjRiM3w PCB Layout 2 - According to Pete];<br /> <br /> <br /> =Week 9 - Magnetic Resonance Fundamentals =<br /> ==''Monday 11/21/16 - in C8-177''==<br /> ===MR Signal Origin ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ==''Wednesday 11/23/16''==<br /> ===Thankgiving Wed - No Class ===<br /> <br /> Magnetic Resonance Imaging (MRI) is probably the most influential and most flexible current means of imaging the human brain. It features a vast number of separable contrast mechanisms, and a near ideal combination of non-invasiveness, safety, resolution and metric accuracy. However, it is extraordinarily expensive and has limited temporal resolution, especially for functional studies<br /> <br /> OUTLINE<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> :*[http://www.cis.rit.edu/htbooks/mri/ These notes] by Joseph Hornak are highly professional and complete coverage of MRI.<br /> :*[http://www.imaios.com/en/e-Courses/e-MRI eMRI] is another excellent online MRI learning resource<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Hahn1950.pdf Erwin Hahn - Spin Echoes: ''Essential reading for the MRI community'']<br /> [[Image:HahnFig1.png]]<br /> ''above'': Figure 1 from Hahn, 1950<br /> <br /> ''Suggested Further Reading''<br /> [[image:PSatSeq.jpg|right]]<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> <br /> =Week 10 - Magnetic Resonance Fundamentals =<br /> ==''Monday 11/28/16''==<br /> ===MR Physics ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ==''Wednesady 11/30/16''==<br /> ===TBD/Q&amp;A===<br /> <br /> <br /> =Week 11 - Finals Week=<br /> ==''Monday 11/5/16''==<br /> ===Final Exam===<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!--<br /> SPARE CONTENT / PREVIOUS YEARS<br /> <br /> =Week 3: Stats begins!=<br /> <br /> ==''Monday 10/12/15''==<br /> <br /> ===- Statistical Fundamentals. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> :*[[media: CohenClassIntroStats11_25_13.pdf‎ | Slides used in class (set 1)]]<br /> <br /> ''Review of'':<br /> :*Descriptive Statistics: mean, mode, variance, standard deviation<br /> :*Statistical Inference. The Binomial and Normal Distribution<br /> :*Basic Tests: t-test, linear correlation<br /> :*Modeling and non-linear relations<br /> :*Bayes rule<br /> <br /> <br /> ==''Wednesday 10/14/15''==<br /> ===- Statistical Fundamentals II. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> =Week 4 - Stats and circuits=<br /> ==''Monday 10/19/15''==<br /> ===- Statistical Fundamentals III. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> ==''Wednesday 10/21/15''==<br /> ===Circuits continued: Active devices. [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ===Optics I. ''Speaker'': [mailto:zdeis@seas.ucla.edu Zachary Taylor]===<br /> [[Image:Reflection.jpg|right]]<br /> The overall goal of this lecture is to establish that:<br /> ''- Physical constants have tangible meanings''<br /> ''- Plane waves form a physically unrealizable but extremely good approximation to real systems''<br /> ''- Boundaries bend light''<br /> ''- Physical constants, plane wave mechanics, and boundaries can be used to describe the operation of a lens''<br /> ''- The PSF gives a good indication of the overall performance of an imaging system''<br /> ''- All of these concepts have analogues in other areas of engineering (ie circuits, mechanical vibrations, etc.)''''<br /> <br /> '''Outline:'''<br /> :* Constitutive parameters (ε, μ, η, n, etc.)<br /> :* Plane wave basics<br /> :* Plane waves at boundaries<br /> :* Lenses<br /> :* Advanced imaging properties of lenses<br /> :* Point spread function.<br /> <br /> ''Required Readings''<br /> Zach has very kindly agreed to post his [http://www.brainmapping.org/NITP/PNA/Readings/OpticsTaylor3-10-10.pdf Optics lecture notes].<br /> ''Suggested Further Reading''<br /> <br /> <br /> <br /> <br /> ==''Wednesday 11/19/14''== <br /> ===Optogenetics. ''Speaker'': [http://faculty.neuroscience.ucla.edu/institution/personnel?personnel_id=47031 Tom Otis]===<br /> <br /> <br /> =Week 8: =<br /> ==''Monday 12/1/14 ''==<br /> ===- Statistics for Imaging. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> [[image:BVTradeoff.jpg|right]]<br /> ''Required Readings''<br /> :*[[media: Mumford_stat_modeling.pdf | Statistical Modeling and Inference (pdf)]]<br /> :*[[media: CohenClassSlides12_2_13.pdf | Slides used in class 12/2/14 and 12/4/14]]<br /> <br /> :*The General Linear Model<br /> *Linear Algebra applied to Statistical Solutions<br /> :*Analysis of Variance<br /> <br /> ==''Wednesday 11/20/14 ...''==<br /> === - Statistics for Imaging II. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> *Fixed and Random Effects<br /> *Repeated measures<br /> :*Bonferroni and Other Corrections<br /> *Non-Parametric Methods<br /> *Autocorrelation<br /> *Unknown Distributions<br /> <br /> ''Required Readings''<br /> :*[[media: CohenClassSlides12_2_13.pdf‎ | same as 12/2/14]]<br /> <br /> ''Suggested Further Reading''<br /> <br /> :*[http://ccn.ucla.edu/media/PNI20121121.mp4 Lecture Video - To be Posted Shortly - Please Right Click and Save rather than Stream - Thank you Alan and Edward!]<br /> <br /> =Week 9: =<br /> ==''Monday 11/25/14''==<br /> === - TBD. [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> ==''Wednesday 11/27/14''==<br /> ===- The Organization of the Human Brain. ''Speaker'': [http://ccn.ucla.edu/bmcweb/bmc_bios/SusanBookheimer/ Susan Bookheimer]===<br /> <br /> We will discuss the general organization of the human brain, and the regional specialization of cortical areas. The emphasis will be on understanding principles of organization:<br /> *Phylogenetic Layering<br /> *Functional Specialization<br /> *Principles Divisions of the Brain<br /> *Brain Systems<br /> <br /> ''Required Readings''<br /> :*[http://da.biostr.washington.edu:80/cgi-bin/DA/PageMaster?atlas:NeuroSyllabus+ffpathIndex/Splash^Page^Syllabus+2 Neuroanatomy Programmed Learning]<br /> :*[[media:Cognitive_science_and_neuro_2012.pdf | Slides shown in Class UPDATED 2012/10/17]]<br /> ''Suggested Further Reading''<br /> <br /> :*[[media:PNIA_2012_PS1_Soln.pdf‎ | Solution to Problem Set 1]]<br /> ===- Human Electrophysiology ''Speakers'': [http://greenlab.npih.ucla.edu/ROSTER.html Agatha Lenartowicz], [http://dgsom.healthsciences.ucla.edu/institution/personnel?personnel_id=9140 John Stern]===<br /> <br /> ''Evoked Responses'' - Guest Lecturer: [http://greenlab.npih.ucla.edu/ROSTER.html Jonathan Wynn]<br /> *A look at real EEG data<br /> *Preprocessing:<br /> **filtering<br /> **artifact detection/removal<br /> *averaging<br /> *single events<br /> *interpretation<br /> <br /> ''Clinical EEG'' - Guest Lecturer: [http://dgsom.healthsciences.ucla.edu/institution/personnel?personnel_id=9140 John Stern]<br /> *Normal and Abnormal EEG<br /> *EEG as a marker for brain state<br /> **sleep staging<br /> **alpha and relaxation<br /> *Neurofeedback??? --&gt;</div>

Alenartowicz https://www.ccn.ucla.edu/wiki/index.php?title=Principles_of_Neuroimaging_A_-_2016&diff=3204 2016-10-26T22:19:07Z

<p>Alenartowicz: </p> <hr /> <div>Principles of Neuroimaging A, Fall, 2016 - Class Schedule and Syllabus<br /> <br /> <br /> ==='''Neuroimaging journal Club (''required for NITP certificate'')'''===<br /> Contact: Katherine Lawrence (katherine.E.Lawrence@ucla.edu) or Janelle Liu (janelle.j.liu@ucla.edu), Faculty Sponsor: Jamie Feusner (JFeusner@mednet.ucla.edu)<br /> <br /> ==This schedule ''will'' change!==<br /> <br /> :'''[[Principles_of_Neuroimaging_-_2016-2017 | Back to main course page for Principles of Neuroimaging]]'''<br /> <br /> :'''Coming up next!: [[Principles_of_Neuroimaging_B_-_2017 | M284B Principles of Neuroimaging B]]'''<br /> <br /> =Course Reading=<br /> ===Required Reading===<br /> :'''Signal Processing for Neuroscientists''' by ''Wim van Drongelen''<br /> ::This can be found as a PDF on scribd.com, for a small fee of $8.99<br /> <br /> ===Supplemental Reading===<br /> :'''Matlab for Neuroscientists'''<br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/88212458/Matlab-Matlab-for-Neuroscientists<br /> <br /> :'''Cartoon Guide to Statistics''' <br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/148072668/Cartoon-Guide-to-Statistics<br /> <br /> :'''NOTE:''' if you subscribe for a Scribd account for a day, you can download as many documents as you like for one fee.<br /> <br /> <br /> =Week 1: Orientation to Neuro-imaging, Neurons, Brains=<br /> <br /> ==''Monday 9/26/16'' ==<br /> ===- Images. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284AImages-intro.pdf | Lecture Slides Images]]<br /> <br /> ==''Wednesday 9/28/16''==<br /> ===- Neurons &amp; Signaling. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> In this first class we will review the basics of neurophysiology with an eye towards what signals of brain function might be visible to the neuroimager. We will discuss information coding, energetics, size and time scales.<br /> [[Image:Neurons.jpg|right]]<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:TheActiveBrain.pdf | The Active Brain]]<br /> :*[[media:NeuronFunctionM284-2016_SM.pdf | Neuron function slides shown in class]]<br /> :*[http://www.science.smith.edu/departments/neurosci/courses/bio330/squid.html Videos of giant squid experiments]<br /> ''Suggested Further Reading''<br /> :*[http://ccn.ucla.edu/wiki/images/5/5a/CAVEAT_LECTOR.pdf Caveat Lector - the misuse of neuroimaging]<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Protected/Kosslyn1999.pdf &quot;If Neuroimaging is the Answer, What is the Question?&quot; Kosslyn, 1999]<br /> :*[http://da.biostr.washington.edu:80/cgi-bin/DA/PageMaster?atlas:NeuroSyllabus+ffpathIndex/Splash^Page^Syllabus+2 Neuroanatomy Programmed Learning]<br /> :*[http://www.amazon.com/Fundamental-Neuroscience-Second-Larry-Squire/dp/0126603030 Squire, Fundamentals of Neuroscience]<br /> :*[http://www.amazon.com/Principles-Neural-Science-Eric-Kandel/dp/0838577016 Kandel, et al., &quot;Principles of Neural Science&quot;]<br /> :This paper, by Malhi, is a nice orientation in methods of neuroimaging. *[http://www.ccn.ucla.edu/wiki/images/f/f2/Malhi2007.pdf Making sense of neuroimaging in psychiatry]<br /> :*[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1359308/pdf/jphysiol01232-0142.pdf Replacement of the axoplasm of giant nerve fibres with artificial solutions]<br /> <br /> =Week 2: Linear Systems, Convolution, Fourier Transforms=<br /> ==''Wednesday 10/3/16''==<br /> <br /> ===- Linear Systems I. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ==''Wednesday 10/5/16''==<br /> ===- Linear Systems II. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> Why the emphasis on Linear Systems? Because they are actually ''easy'' (as compared to non-linear systems, which are not.) As we go through this course, we will see many ways in which linear systems theory is applied to:<br /> :Modeling of Neural Systems<br /> :Extraction of Signal from Noise<br /> :Design of Circuits<br /> :Image Enhancement<br /> :Understanding of Image artifacts, and others.<br /> <br /> Linear systems analysis is one of the great technologies of the 20th and 21st century. It is now the basis for virtually all electronics design, and its extension into the discrete (digital) domain is the basis for most of modern signal processing. <br /> <br /> In our specific case, we will use these few basic principles of linear systems to understand both the instruments we use and the neuroimaging signals we collect. When you have mastered this material, you should be in a much better position to model the systems that you study in order to develop an approach to studying them.<br /> <br /> Here is [http://www.brainmapping.org/NITP/PNA/Readings/ImaginaryNumbers.pdf A primer I wrote on imaginary numbers] that might be a helpful review.<br /> <br /> There is a nice [http://en.wikibooks.org/wiki/Calculus Wikibook on Calculus].<br /> <br /> ''Required Readings''<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapter 1<br /> :*[[media:MathematicalTools2016.pdf | Mathematical Tools]]<br /> <br /> ''Suggested Further Reading''<br /> :'''Introduction to matlab'''<br /> <br /> ''Slides shown in class''<br /> :[[media:LinearityM284A-2016.pdf | Linearity and the Fourier Transform]]<br /> <br /> Please see [http://www.brainmapping.org/NITP/PNA/html/Linearity.html MATLAB linearity demo]<br /> <br /> If you are the type who sees beauty in mathematics, the Euler identity may be one of the most beautiful pieces of math in the world.<br /> <br /> =Week 3: Math &amp; Circuits I=<br /> <br /> ==''Monday 10/10/16''==<br /> === - Circuits I. Speaker: Cameron Rodriguez ===<br /> :*[[media:Circuits_2016.pdf | Circuit slides shown in class]]<br /> <br /> ==''Wednesday 10/12/16''==<br /> === - Circuits II. ''Speaker'': Cameron Rodriguez ===<br /> <br /> Why circuits?<br /> :(Virtually) Every device you use in your research is electronic. You access your primary data only indirectly<br /> :The device you ''really'' want in your lab doesn't exist. You very well may have to make it.<br /> :There are electronic analogs to most of the linear systems that you have so far studied (and ''vice versa'' - the tools you now understand can be used to analyze and predict circuit behavior).<br /> <br /> :If you have not had any of this background, you might want to have a look at this handout, [[Media:Electricity.pdf|Electrical Circuits]], in advance. There are near infinite numbers of resources on the web that cover similar material (near enough to infinite that by the time you read all of them, there would be a whole new set.) I have recently come across a link to [http://www.allaboutcircuits.com/ Online Books: All About Circuits] ''IF'' you want practical hands-on knowledge about this material, my all-time favorite text is [http://www.google.com/products/catalog?hl=en&amp;client=safari&amp;rls=en-us&amp;ei=uVSPSfaxE5nMsAPf-tmSCQ&amp;resnum=1&amp;q=art+of+electronics&amp;um=1&amp;ie=UTF-8&amp;cid=8820839049329255765#ps-sellers &quot;Horowitz and Hill: ''The Art of Electronics.''&quot;] The latest edition, however, is dated 1989 and a new third edition is promised. I have therefore stopped short of recommending a purchase unless your need to make circuits is immediate. In this book, you will find an excellent education on the fundamental principles of electrical circuits and an incredible compendium of practical data, such as how to assemble circuit boards, how to make measurements, etc...) <br /> <br /> Readings:<br /> :*[http://www.ti.com/lit/an/sloa093/sloa093.pdf Filter Design in 30 Seconds]<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapter 2 and 10<br /> :*[https://www.circuitlab.com/ Circuit Lab ] A Free Circuit Web Base Simulator<br /> **You may or may not find this comprehensible without chapters 5 through 9.<br /> <br /> We will discuss:<br /> :*Passive Circuit Elements: Resistors, Capacitors, Inductors<br /> :*Gain<br /> :*Transformers<br /> :*Rectifiers<br /> :*Active Elements<br /> ::- ''Amplifiers''<br /> ::- ''Transistors''<br /> ::- ''Op Amps''<br /> :*Solutions with Matrices<br /> ''Suggested Further Reading''<br /> :*[http://video.google.com/videoplay?docid=5645396659673218353&amp;q=Physics+for+Future+Presidents+Electricity&amp;total=5&amp;start=0&amp;num=10&amp;so=0&amp;type=search&amp;plindex=0#0h20m30s Video intro lecture on charge, current and voltage].<br /> :*[[media:Electricity_Basics.pdf‎ | Well organized text on electrical concepts by Tony R. Kuphaldt]]<br /> :*[http://en.wikibooks.org/wiki/Circuit_Theory Circuit Theory - Wikibook]<br /> <br /> '''Suggested, Optional Readings from [http://www.dspguide.com DSPguide.com]:'''<br /> :*[http://www.dspguide.com/CH5.PDF Linear Systems]<br /> :*[http://www.dspguide.com/CH6.PDF Convolution]<br /> :*[http://www.dspguide.com/CH8.PDF Discrete Fourier Transform (DFT)]<br /> :''Note: These chapters are light on math and try to focus on a conceptual understanding''<br /> <br /> Time and Frequency / Spectral Filters<br /> <br /> ----<br /> Practice using the Fourier transform:<br /> :[http://www.brainmapping.org/NITP/PNA/ConvFThtml/ConvolutionWorksheet.pdf Fourier transform and Convolution Worksheet]. &lt;!-- [http://www.brainmapping.org/NITP/PNA/ConvFThtml/ConvFT.html (''Solutions'').] --&gt;<br /> :[http://www.brainmapping.org/NITP/PNA/ConvFThtml/Something.wav Sound file for worksheet above.]<br /> <br /> Other<br /> :*[http://www.youtube.com/watch?feature=player_embedded&amp;v=7uHfjpU3OH0#! Getting Started with Arduino]<br /> <br /> =Week 4 Signal Processing=<br /> <br /> ==''Monday 10/17/16''==<br /> === - Signal Processing. ''Speaker'': Cameron Rodriguez ===<br /> <br /> :*[[media:Signals1-PreProcessing1_2016.pdf | Signal Pre Processing Slide Set 1 shown in class]]<br /> <br /> <br /> ==''Wednesday 10/19/16''==<br /> === - Noise ''Speaker'': Cameron Rodriguez ===<br /> <br /> :*[[media:OneD_signal_Filter_Ex.pdf | Matlab Signal Tutorial printout]]<br /> <br /> It is what you ''don't'' want - usually - but things change in quantized systems<br /> :Additive noise<br /> :White Noise<br /> :Boltzmann noise<br /> :Colored Noise<br /> :Gaussian Noise<br /> :Coherent noise<br /> :Sampling Errors<br /> :Aliasing<br /> :Quantization noise<br /> :Spectral filtering<br /> <br /> Noise comes in all shapes and colors. It is present in every measurement we make, from an EEG voltage to an estimate of the effects of dopamine on forebrain signal. Our best weapons are an understanding of the statistical properties of noise, the sources of noise and the ways to control it. Noise in the discrete digital domain is special, as it is both ''created'' by digitization and amplified by sampling.<br /> <br /> Readings:<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapters 2 through 4<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Noise.pdf Noise Slides]<br /> <br /> =Week 5 - Information &amp; Statistical Theory =<br /> <br /> ==''Monday 10/24/16 - in C8-177''==<br /> === Noise Cont' &amp; Information Theory Speakers: Cameron Rodriguez &amp; John Villasenor===<br /> <br /> ==''Wednesday 10/26/16''==<br /> ===Statistical Theory ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> We will consider the general problems of statistical inference, with a concentration on developing an intuitive understanding of statistical concepts.<br /> [[Image:MeasureForMeasure.jpg|right]]<br /> <br /> Suggested reading<br /> :*[http://www.statsoft.com/textbook/stbasic.html Statsoft online text (''free'')]<br /> :*[http://www.amazon.com/Cartoon-Guide-Statistics-Larry-Gonick/dp/0062731025 The Cartoon Guide to Statistics - Gonick $17.95 new]<br /> :The latter teaches stats at what I feel to be the right level - developing intuitions about the kinds of questions that can be answered using stats and about the statistical tests and measures<br /> <br /> :'''Problem Set 3 - Statistics in matlab'''<br /> ::[[media: Problem_Set_1.doc|Problem set using stats and MATLAB]]<br /> ::[[media: Problem_Set_1B.doc|More practice with stats and MATLAB]]<br /> <br /> <br /> =MIDTERM=<br /> <br /> <br /> =Week 6 - Neurophysiology=<br /> ==''Monday 10/31/16''==<br /> === Electrophysiology I ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> We will examine our first imaging modality, EEG (and MEG).<br /> <br /> ==''Wednesday 11/2/16''==<br /> === Electrophysiology II ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> Continuing with neurophysiology.<br /> <br /> =Week 7 - Neurophysiology =<br /> ==''Monday 11/7/16''==<br /> ===Midterm Review &amp; EEG Signal Processing ''Speaker'': Agatha Lenartowicz, Cameron Rodriguez===<br /> <br /> ==''Wednesday 11/9/16''==<br /> ===Intracranial Recordings ''Speaker'': [http://neurosurgery.ucla.edu/neuromodulation-neuroimaging-lab Nanthia Sultana] ===<br /> <br /> <br /> =Week 8 - Practical Neurophysiology=<br /> ==''Monday 11/14/16''==<br /> ===Building an EEG System - Circuit 1 ''Speaker'': Cameron Rodriguez===<br /> <br /> ==''Wednesday 11/16/16''==<br /> ===Building an EEG System - Circuit 2 ''Speaker'': Cameron Rodriguez===<br /> <br /> :*[https://learn.sparkfun.com/tutorials/how-to-solder Through holesoldering].<br /> :*[https://learn.sparkfun.com/tutorials/pcb-basics Printed Circuit Board Basics].<br /> :*[https://learn.sparkfun.com/tutorials/how-to-install-and-setup-eagle Installing and Setting Up Eagle].<br /> :*[https://learn.sparkfun.com/tutorials/using-eagle-schematic Using Eagle Schematic].<br /> :*[https://learn.sparkfun.com/tutorials/using-eagle-board-layoutc Using Eagle Board Layout].<br /> <br /> :*[https://www.sparkfun.com/videos#pete/NJKZZArjdg8 PCB Layout 1 - According to Pete];<br /> :*[https://www.sparkfun.com/videos#pete/JANZsjRiM3w PCB Layout 2 - According to Pete];<br /> <br /> <br /> =Week 9 - Magnetic Resonance Fundamentals =<br /> ==''Monday 11/21/16 - in C8-177''==<br /> ===MR Signal Origin ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ==''Wednesday 11/23/16''==<br /> ===Thankgiving Wed - No Class ===<br /> <br /> Magnetic Resonance Imaging (MRI) is probably the most influential and most flexible current means of imaging the human brain. It features a vast number of separable contrast mechanisms, and a near ideal combination of non-invasiveness, safety, resolution and metric accuracy. However, it is extraordinarily expensive and has limited temporal resolution, especially for functional studies<br /> <br /> OUTLINE<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> :*[http://www.cis.rit.edu/htbooks/mri/ These notes] by Joseph Hornak are highly professional and complete coverage of MRI.<br /> :*[http://www.imaios.com/en/e-Courses/e-MRI eMRI] is another excellent online MRI learning resource<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Hahn1950.pdf Erwin Hahn - Spin Echoes: ''Essential reading for the MRI community'']<br /> [[Image:HahnFig1.png]]<br /> ''above'': Figure 1 from Hahn, 1950<br /> <br /> ''Suggested Further Reading''<br /> [[image:PSatSeq.jpg|right]]<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> <br /> =Week 10 - Magnetic Resonance Fundamentals =<br /> ==''Monday 11/28/16''==<br /> ===MR Physics ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ==''Wednesady 11/30/16''==<br /> ===TBD/Q&amp;A===<br /> <br /> <br /> =Week 11 - Finals Week=<br /> ==''Monday 11/5/16''==<br /> ===Final Exam===<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!--<br /> SPARE CONTENT / PREVIOUS YEARS<br /> <br /> =Week 3: Stats begins!=<br /> <br /> ==''Monday 10/12/15''==<br /> <br /> ===- Statistical Fundamentals. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> :*[[media: CohenClassIntroStats11_25_13.pdf‎ | Slides used in class (set 1)]]<br /> <br /> ''Review of'':<br /> :*Descriptive Statistics: mean, mode, variance, standard deviation<br /> :*Statistical Inference. The Binomial and Normal Distribution<br /> :*Basic Tests: t-test, linear correlation<br /> :*Modeling and non-linear relations<br /> :*Bayes rule<br /> <br /> <br /> ==''Wednesday 10/14/15''==<br /> ===- Statistical Fundamentals II. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> =Week 4 - Stats and circuits=<br /> ==''Monday 10/19/15''==<br /> ===- Statistical Fundamentals III. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> ==''Wednesday 10/21/15''==<br /> ===Circuits continued: Active devices. [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ===Optics I. ''Speaker'': [mailto:zdeis@seas.ucla.edu Zachary Taylor]===<br /> [[Image:Reflection.jpg|right]]<br /> The overall goal of this lecture is to establish that:<br /> ''- Physical constants have tangible meanings''<br /> ''- Plane waves form a physically unrealizable but extremely good approximation to real systems''<br /> ''- Boundaries bend light''<br /> ''- Physical constants, plane wave mechanics, and boundaries can be used to describe the operation of a lens''<br /> ''- The PSF gives a good indication of the overall performance of an imaging system''<br /> ''- All of these concepts have analogues in other areas of engineering (ie circuits, mechanical vibrations, etc.)''''<br /> <br /> '''Outline:'''<br /> :* Constitutive parameters (ε, μ, η, n, etc.)<br /> :* Plane wave basics<br /> :* Plane waves at boundaries<br /> :* Lenses<br /> :* Advanced imaging properties of lenses<br /> :* Point spread function.<br /> <br /> ''Required Readings''<br /> Zach has very kindly agreed to post his [http://www.brainmapping.org/NITP/PNA/Readings/OpticsTaylor3-10-10.pdf Optics lecture notes].<br /> ''Suggested Further Reading''<br /> <br /> <br /> <br /> <br /> ==''Wednesday 11/19/14''== <br /> ===Optogenetics. ''Speaker'': [http://faculty.neuroscience.ucla.edu/institution/personnel?personnel_id=47031 Tom Otis]===<br /> <br /> <br /> =Week 8: =<br /> ==''Monday 12/1/14 ''==<br /> ===- Statistics for Imaging. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> [[image:BVTradeoff.jpg|right]]<br /> ''Required Readings''<br /> :*[[media: Mumford_stat_modeling.pdf | Statistical Modeling and Inference (pdf)]]<br /> :*[[media: CohenClassSlides12_2_13.pdf | Slides used in class 12/2/14 and 12/4/14]]<br /> <br /> :*The General Linear Model<br /> *Linear Algebra applied to Statistical Solutions<br /> :*Analysis of Variance<br /> <br /> ==''Wednesday 11/20/14 ...''==<br /> === - Statistics for Imaging II. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> *Fixed and Random Effects<br /> *Repeated measures<br /> :*Bonferroni and Other Corrections<br /> *Non-Parametric Methods<br /> *Autocorrelation<br /> *Unknown Distributions<br /> <br /> ''Required Readings''<br /> :*[[media: CohenClassSlides12_2_13.pdf‎ | same as 12/2/14]]<br /> <br /> ''Suggested Further Reading''<br /> <br /> :*[http://ccn.ucla.edu/media/PNI20121121.mp4 Lecture Video - To be Posted Shortly - Please Right Click and Save rather than Stream - Thank you Alan and Edward!]<br /> <br /> =Week 9: =<br /> ==''Monday 11/25/14''==<br /> === - TBD. [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> ==''Wednesday 11/27/14''==<br /> ===- The Organization of the Human Brain. ''Speaker'': [http://ccn.ucla.edu/bmcweb/bmc_bios/SusanBookheimer/ Susan Bookheimer]===<br /> <br /> We will discuss the general organization of the human brain, and the regional specialization of cortical areas. The emphasis will be on understanding principles of organization:<br /> *Phylogenetic Layering<br /> *Functional Specialization<br /> *Principles Divisions of the Brain<br /> *Brain Systems<br /> <br /> ''Required Readings''<br /> :*[http://da.biostr.washington.edu:80/cgi-bin/DA/PageMaster?atlas:NeuroSyllabus+ffpathIndex/Splash^Page^Syllabus+2 Neuroanatomy Programmed Learning]<br /> :*[[media:Cognitive_science_and_neuro_2012.pdf | Slides shown in Class UPDATED 2012/10/17]]<br /> ''Suggested Further Reading''<br /> <br /> :*[[media:PNIA_2012_PS1_Soln.pdf‎ | Solution to Problem Set 1]]<br /> ===- Human Electrophysiology ''Speakers'': [http://greenlab.npih.ucla.edu/ROSTER.html Agatha Lenartowicz], [http://dgsom.healthsciences.ucla.edu/institution/personnel?personnel_id=9140 John Stern]===<br /> <br /> ''Evoked Responses'' - Guest Lecturer: [http://greenlab.npih.ucla.edu/ROSTER.html Jonathan Wynn]<br /> *A look at real EEG data<br /> *Preprocessing:<br /> **filtering<br /> **artifact detection/removal<br /> *averaging<br /> *single events<br /> *interpretation<br /> <br /> ''Clinical EEG'' - Guest Lecturer: [http://dgsom.healthsciences.ucla.edu/institution/personnel?personnel_id=9140 John Stern]<br /> *Normal and Abnormal EEG<br /> *EEG as a marker for brain state<br /> **sleep staging<br /> **alpha and relaxation<br /> *Neurofeedback??? --&gt;</div>

Alenartowicz https://www.ccn.ucla.edu/wiki/index.php?title=Principles_of_Neuroimaging_A_-_2016&diff=3190 2016-10-10T17:38:20Z

<p>Alenartowicz: /* - Images. Speaker: Mark Cohen */</p> <hr /> <div>Principles of Neuroimaging A, Fall, 2016 - Class Schedule and Syllabus<br /> <br /> <br /> ==='''Neuroimaging journal Club (''required for NITP certificate'')'''===<br /> Contact: Katherine Lawrence (katherine.E.Lawrence@ucla.edu) or Janelle Liu (janelle.j.liu@ucla.edu), Faculty Sponsor: Jamie Feusner (JFeusner@mednet.ucla.edu)<br /> <br /> ==This schedule ''will'' change!==<br /> <br /> :'''[[Principles_of_Neuroimaging_-_2016-2017 | Back to main course page for Principles of Neuroimaging]]'''<br /> <br /> :'''Coming up next!: [[Principles_of_Neuroimaging_B_-_2017 | M284B Principles of Neuroimaging B]]'''<br /> <br /> =Course Reading=<br /> ===Required Reading===<br /> :'''Signal Processing for Neuroscientists''' by ''Wim van Drongelen''<br /> ::This can be found as a PDF on scribd.com, for a small fee of $8.99<br /> <br /> ===Supplemental Reading===<br /> :'''Matlab for Neuroscientists'''<br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/88212458/Matlab-Matlab-for-Neuroscientists<br /> <br /> :'''Cartoon Guide to Statistics''' <br /> ::Link for download found here for a small fee: http://www.scribd.com/doc/148072668/Cartoon-Guide-to-Statistics<br /> <br /> :'''NOTE:''' if you subscribe for a Scribd account for a day, you can download as many documents as you like for one fee.<br /> <br /> <br /> =Week 1: Orientation to Neuro-imaging, Neurons, Brains=<br /> <br /> ==''Monday 9/26/16'' ==<br /> ===- Images. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:M284AImages-intro.pdf | Lecture Slides Images]]<br /> <br /> ==''Wednesday 9/28/16''==<br /> ===- Neurons &amp; Signaling. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> In this first class we will review the basics of neurophysiology with an eye towards what signals of brain function might be visible to the neuroimager. We will discuss information coding, energetics, size and time scales.<br /> [[Image:Neurons.jpg|right]]<br /> ''Required Readings'' - Please complete these readings prior to class.<br /> :*[[media:TheActiveBrain.pdf | The Active Brain]]<br /> :*[[media:NeuronFunctionM284-2016_SM.pdf | Neuron function slides shown in class]]<br /> :*[http://www.science.smith.edu/departments/neurosci/courses/bio330/squid.html Videos of giant squid experiments]<br /> ''Suggested Further Reading''<br /> :*[http://ccn.ucla.edu/wiki/images/5/5a/CAVEAT_LECTOR.pdf Caveat Lector - the misuse of neuroimaging]<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Protected/Kosslyn1999.pdf &quot;If Neuroimaging is the Answer, What is the Question?&quot; Kosslyn, 1999]<br /> :*[http://da.biostr.washington.edu:80/cgi-bin/DA/PageMaster?atlas:NeuroSyllabus+ffpathIndex/Splash^Page^Syllabus+2 Neuroanatomy Programmed Learning]<br /> :*[http://www.amazon.com/Fundamental-Neuroscience-Second-Larry-Squire/dp/0126603030 Squire, Fundamentals of Neuroscience]<br /> :*[http://www.amazon.com/Principles-Neural-Science-Eric-Kandel/dp/0838577016 Kandel, et al., &quot;Principles of Neural Science&quot;]<br /> :This paper, by Malhi, is a nice orientation in methods of neuroimaging. *[http://www.ccn.ucla.edu/wiki/images/f/f2/Malhi2007.pdf Making sense of neuroimaging in psychiatry]<br /> :*[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1359308/pdf/jphysiol01232-0142.pdf Replacement of the axoplasm of giant nerve fibres with artificial solutions]<br /> <br /> =Week 2: Linear Systems, Convolution, Fourier Transforms=<br /> ==''Wednesday 10/3/16''==<br /> <br /> ===- Linear Systems I. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ==''Wednesday 10/5/16''==<br /> ===- Linear Systems II. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> Why the emphasis on Linear Systems? Because they are actually ''easy'' (as compared to non-linear systems, which are not.) As we go through this course, we will see many ways in which linear systems theory is applied to:<br /> :Modeling of Neural Systems<br /> :Extraction of Signal from Noise<br /> :Design of Circuits<br /> :Image Enhancement<br /> :Understanding of Image artifacts, and others.<br /> <br /> Linear systems analysis is one of the great technologies of the 20th and 21st century. It is now the basis for virtually all electronics design, and its extension into the discrete (digital) domain is the basis for most of modern signal processing. <br /> <br /> In our specific case, we will use these few basic principles of linear systems to understand both the instruments we use and the neuroimaging signals we collect. When you have mastered this material, you should be in a much better position to model the systems that you study in order to develop an approach to studying them.<br /> <br /> Here is [http://www.brainmapping.org/NITP/PNA/Readings/ImaginaryNumbers.pdf A primer I wrote on imaginary numbers] that might be a helpful review.<br /> <br /> There is a nice [http://en.wikibooks.org/wiki/Calculus Wikibook on Calculus].<br /> <br /> ''Required Readings''<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapter 1<br /> :*[[media:MathematicalTools2016.pdf | Mathematical Tools]]<br /> <br /> ''Suggested Further Reading''<br /> :'''Introduction to matlab'''<br /> <br /> ''Slides shown in class''<br /> :[[media:LinearityM284A-2016.pdf | Linearity and the Fourier Transform]]<br /> <br /> Please see [http://www.brainmapping.org/NITP/PNA/html/Linearity.html MATLAB linearity demo]<br /> <br /> If you are the type who sees beauty in mathematics, the Euler identity may be one of the most beautiful pieces of math in the world.<br /> <br /> =Week 3: Math &amp; Circuits I=<br /> <br /> ==''Monday 10/10/16''==<br /> === - Circuits I. Speaker: Cameron Rodriguez ===<br /> We will continue with the linear systems lecture and move to circuits<br /> <br /> ==''Wednesday 10/12/16''==<br /> === - Circuits II. ''Speaker'': Cameron Rodriguez ===<br /> <br /> Why circuits?<br /> :(Virtually) Every device you use in your research is electronic. You access your primary data only indirectly<br /> :The device you ''really'' want in your lab doesn't exist. You very well may have to make it.<br /> :There are electronic analogs to most of the linear systems that you have so far studied (and ''vice versa'' - the tools you now understand can be used to analyze and predict circuit behavior).<br /> <br /> :If you have not had any of this background, you might want to have a look at this handout, [[Media:Electricity.pdf|Electrical Circuits]], in advance. There are near infinite numbers of resources on the web that cover similar material (near enough to infinite that by the time you read all of them, there would be a whole new set.) I have recently come across a link to [http://www.allaboutcircuits.com/ Online Books: All About Circuits] ''IF'' you want practical hands-on knowledge about this material, my all-time favorite text is [http://www.google.com/products/catalog?hl=en&amp;client=safari&amp;rls=en-us&amp;ei=uVSPSfaxE5nMsAPf-tmSCQ&amp;resnum=1&amp;q=art+of+electronics&amp;um=1&amp;ie=UTF-8&amp;cid=8820839049329255765#ps-sellers &quot;Horowitz and Hill: ''The Art of Electronics.''&quot;] The latest edition, however, is dated 1989 and a new third edition is promised. I have therefore stopped short of recommending a purchase unless your need to make circuits is immediate. In this book, you will find an excellent education on the fundamental principles of electrical circuits and an incredible compendium of practical data, such as how to assemble circuit boards, how to make measurements, etc...) <br /> <br /> Readings:<br /> :*[[Media: Circuits.pdf|Circuits 1 &amp; 2]]<br /> :*[http://www.ti.com/lit/an/sloa093/sloa093.pdf Filter Design in 30 Seconds]<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapter 2 and 10<br /> :*[https://www.circuitlab.com/ Circuit Lab ] A Free Circuit Web Base Simulator<br /> **You may or may not find this comprehensible without chapters 5 through 9.<br /> <br /> We will discuss:<br /> :*Passive Circuit Elements: Resistors, Capacitors, Inductors<br /> :*Gain<br /> :*Transformers<br /> :*Rectifiers<br /> :*Active Elements<br /> ::- ''Amplifiers''<br /> ::- ''Transistors''<br /> ::- ''Op Amps''<br /> :*Solutions with Matrices<br /> ''Suggested Further Reading''<br /> :*[http://video.google.com/videoplay?docid=5645396659673218353&amp;q=Physics+for+Future+Presidents+Electricity&amp;total=5&amp;start=0&amp;num=10&amp;so=0&amp;type=search&amp;plindex=0#0h20m30s Video intro lecture on charge, current and voltage].<br /> :*[[media:Electricity_Basics.pdf‎ | Well organized text on electrical concepts by Tony R. Kuphaldt]]<br /> :*[http://en.wikibooks.org/wiki/Circuit_Theory Circuit Theory - Wikibook]<br /> <br /> '''Suggested, Optional Readings from [http://www.dspguide.com DSPguide.com]:'''<br /> :*[http://www.dspguide.com/CH5.PDF Linear Systems]<br /> :*[http://www.dspguide.com/CH6.PDF Convolution]<br /> :*[http://www.dspguide.com/CH8.PDF Discrete Fourier Transform (DFT)]<br /> :''Note: These chapters are light on math and try to focus on a conceptual understanding''<br /> <br /> Time and Frequency / Spectral Filters<br /> <br /> ----<br /> Practice using the Fourier transform:<br /> :[http://www.brainmapping.org/NITP/PNA/ConvFThtml/ConvolutionWorksheet.pdf Fourier transform and Convolution Worksheet]. &lt;!-- [http://www.brainmapping.org/NITP/PNA/ConvFThtml/ConvFT.html (''Solutions'').] --&gt;<br /> :[http://www.brainmapping.org/NITP/PNA/ConvFThtml/Something.wav Sound file for worksheet above.]<br /> <br /> :*[[Media: Circuits.pdf|Circuits 1 &amp; 2]]<br /> Other<br /> :*[http://www.youtube.com/watch?feature=player_embedded&amp;v=7uHfjpU3OH0#! Getting Started with Arduino]<br /> <br /> <br /> =Week 4 Signal Processing=<br /> <br /> ==''Monday 10/17/16''==<br /> === - Signal Processing. ''Speaker'': Cameron Rodriguez ===<br /> <br /> <br /> ==''Wednesday 10/19/16''==<br /> === - Noise ''Speaker'': Cameron Rodriguez ===<br /> <br /> It is what you ''don't'' want - usually - but things change in quantized systems<br /> :Additive noise<br /> :White Noise<br /> :Boltzmann noise<br /> :Colored Noise<br /> :Gaussian Noise<br /> :Coherent noise<br /> :Sampling Errors<br /> :Aliasing<br /> :Quantization noise<br /> :Spectral filtering<br /> <br /> Noise comes in all shapes and colors. It is present in every measurement we make, from an EEG voltage to an estimate of the effects of dopamine on forebrain signal. Our best weapons are an understanding of the statistical properties of noise, the sources of noise and the ways to control it. Noise in the discrete digital domain is special, as it is both ''created'' by digitization and amplified by sampling.<br /> <br /> Readings:<br /> :*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapters 2 through 4<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Noise.pdf Noise Slides]<br /> <br /> <br /> =Week 5 - Information &amp; Statistical Theory =<br /> <br /> ==''Monday 10/24/16 - in C8-177''==<br /> === Noise Cont' &amp; Information Theory Speakers: Cameron Rodriguez &amp; John Villasenor===<br /> <br /> ==''Wednesday 10/26/16''==<br /> ===Statistical Theory ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> We will consider the general problems of statistical inference, with a concentration on developing an intuitive understanding of statistical concepts.<br /> [[Image:MeasureForMeasure.jpg|right]]<br /> <br /> Suggested reading<br /> :*[http://www.statsoft.com/textbook/stbasic.html Statsoft online text (''free'')]<br /> :*[http://www.amazon.com/Cartoon-Guide-Statistics-Larry-Gonick/dp/0062731025 The Cartoon Guide to Statistics - Gonick $17.95 new]<br /> :The latter teaches stats at what I feel to be the right level - developing intuitions about the kinds of questions that can be answered using stats and about the statistical tests and measures<br /> <br /> :'''Problem Set 3 - Statistics in matlab'''<br /> ::[[media: Problem_Set_1.doc|Problem set using stats and MATLAB]]<br /> ::[[media: Problem_Set_1B.doc|More practice with stats and MATLAB]]<br /> <br /> <br /> =MIDTERM=<br /> <br /> <br /> =Week 6 - Neurophysiology=<br /> ==''Monday 10/31/16''==<br /> === Midterm Review ===<br /> <br /> ==''Wednesday 11/2/16''==<br /> === Electrophysiology ''Speaker'': [http://alenarto.bol.ucla.edu Agatha Lenartowicz] ===<br /> We will examine our first imaging modality, EEG (and MEG).<br /> <br /> <br /> =Week 7 - Neurophysiology =<br /> ==''Monday 11/7/16''==<br /> ===EEG Signal Processing ''Speaker'': Agatha Lenartowicz, Cameron Rodriguez===<br /> <br /> ==''Wednesday 11/9/16''==<br /> ===Intracranial Recordings ''Speaker'': Nanthia Sultana ===<br /> <br /> <br /> =Week 8 - Practical Neurophysiology=<br /> ==''Monday 11/14/16''==<br /> ===Building an EEG System - Circuit 1 ''Speaker'': Cameron Rodriguez===<br /> <br /> ==''Wednesday 11/16/16''==<br /> ===Building an EEG System - Circuit 2 ''Speaker'': Cameron Rodriguez===<br /> <br /> :*[https://learn.sparkfun.com/tutorials/how-to-solder Through holesoldering].<br /> :*[https://learn.sparkfun.com/tutorials/pcb-basics Printed Circuit Board Basics].<br /> :*[https://learn.sparkfun.com/tutorials/how-to-install-and-setup-eagle Installing and Setting Up Eagle].<br /> :*[https://learn.sparkfun.com/tutorials/using-eagle-schematic Using Eagle Schematic].<br /> :*[https://learn.sparkfun.com/tutorials/using-eagle-board-layoutc Using Eagle Board Layout].<br /> <br /> :*[https://www.sparkfun.com/videos#pete/NJKZZArjdg8 PCB Layout 1 - According to Pete];<br /> :*[https://www.sparkfun.com/videos#pete/JANZsjRiM3w PCB Layout 2 - According to Pete];<br /> <br /> <br /> =Week 9 - Magnetic Resonance Fundamentals =<br /> ==''Monday 11/21/16 - in C8-177''==<br /> ===MR Signal Origin ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ==''Wednesday 11/23/16''==<br /> ===Thankgiving Wed - No Class ===<br /> <br /> Magnetic Resonance Imaging (MRI) is probably the most influential and most flexible current means of imaging the human brain. It features a vast number of separable contrast mechanisms, and a near ideal combination of non-invasiveness, safety, resolution and metric accuracy. However, it is extraordinarily expensive and has limited temporal resolution, especially for functional studies<br /> <br /> OUTLINE<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> :*[http://www.cis.rit.edu/htbooks/mri/ These notes] by Joseph Hornak are highly professional and complete coverage of MRI.<br /> :*[http://www.imaios.com/en/e-Courses/e-MRI eMRI] is another excellent online MRI learning resource<br /> :*[http://www.brainmapping.org/NITP/PNA/Readings/Hahn1950.pdf Erwin Hahn - Spin Echoes: ''Essential reading for the MRI community'']<br /> [[Image:HahnFig1.png]]<br /> ''above'': Figure 1 from Hahn, 1950<br /> <br /> ''Suggested Further Reading''<br /> [[image:PSatSeq.jpg|right]]<br /> ''Required Readings''<br /> :*[[media:MRIforPNIB2014_sm.pdf‎ | MRI Slides]]<br /> <br /> =Week 10 - Magnetic Resonance Fundamentals =<br /> ==''Monday 11/28/16''==<br /> ===MR Physics ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ==''Wednesady 11/30/16''==<br /> ===TBD/Q&amp;A===<br /> <br /> <br /> =Week 11 - Finals Week=<br /> ==''Monday 11/5/16''==<br /> ===Final Exam===<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;!--<br /> SPARE CONTENT / PREVIOUS YEARS<br /> <br /> =Week 3: Stats begins!=<br /> <br /> ==''Monday 10/12/15''==<br /> <br /> ===- Statistical Fundamentals. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> :*[[media: CohenClassIntroStats11_25_13.pdf‎ | Slides used in class (set 1)]]<br /> <br /> ''Review of'':<br /> :*Descriptive Statistics: mean, mode, variance, standard deviation<br /> :*Statistical Inference. The Binomial and Normal Distribution<br /> :*Basic Tests: t-test, linear correlation<br /> :*Modeling and non-linear relations<br /> :*Bayes rule<br /> <br /> <br /> ==''Wednesday 10/14/15''==<br /> ===- Statistical Fundamentals II. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> =Week 4 - Stats and circuits=<br /> ==''Monday 10/19/15''==<br /> ===- Statistical Fundamentals III. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> ==''Wednesday 10/21/15''==<br /> ===Circuits continued: Active devices. [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> <br /> ===Optics I. ''Speaker'': [mailto:zdeis@seas.ucla.edu Zachary Taylor]===<br /> [[Image:Reflection.jpg|right]]<br /> The overall goal of this lecture is to establish that:<br /> ''- Physical constants have tangible meanings''<br /> ''- Plane waves form a physically unrealizable but extremely good approximation to real systems''<br /> ''- Boundaries bend light''<br /> ''- Physical constants, plane wave mechanics, and boundaries can be used to describe the operation of a lens''<br /> ''- The PSF gives a good indication of the overall performance of an imaging system''<br /> ''- All of these concepts have analogues in other areas of engineering (ie circuits, mechanical vibrations, etc.)''''<br /> <br /> '''Outline:'''<br /> :* Constitutive parameters (ε, μ, η, n, etc.)<br /> :* Plane wave basics<br /> :* Plane waves at boundaries<br /> :* Lenses<br /> :* Advanced imaging properties of lenses<br /> :* Point spread function.<br /> <br /> ''Required Readings''<br /> Zach has very kindly agreed to post his [http://www.brainmapping.org/NITP/PNA/Readings/OpticsTaylor3-10-10.pdf Optics lecture notes].<br /> ''Suggested Further Reading''<br /> <br /> <br /> <br /> <br /> ==''Wednesday 11/19/14''== <br /> ===Optogenetics. ''Speaker'': [http://faculty.neuroscience.ucla.edu/institution/personnel?personnel_id=47031 Tom Otis]===<br /> <br /> <br /> =Week 8: =<br /> ==''Monday 12/1/14 ''==<br /> ===- Statistics for Imaging. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> <br /> [[image:BVTradeoff.jpg|right]]<br /> ''Required Readings''<br /> :*[[media: Mumford_stat_modeling.pdf | Statistical Modeling and Inference (pdf)]]<br /> :*[[media: CohenClassSlides12_2_13.pdf | Slides used in class 12/2/14 and 12/4/14]]<br /> <br /> :*The General Linear Model<br /> *Linear Algebra applied to Statistical Solutions<br /> :*Analysis of Variance<br /> <br /> ==''Wednesday 11/20/14 ...''==<br /> === - Statistics for Imaging II. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]===<br /> *Fixed and Random Effects<br /> *Repeated measures<br /> :*Bonferroni and Other Corrections<br /> *Non-Parametric Methods<br /> *Autocorrelation<br /> *Unknown Distributions<br /> <br /> ''Required Readings''<br /> :*[[media: CohenClassSlides12_2_13.pdf‎ | same as 12/2/14]]<br /> <br /> ''Suggested Further Reading''<br /> <br /> :*[http://ccn.ucla.edu/media/PNI20121121.mp4 Lecture Video - To be Posted Shortly - Please Right Click and Save rather than Stream - Thank you Alan and Edward!]<br /> <br /> =Week 9: =<br /> ==''Monday 11/25/14''==<br /> === - TBD. [http://www.brainmapping.org/MarkCohen Mark Cohen]===<br /> ==''Wednesday 11/27/14''==<br /> ===- The Organization of the Human Brain. ''Speaker'': [http://ccn.ucla.edu/bmcweb/bmc_bios/SusanBookheimer/ Susan Bookheimer]===<br /> <br /> We will discuss the general organization of the human brain, and the regional specialization of cortical areas. The emphasis will be on understanding principles of organization:<br /> *Phylogenetic Layering<br /> *Functional Specialization<br /> *Principles Divisions of the Brain<br /> *Brain Systems<br /> <br /> ''Required Readings''<br /> :*[http://da.biostr.washington.edu:80/cgi-bin/DA/PageMaster?atlas:NeuroSyllabus+ffpathIndex/Splash^Page^Syllabus+2 Neuroanatomy Programmed Learning]<br /> :*[[media:Cognitive_science_and_neuro_2012.pdf | Slides shown in Class UPDATED 2012/10/17]]<br /> ''Suggested Further Reading''<br /> <br /> :*[[media:PNIA_2012_PS1_Soln.pdf‎ | Solution to Problem Set 1]]<br /> ===- Human Electrophysiology ''Speakers'': [http://greenlab.npih.ucla.edu/ROSTER.html Agatha Lenartowicz], [http://dgsom.healthsciences.ucla.edu/institution/personnel?personnel_id=9140 John Stern]===<br /> <br /> ''Evoked Responses'' - Guest Lecturer: [http://greenlab.npih.ucla.edu/ROSTER.html Jonathan Wynn]<br /> *A look at real EEG data<br /> *Preprocessing:<br /> **filtering<br /> **artifact detection/removal<br /> *averaging<br /> *single events<br /> *interpretation<br /> <br /> ''Clinical EEG'' - Guest Lecturer: [http://dgsom.healthsciences.ucla.edu/institution/personnel?personnel_id=9140 John Stern]<br /> *Normal and Abnormal EEG<br /> *EEG as a marker for brain state<br /> **sleep staging<br /> **alpha and relaxation<br /> *Neurofeedback??? --&gt;</div>

Alenartowicz https://www.ccn.ucla.edu/wiki/index.php?title=Principles_of_Neuroimaging_-_2016-2017&diff=3189 2016-10-10T16:33:48Z

<p>Alenartowicz: /* Programming */</p> <hr /> <div>[[File:NeuroImages.jpg | x203px]]<br /> =If you are a guest instructor, please read: [[Notes for Instructors]].=<br /> &lt;br /&gt;<br /> <br /> =Class Meetings=<br /> ===Mondays and Wednesdays at from 2-4 pm in '''[http://maps.ucla.edu/campus/?zlvl=10&amp;cpnt=-118.4441009215107,34.065875066286004 Room 17-369]''' of the Semel Institute on the first floor of the NPI.===<br /> &lt;br /&gt;<br /> <br /> =Course Schedule &amp; Syllabus=<br /> * [[Principles_of_Neuroimaging_A_-_2016 | '''Course Schedule &amp; Syllabus M284A (link)''']]<br /> * [[Principles_of_Neuroimaging_B_-_2017 | '''Course Schedule &amp; Syllabus M284B (link)''' ]]<br /> &lt;br /&gt;<br /> <br /> =General Information=<br /> This is a Wiki: You are encouraged to post comments and clarifications.<br /> ==Course Goals==<br /> The overall goal of this course, and of the NITP teaching program, is to give you a solid background in the concepts common to many types of neuroimaging, as well as a set of tools to think about and to analyze these images in the service of scientific hypothesis testing. There are ways of thinking about images that are shared across microscopy, positron emission tomography, EEG, X-ray, MRI and many others and that a good understanding of these will leave you prepared to take on not only the current armamentarium of imaging tools, but the newer methods that will arise during your careers.<br /> <br /> Extract information from images ''always'' implies the existence of a model for that information. Generally, we seek to remove extraneous content (by ''filtering''), and seek evidence in the images of data that conform to our model, usually by comparing what's in the image data to our model. This course concerns itself with themes in signal detection, statistical analysis, modeling, filtering, and evidence.<br /> <br /> Our eyes act as filters, our prior experiences as hypotheses, our entire perceptual system as models. Likewise, the devices themselves instantiate models of the world or of the data we hope to detect. A mission of this course is to make us more aware of the implicit expectations built in to all current imaging tools.<br /> <br /> This year, we will explore emerging concepts in imaging - new, and groundbreaking science.<br /> <br /> ===Teaching Philosophy===<br /> At the graduate level, IMHO the courses are not about grades, but about learning at a professional level. We do not emphasize exams and papers but:<br /> <br /> &lt;ol&gt;<br /> &lt;li&gt; This is a core course in several departments. Rigorous grading is required and, &lt;/li&gt;<br /> &lt;li&gt; Preparing for evaluations tends to force one to think and consolidate information.&lt;/li&gt;<br /> &lt;/ol&gt;<br /> Much more important, however, is your commitment to reading the material and participating in class. This means challenging the lecturers and students to be clear about concepts, and to place their work in the broadest context possible.<br /> <br /> Because the emphasis is on skills learning, as much as on content, we will prepare lectures and exercises on tools, including math, engineering and programming, that I hope will be useful to you for years into the future.<br /> <br /> MATLAB will be required for the course. While I had tried in prior classes to allow students to use a variety of programming languages, I found that this made things complicated for everybody. Usually, the example data will be made available through the course web site and, in many cases, there will be matlab code associated with it, so that you can open the files and read the data. You can purchase student copies of MATLAB for $99 (which is a bargain, BTW). If, for some reason, this is a hardship, please let me know and I will make arrangements on your behalf. I will provide some basic training in the software, but you should go through the tutorials on your own.<br /> <br /> Form time-to-time, we might collect some live example data during the course and will need volunteers willing to participate. If you would like to volunteer to have your brain studied, please contact me.<br /> <br /> ===Required Text===<br /> This year, as in the past, we will be ''informally'' using [http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description '''Signal Processing for Neuroscientists: An Introduction to the Analysis of Physiological Signals'''] by Wim can Drongelen - This comes with a CD containing Matlab code for the examples, all of which are based in neuroscience (e.g. EEG, spike trains, etc...) Intensive use of this book will start no sooner than week 3. You can obtain the book at ASUCLA bookstore.<br /> <br /> ===Further Reading===<br /> There are many links to reading materials on the [[Principles_of_Neuroimaging_A_-_2016 | '''Course Schedule &amp; Syllabus M284A''' ]] and [[Principles_of_Neuroimaging_B_-_2017 | '''Course Schedule &amp; Syllabus M284B''' ]] pages. If they are optional, it will say so.<br /> <br /> For the statistics sections, I STRONGLY recommend <br /> *[http://www.amazon.com/Cartoon-Guide-Statistics-Larry-Gonick/dp/0062731025 The Cartoon Guide to Statistics - Gonick $17.95 new]. This book is available at ASUCLA.<br /> Some other excellent resource texts include:<br /> *[http://www.amazon.com/MATLAB-Behavioral-Scientists-David-Rosenbaum/dp/0805863192/ref=ed_oe_p Matlab for Behavioral Scientists]<br /> *[http://www.amazon.com/Matlab-Neuroscientists-Introduction-Scientific-Computing/dp/0123745519/ref=sr_1_1?ie=UTF8&amp;s=books&amp;qid=1231366863&amp;sr=1-1 Matlab for Neuroscientists]<br /> *[http://www.amazon.com/Understanding-Digital-Signal-Processing-2nd/dp/0131089897/ref=sr_1_1?ie=UTF8&amp;s=books&amp;qid=1231979157&amp;sr=1-1 Understanding Digital Signal Processing] - An easy to understand explanation of digital sampling and [http://www.popularreview.com reviewing], various Fourier transforms, types of filtering, etc.<br /> *[http://www.elsevierdirect.com/v2/companion.jsp?ISBN=9780123708670 Signal Processing Matlab Files] - A Link to the Matlab file for the above book can be found here<br /> *[http://www.dspguide.com/pdfbook.htm The Scientist and Engineer's Guide to Digital Signal Processing] - '''FREE''' online DSP book with '''FREE''' downloads of each chapter in pdf format! I will occasionally post links on the [[syllabus page]] to chapters relevant current course lectures (kmc).<br /> <br /> ===Problem Sets===<br /> Problem sets will occur about once per week. Generally, you will have a week to work on them. These are often are kept simple and mechanical in order to learn the mechanics; some may be more challenging. You will use these skills in the midterm and final.<br /> <br /> Please send these to Cameron Rodriguez [mailto:cdrodriguez@g.ucla.edu (cdrodriguez@g.ucla.edu)]. If you do not get a response that your mail has been received, call or otherwise follow up with the instructors.<br /> <br /> Please Always Include This Title Line: '''M284 2016/17 Problem Set''', so that your mails are never lost.<br /> <br /> ==Instructor Information==<br /> Agatha Lenartowicz can be reached at [mailto:alenarto@g.ucla.edu alenarto@g.ucla.edu].<br /> Cameron Rodriguez can be reached at [mailto:cdrodriguez@g.ucla.edu cdrodriguez@g.ucla.edu].<br /> Office hours will be after class on Mondays and Wednesdays in room 17-369 of the NPI.<br /> <br /> ==Organizational notes==<br /> When sending mail about the course, please include the characters: '''NITP''' in the subject line somewhere, as that helps a great deal in file management. Thanks.<br /> <br /> While most of the classes will be in lecture format, there will also be lab work in computing, electronics and image collection. It may be necessary to schedule these outside of standard class hours to accommodate the availability of the equipment we need.<br /> <br /> ==Class List sign up==<br /> As soon as possible, please add yourself to the list of students in the class.<br /> [http://ccn.ucla.edu/mailman/listinfo/neuroimaging Class List].<br /> <br /> Please also send an email directly to [mailto:cdrodriguez@g.ucla.edu Agatha Lenartowicz] with your name, your best contact email and a subject line of &quot;'''M284 class signup - NITP'''&quot;<br /> <br /> ==Auditing==<br /> Auditing means ''taking the course'', though not for credit. Auditors are expected to attend all lectures, participate in discussions, and do ''all'' problem sets and tests. For my part, I will read an score the materials. If you do not intend to actually do the classwork, auditing is actively discouraged. If you miss several assignments you will be asked to drop the course.<br /> <br /> =Catalog Course Description=<br /> Factors common to neuroimaging in multiple modalities including: Physiological Contrast mechanisms and Biophysics; Signal and Image processing, including transform approaches, Statistical Modeling and Inference, Time-Series Statistics, Detection Theory, Contrast Agents, Experimental Design, Modeling and Inference, Electrical Detection methods, Electroencephalography, Optical Methods, Microscopy.<br /> <br /> =Pre-Requisites=<br /> Functional Neuroanatomy (M292) and competence in 1) Integral calculus 2) Statistics 3) Electricity and Magnetism and 4) Computer Programming (any language). Waiver of some requirements may be possible by consent of the instructor.<br /> <br /> The following are examples of the level of knowledge expected on entry. If you do not have this background please let Mark know as soon as possible. We will do our best to remediate any missing knowledge.<br /> <br /> '''In week 1 of class please ensure you fill out this online quiz to assess your skills, both for our and your benefit [[https://docs.google.com/forms/d/e/1FAIpQLSftU5YiXipM64fGcAM-guvfUBsT60r-jzISWc-6smBiTfUd9g/viewform]]'''<br /> <br /> ==Stats==<br /> A general philosophy of the course and of the NITP is that a sophisticated consumer of images uses these data as a test of a hypothesis. You will learn more about the instructor's feelings about truth by ''p''-values, but it is important to have a good intuitive understanding of random processes, noise, reliability, estimation, ''etc...'' For this reason, stats comfort is a must.&lt;br&gt;<br /> ----<br /> Here are a few questions that you should be easily able to find the answers to:&lt;br&gt;<br /> Given a sample of student heights at UCLA in inches:&lt;br&gt;<br /> : H(&quot;males&quot;) = [74, 71, 67, 69, 71, 70, 65, 67, 71, 68, 69, 66], and&lt;br&gt;<br /> : H(&quot;females&quot;) = [62, 66, 68, 62, 65, 62, 63, 64]&lt;br&gt;&lt;ul&gt;<br /> &lt;li&gt;What is the modal height of the males?&lt;/li&gt;<br /> &lt;li&gt;What is the difference in mean height between males and females?&lt;/li&gt;<br /> &lt;li&gt;Which of the following ''should'' be used to test if the average height of UCLA males and females differ significantly at &quot;p&quot;&lt;0.01?<br /> &lt;ol&gt;<br /> &lt;li&gt; Increase the number of females in the sample be eight, then perform a ''t''-test on the means&lt;/li&gt;<br /> &lt;li&gt; Continue collecting more data until the probability of a two-tailed ''t''-test statistic comparing males and females is less than 0.01.&lt;/li&gt;<br /> &lt;li&gt; Collect the heights of &quot;all&quot; males and females at UCLA and then calculate the ''t''--statistic to determine if the heights differ at the assigned probability level&lt;/li&gt;<br /> &lt;li&gt; Collect height data from an age-matched sample in the surrounding community.&lt;/li&gt;<br /> &lt;li&gt; Add to the sample until there are exactly 100 males and 100 females, and calculate if the heights differ by more than 1%.&lt;/li&gt;<br /> &lt;li&gt; None of the above.&lt;/li&gt;<br /> &lt;li&gt; All of the above&lt;/li&gt;<br /> &lt;/ol&gt;<br /> &lt;/ul&gt;<br /> <br /> ==Programming==<br /> Formally, students are required to have a background in at least some programming language. The fact of the matter is that Neuroimaging is computationally intensive; programming is a basic skill for this work. I intend to prepare problem sets that will require programming to solve.&lt;br&gt;<br /> This year, all of our programming will be done using MATLAB, purchase of which is a course requirement. The [http://i2w3.ais.ucla.edu/asucla/store.aspx?pg=macsoftware.pdf ASUCLA student store] has the licenses for students at an incredibly discounted price of $99. You will not regret owning this.<br /> <br /> ''Many'' MATLAB tutorials can be found online. Here is a good<br /> [http://www.mathworks.com/academia/student_center/tutorials/register.html interactive beginner tutorial] from MathWorks. It takes about 2 hours and you must register with MathWorks beforehand, but it covers many aspects of MATLAB in depth (e.g. the workspace, importing data, visualizing data, scripts, functions, &amp; loops).<br /> <br /> Another useful option is the ''demo'' feature that can be accessed within MATLAB by typing 'demo' at the command prompt.<br /> <br /> &gt;&gt; demo<br /> <br /> This will open a help window of all available demos. Here are a few demos I recommend (kmc):<br /> *Importing Data from Files<br /> *Using Basic Plotting Functions<br /> *Working with Arrays<br /> *Manipulating Multidimensional Arrays<br /> <br /> == Mathematics ==<br /> Can you solve for y or &lt;math&gt;\mathbf{Y}&lt;/math&gt; in these equations?&lt;br&gt;<br /> &lt;math&gt;y = \frac{d(e^x)}{dx}&lt;/math&gt;&lt;br&gt;<br /> &lt;math&gt;y = \int\sin x\,dx&lt;/math&gt;&lt;br&gt;<br /> <br /> &lt;math&gt;\mathbf{Y}=\left[\begin{array}{cc}<br /> 2 &amp; 4\\<br /> 5 &amp; 7\end{array}\right]^{-1}&lt;/math&gt;&lt;br&gt;<br /> If &lt;math&gt;y = 3x^2 + 6x + 2&lt;/math&gt;, what is &lt;math&gt;\frac{d(e^x)}{dx}&lt;/math&gt;?<br /> &lt;br&gt;<br /> If not, please let me know, and we will try to remedy things. In the meantime, there are a number of excellent online math tutorials. For matrices, may I suggest:<br /> &lt;ul&gt;<br /> &lt;li&gt;[http://www.math.hmc.edu/calculus/tutorials/ Harvey Mudd mathematics online tutorial]<br /> &lt;li&gt;[http://www.sosmath.com/matrix/matrix.html S.O.S. Mathematics]&lt;/li&gt;<br /> &lt;li&gt;[http://www.mathworks.com/access/helpdesk/help/techdoc/matlab.html MATLAB online tutorial]&lt;/li&gt;<br /> &lt;li&gt;[ftp://joshua.smcvt.edu/pub/hefferon/book/book.pdf Programmed text in Linear Algebra - ''Hefferon'']<br /> &lt;/ul&gt;<br /> These are all excellent free sources. Please feel free to suggest more.<br /> <br /> ==Functional Neuroanatomy==<br /> Again, there are many excellent resources online! Including:<br /> &lt;ul&gt;<br /> &lt;li&gt;[http://www9.biostr.washington.edu/da.html Washington Interactive Atlas]&lt;/li&gt;<br /> &lt;li&gt;[http://thebrain.mcgill.ca/ McGill Brain Tutorial]&lt;/li&gt;<br /> &lt;li&gt;[http://www.anatomie-amsterdam.nl/sub_sites/anatomie-zenuwwerking/123_neuro/start.htm Amsterdam Brain Atlas]&lt;/li&gt;<br /> &lt;/ul&gt;<br /> <br /> =Concepts and Teaching Plan=<br /> We will start looking at a few papers that use ''images of various kinds to address neuroscientific questions.'' Here, you should be paying especial attention to how the images are used in a theoretical context. Did the investigator pose the question first then collect the data? What is the role of ''a posteriori'' interpretation (reverse inference)? What is assumed about the ground truth of the phenomena exposed by neuroimaging?<br /> <br /> After this, we will begin to look at the properties of neurons that might make them visible to our neuroimaging tools. We will consider signaling in neurons, its energetic costs, and the changes in the cellular milieu that are associated. We will begin to consider the optical properties of neurons and their size scale, and the chemical changes that are associated with neuroal activity. As best possible, I will try to incorporate neurogenetics here to consider cell identification and labeling.<br /> <br /> At the same time, we will start the ''practical work in MATLAB.'' If you are already MATLAB proficient, consider your assignment to include bringing the rest of the class up to speed as quickly as possible so that we can move on. As noted above, MATLAB will be used for our quantitative examples, but it is also a strong standard for image and numerical analysis in the sciences and a relatively easy programming language to use, with a pretty quick startup.<br /> <br /> We will start also, on developing the mathematical tools we will need to carry forward. In the digital age, we are dealing always with very large numbers of data points and are forced to deal with large sample sizes (at the very least, a large number of pixels) and we need means of quantitative summary. Our initial steps will be in very ''basic statistical concepts'' in anticipation of doing more and deeper work later.<br /> <br /> This will be followed by work on ''analytic math'', building to ''transform theory.'' Depending on what I find out about your skills level in maths, we may start with some calculus review, or we may have to schedule one-on-one meetings to balance everyone’s background. The goal here is to develop a framework with which to understand what happens to the ground truth data we try to observe as it is filtered through our imaging tools. There are very powerful mathematical tools that can be applied here, particularly the field known as linear systems analysis that considers ''transfer functions'' and especially ''convolution.'' Each device we build or use can be analyzed, at least in part, within this framework. More importantly, for many classes of systems, the filtering they apply can be inverted – in some cases unblurring and recapturing much of the original data. ''Deconvolution'' is the general rubrick under which we will try to analyze this process.<br /> <br /> Mathematical transforms are, in general, ways to change the representation of equations into forms that are much easier to solve, or that offer additional insight into the underlying properties. We will look at a few transforms, particularly the ''LaPlace Transform'' and the ''Fourier Transform.'' The latter is simply a means of expressing and quantifying the frequencies contained in a signal. The maths for these includes a little bit of trigonometry and some basic calculus. By the time we start on these topics, you should make yourself responsible for knowing how to integrate sines and cosines, and reviewing properties of the natural logarithm, e. I will introduce, in class, the concepts and algebra of imaginary numbers, which we will need as well.<br /> <br /> [[File:Fourier-cat.jpg | x250px]]<br /> <br /> The essential results of the Fourier transform find their way into literally every means we have of neuroimaging, the statistical processing of images, concepts of noise and a host of other applications in neuroscience. I truly believe, that although you may find this material difficult, you will be happy about knowing it for the rest of your career as a scientist, making it well worth the effort.<br /> <br /> Our first direct application of the analytic tools will be in the analysis and then creation of electrical circuits. We do this for several reasons. Unlike many real-world devices, electrical circuit elements: resistors, batteries, capacitors, inductors and operational amplifiers, act very much like their idealized representations, storing and converting energy in very predictable ways. The tools that have grown to analyze such circuit elements are very mature and quite powerful, making prediction of their behavior straightforward. For this reason, many real-world physics and imaging problems are ''modeled'' using electrical circuit elements where we can predict their input-output properties.<br /> <br /> The second reason for looking at electrical circuits is that they are present in more or less every lab instrument you are likely to use. Towards the end of the first quarter, we will build, in class, an EEG system based on your understanding of these devices. This will also give us an entrée into the important study of noise, which is present in any experiments. We will look at the many sources of noise in neuroimaging and experiments, and consider ways in which modeling the noise can help us to reduce it. Conversely, we will discuss ways in which we can study the characteristics of the noise in order to better understand either our devices, or the actual features of our images.<br /> <br /> We will cover principles of optics, emphasizing the issues of resolution, optical spectrum (frequency ranges), distortion and digital imaging. One way to think about the effects of lenses is as convolution filters (''see above'') that ''color'' the signal. Color, as used here, is a rather broad concept. The process of ''whitening'' the signal can be considered a deconvolution. Undoing the lens convolution is a way of removing the blur or distortion produced by a lens. As we go on, we will see this theme of convolution blurring and deconvolution sharpening applied to the many modalities used in modern neuroimaging. Similarly, statistical variance or noise can be reduced or at least better understood in this context, sharpening our statistical inferences and improving detection power.<br /> <br /> Our next foray will be into electroencephalography (EEG), which is a simply a measure of the differences in electrical voltage from point to point on the scalp or brain. In addition to looking at the biological basis of the EEG, we will build and test an EEG system in class and we will look at some software approaches to interpreting the EEG both as spatially-resolved (''i.e., image'') data and as cognitive/physiological signals.<br /> <br /> =Grading=<br /> Your final grades will be determined by the problem sets, the midterm and final and by your class participation. Generally the rubrick is:<br /> :*Participation 10%<br /> :*Problem Sets 25%<br /> :*Midterm 30%<br /> :*Final 35%.<br /> As you can see, your participation in the class is of major import, as I believe that everyone learns from other people's questions and comments.<br /> <br /> As M284 is a required course for some students continuation in several Ph.D. programs, grading will necessarily be rigorous.</div>

Alenartowicz https://www.ccn.ucla.edu/wiki/index.php?title=Principles_of_Neuroimaging_-_2016-2017&diff=3188 2016-10-10T16:30:38Z

<p>Alenartowicz: /* Programming */</p> <hr /> <div>[[File:NeuroImages.jpg | x203px]]<br /> =If you are a guest instructor, please read: [[Notes for Instructors]].=<br /> &lt;br /&gt;<br /> <br /> =Class Meetings=<br /> ===Mondays and Wednesdays at from 2-4 pm in '''[http://maps.ucla.edu/campus/?zlvl=10&amp;cpnt=-118.4441009215107,34.065875066286004 Room 17-369]''' of the Semel Institute on the first floor of the NPI.===<br /> &lt;br /&gt;<br /> <br /> =Course Schedule &amp; Syllabus=<br /> * [[Principles_of_Neuroimaging_A_-_2016 | '''Course Schedule &amp; Syllabus M284A (link)''']]<br /> * [[Principles_of_Neuroimaging_B_-_2017 | '''Course Schedule &amp; Syllabus M284B (link)''' ]]<br /> &lt;br /&gt;<br /> <br /> =General Information=<br /> This is a Wiki: You are encouraged to post comments and clarifications.<br /> ==Course Goals==<br /> The overall goal of this course, and of the NITP teaching program, is to give you a solid background in the concepts common to many types of neuroimaging, as well as a set of tools to think about and to analyze these images in the service of scientific hypothesis testing. There are ways of thinking about images that are shared across microscopy, positron emission tomography, EEG, X-ray, MRI and many others and that a good understanding of these will leave you prepared to take on not only the current armamentarium of imaging tools, but the newer methods that will arise during your careers.<br /> <br /> Extract information from images ''always'' implies the existence of a model for that information. Generally, we seek to remove extraneous content (by ''filtering''), and seek evidence in the images of data that conform to our model, usually by comparing what's in the image data to our model. This course concerns itself with themes in signal detection, statistical analysis, modeling, filtering, and evidence.<br /> <br /> Our eyes act as filters, our prior experiences as hypotheses, our entire perceptual system as models. Likewise, the devices themselves instantiate models of the world or of the data we hope to detect. A mission of this course is to make us more aware of the implicit expectations built in to all current imaging tools.<br /> <br /> This year, we will explore emerging concepts in imaging - new, and groundbreaking science.<br /> <br /> ===Teaching Philosophy===<br /> At the graduate level, IMHO the courses are not about grades, but about learning at a professional level. We do not emphasize exams and papers but:<br /> <br /> &lt;ol&gt;<br /> &lt;li&gt; This is a core course in several departments. Rigorous grading is required and, &lt;/li&gt;<br /> &lt;li&gt; Preparing for evaluations tends to force one to think and consolidate information.&lt;/li&gt;<br /> &lt;/ol&gt;<br /> Much more important, however, is your commitment to reading the material and participating in class. This means challenging the lecturers and students to be clear about concepts, and to place their work in the broadest context possible.<br /> <br /> Because the emphasis is on skills learning, as much as on content, we will prepare lectures and exercises on tools, including math, engineering and programming, that I hope will be useful to you for years into the future.<br /> <br /> MATLAB will be required for the course. While I had tried in prior classes to allow students to use a variety of programming languages, I found that this made things complicated for everybody. Usually, the example data will be made available through the course web site and, in many cases, there will be matlab code associated with it, so that you can open the files and read the data. You can purchase student copies of MATLAB for $99 (which is a bargain, BTW). If, for some reason, this is a hardship, please let me know and I will make arrangements on your behalf. I will provide some basic training in the software, but you should go through the tutorials on your own.<br /> <br /> Form time-to-time, we might collect some live example data during the course and will need volunteers willing to participate. If you would like to volunteer to have your brain studied, please contact me.<br /> <br /> ===Required Text===<br /> This year, as in the past, we will be ''informally'' using [http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description '''Signal Processing for Neuroscientists: An Introduction to the Analysis of Physiological Signals'''] by Wim can Drongelen - This comes with a CD containing Matlab code for the examples, all of which are based in neuroscience (e.g. EEG, spike trains, etc...) Intensive use of this book will start no sooner than week 3. You can obtain the book at ASUCLA bookstore.<br /> <br /> ===Further Reading===<br /> There are many links to reading materials on the [[Principles_of_Neuroimaging_A_-_2016 | '''Course Schedule &amp; Syllabus M284A''' ]] and [[Principles_of_Neuroimaging_B_-_2017 | '''Course Schedule &amp; Syllabus M284B''' ]] pages. If they are optional, it will say so.<br /> <br /> For the statistics sections, I STRONGLY recommend <br /> *[http://www.amazon.com/Cartoon-Guide-Statistics-Larry-Gonick/dp/0062731025 The Cartoon Guide to Statistics - Gonick $17.95 new]. This book is available at ASUCLA.<br /> Some other excellent resource texts include:<br /> *[http://www.amazon.com/MATLAB-Behavioral-Scientists-David-Rosenbaum/dp/0805863192/ref=ed_oe_p Matlab for Behavioral Scientists]<br /> *[http://www.amazon.com/Matlab-Neuroscientists-Introduction-Scientific-Computing/dp/0123745519/ref=sr_1_1?ie=UTF8&amp;s=books&amp;qid=1231366863&amp;sr=1-1 Matlab for Neuroscientists]<br /> *[http://www.amazon.com/Understanding-Digital-Signal-Processing-2nd/dp/0131089897/ref=sr_1_1?ie=UTF8&amp;s=books&amp;qid=1231979157&amp;sr=1-1 Understanding Digital Signal Processing] - An easy to understand explanation of digital sampling and [http://www.popularreview.com reviewing], various Fourier transforms, types of filtering, etc.<br /> *[http://www.elsevierdirect.com/v2/companion.jsp?ISBN=9780123708670 Signal Processing Matlab Files] - A Link to the Matlab file for the above book can be found here<br /> *[http://www.dspguide.com/pdfbook.htm The Scientist and Engineer's Guide to Digital Signal Processing] - '''FREE''' online DSP book with '''FREE''' downloads of each chapter in pdf format! I will occasionally post links on the [[syllabus page]] to chapters relevant current course lectures (kmc).<br /> <br /> ===Problem Sets===<br /> Problem sets will occur about once per week. Generally, you will have a week to work on them. These are often are kept simple and mechanical in order to learn the mechanics; some may be more challenging. You will use these skills in the midterm and final.<br /> <br /> Please send these to Cameron Rodriguez [mailto:cdrodriguez@g.ucla.edu (cdrodriguez@g.ucla.edu)]. If you do not get a response that your mail has been received, call or otherwise follow up with the instructors.<br /> <br /> Please Always Include This Title Line: '''M284 2016/17 Problem Set''', so that your mails are never lost.<br /> <br /> ==Instructor Information==<br /> Agatha Lenartowicz can be reached at [mailto:alenarto@g.ucla.edu alenarto@g.ucla.edu].<br /> Cameron Rodriguez can be reached at [mailto:cdrodriguez@g.ucla.edu cdrodriguez@g.ucla.edu].<br /> Office hours will be after class on Mondays and Wednesdays in room 17-369 of the NPI.<br /> <br /> ==Organizational notes==<br /> When sending mail about the course, please include the characters: '''NITP''' in the subject line somewhere, as that helps a great deal in file management. Thanks.<br /> <br /> While most of the classes will be in lecture format, there will also be lab work in computing, electronics and image collection. It may be necessary to schedule these outside of standard class hours to accommodate the availability of the equipment we need.<br /> <br /> ==Class List sign up==<br /> As soon as possible, please add yourself to the list of students in the class.<br /> [http://ccn.ucla.edu/mailman/listinfo/neuroimaging Class List].<br /> <br /> Please also send an email directly to [mailto:cdrodriguez@g.ucla.edu Agatha Lenartowicz] with your name, your best contact email and a subject line of &quot;'''M284 class signup - NITP'''&quot;<br /> <br /> ==Auditing==<br /> Auditing means ''taking the course'', though not for credit. Auditors are expected to attend all lectures, participate in discussions, and do ''all'' problem sets and tests. For my part, I will read an score the materials. If you do not intend to actually do the classwork, auditing is actively discouraged. If you miss several assignments you will be asked to drop the course.<br /> <br /> =Catalog Course Description=<br /> Factors common to neuroimaging in multiple modalities including: Physiological Contrast mechanisms and Biophysics; Signal and Image processing, including transform approaches, Statistical Modeling and Inference, Time-Series Statistics, Detection Theory, Contrast Agents, Experimental Design, Modeling and Inference, Electrical Detection methods, Electroencephalography, Optical Methods, Microscopy.<br /> <br /> =Pre-Requisites=<br /> Functional Neuroanatomy (M292) and competence in 1) Integral calculus 2) Statistics 3) Electricity and Magnetism and 4) Computer Programming (any language). Waiver of some requirements may be possible by consent of the instructor.<br /> <br /> The following are examples of the level of knowledge expected on entry. If you do not have this background please let Mark know as soon as possible. We will do our best to remediate any missing knowledge.<br /> <br /> '''In week 1 of class please ensure you fill out this online quiz to assess your skills, both for our and your benefit [[https://docs.google.com/forms/d/e/1FAIpQLSftU5YiXipM64fGcAM-guvfUBsT60r-jzISWc-6smBiTfUd9g/viewform]]'''<br /> <br /> ==Stats==<br /> A general philosophy of the course and of the NITP is that a sophisticated consumer of images uses these data as a test of a hypothesis. You will learn more about the instructor's feelings about truth by ''p''-values, but it is important to have a good intuitive understanding of random processes, noise, reliability, estimation, ''etc...'' For this reason, stats comfort is a must.&lt;br&gt;<br /> ----<br /> Here are a few questions that you should be easily able to find the answers to:&lt;br&gt;<br /> Given a sample of student heights at UCLA in inches:&lt;br&gt;<br /> : H(&quot;males&quot;) = [74, 71, 67, 69, 71, 70, 65, 67, 71, 68, 69, 66], and&lt;br&gt;<br /> : H(&quot;females&quot;) = [62, 66, 68, 62, 65, 62, 63, 64]&lt;br&gt;&lt;ul&gt;<br /> &lt;li&gt;What is the modal height of the males?&lt;/li&gt;<br /> &lt;li&gt;What is the difference in mean height between males and females?&lt;/li&gt;<br /> &lt;li&gt;Which of the following ''should'' be used to test if the average height of UCLA males and females differ significantly at &quot;p&quot;&lt;0.01?<br /> &lt;ol&gt;<br /> &lt;li&gt; Increase the number of females in the sample be eight, then perform a ''t''-test on the means&lt;/li&gt;<br /> &lt;li&gt; Continue collecting more data until the probability of a two-tailed ''t''-test statistic comparing males and females is less than 0.01.&lt;/li&gt;<br /> &lt;li&gt; Collect the heights of &quot;all&quot; males and females at UCLA and then calculate the ''t''--statistic to determine if the heights differ at the assigned probability level&lt;/li&gt;<br /> &lt;li&gt; Collect height data from an age-matched sample in the surrounding community.&lt;/li&gt;<br /> &lt;li&gt; Add to the sample until there are exactly 100 males and 100 females, and calculate if the heights differ by more than 1%.&lt;/li&gt;<br /> &lt;li&gt; None of the above.&lt;/li&gt;<br /> &lt;li&gt; All of the above&lt;/li&gt;<br /> &lt;/ol&gt;<br /> &lt;/ul&gt;<br /> <br /> ==Programming==<br /> Formally, students are required to have a background in at least some programming language. The fact of the matter is that Neuroimaging is computationally intensive; programming is a basic skill for this work. I intend to prepare problem sets that will require programming to solve.&lt;br&gt;<br /> This year, all of our programming will be done using MATLAB, purchase of which is a course requirement. The [http://i2w3.ais.ucla.edu/asucla/store.aspx?pg=macsoftware.pdf ASUCLA student store] has the licenses for students at an incredibly discounted price of $99. You will not regret owning this.<br /> <br /> ''Many'' MATLAB tutorials can be found online. Here is a good<br /> [http://www.mathworks.com/academia/student_center/tutorials/register.html interactive beginner tutorial] from MathWorks. It takes about 2 hours and you must register with MathWorks beforehand, but it covers many aspects of MATLAB in depth (e.g. the workspace, importing data, visualizing data, scripts, functions, &amp; loops).<br /> <br /> Another useful option is the ''demo'' feature that can be accessed within MATLAB by typing 'demo' at the command prompt.<br /> <br /> &gt;&gt; demo<br /> <br /> This will open a help window of all available demos. Here are a few demos I recommend (kmc):<br /> *Importing Data from Files<br /> *Using Basic Plotting Functions<br /> *Working with Arrays<br /> *Manipulating Multidimensional Arrays<br /> <br /> [[Matlab Tutorial]]<br /> <br /> == Mathematics ==<br /> Can you solve for y or &lt;math&gt;\mathbf{Y}&lt;/math&gt; in these equations?&lt;br&gt;<br /> &lt;math&gt;y = \frac{d(e^x)}{dx}&lt;/math&gt;&lt;br&gt;<br /> &lt;math&gt;y = \int\sin x\,dx&lt;/math&gt;&lt;br&gt;<br /> <br /> &lt;math&gt;\mathbf{Y}=\left[\begin{array}{cc}<br /> 2 &amp; 4\\<br /> 5 &amp; 7\end{array}\right]^{-1}&lt;/math&gt;&lt;br&gt;<br /> If &lt;math&gt;y = 3x^2 + 6x + 2&lt;/math&gt;, what is &lt;math&gt;\frac{d(e^x)}{dx}&lt;/math&gt;?<br /> &lt;br&gt;<br /> If not, please let me know, and we will try to remedy things. In the meantime, there are a number of excellent online math tutorials. For matrices, may I suggest:<br /> &lt;ul&gt;<br /> &lt;li&gt;[http://www.math.hmc.edu/calculus/tutorials/ Harvey Mudd mathematics online tutorial]<br /> &lt;li&gt;[http://www.sosmath.com/matrix/matrix.html S.O.S. Mathematics]&lt;/li&gt;<br /> &lt;li&gt;[http://www.mathworks.com/access/helpdesk/help/techdoc/matlab.html MATLAB online tutorial]&lt;/li&gt;<br /> &lt;li&gt;[ftp://joshua.smcvt.edu/pub/hefferon/book/book.pdf Programmed text in Linear Algebra - ''Hefferon'']<br /> &lt;/ul&gt;<br /> These are all excellent free sources. Please feel free to suggest more.<br /> <br /> ==Functional Neuroanatomy==<br /> Again, there are many excellent resources online! Including:<br /> &lt;ul&gt;<br /> &lt;li&gt;[http://www9.biostr.washington.edu/da.html Washington Interactive Atlas]&lt;/li&gt;<br /> &lt;li&gt;[http://thebrain.mcgill.ca/ McGill Brain Tutorial]&lt;/li&gt;<br /> &lt;li&gt;[http://www.anatomie-amsterdam.nl/sub_sites/anatomie-zenuwwerking/123_neuro/start.htm Amsterdam Brain Atlas]&lt;/li&gt;<br /> &lt;/ul&gt;<br /> <br /> =Concepts and Teaching Plan=<br /> We will start looking at a few papers that use ''images of various kinds to address neuroscientific questions.'' Here, you should be paying especial attention to how the images are used in a theoretical context. Did the investigator pose the question first then collect the data? What is the role of ''a posteriori'' interpretation (reverse inference)? What is assumed about the ground truth of the phenomena exposed by neuroimaging?<br /> <br /> After this, we will begin to look at the properties of neurons that might make them visible to our neuroimaging tools. We will consider signaling in neurons, its energetic costs, and the changes in the cellular milieu that are associated. We will begin to consider the optical properties of neurons and their size scale, and the chemical changes that are associated with neuroal activity. As best possible, I will try to incorporate neurogenetics here to consider cell identification and labeling.<br /> <br /> At the same time, we will start the ''practical work in MATLAB.'' If you are already MATLAB proficient, consider your assignment to include bringing the rest of the class up to speed as quickly as possible so that we can move on. As noted above, MATLAB will be used for our quantitative examples, but it is also a strong standard for image and numerical analysis in the sciences and a relatively easy programming language to use, with a pretty quick startup.<br /> <br /> We will start also, on developing the mathematical tools we will need to carry forward. In the digital age, we are dealing always with very large numbers of data points and are forced to deal with large sample sizes (at the very least, a large number of pixels) and we need means of quantitative summary. Our initial steps will be in very ''basic statistical concepts'' in anticipation of doing more and deeper work later.<br /> <br /> This will be followed by work on ''analytic math'', building to ''transform theory.'' Depending on what I find out about your skills level in maths, we may start with some calculus review, or we may have to schedule one-on-one meetings to balance everyone’s background. The goal here is to develop a framework with which to understand what happens to the ground truth data we try to observe as it is filtered through our imaging tools. There are very powerful mathematical tools that can be applied here, particularly the field known as linear systems analysis that considers ''transfer functions'' and especially ''convolution.'' Each device we build or use can be analyzed, at least in part, within this framework. More importantly, for many classes of systems, the filtering they apply can be inverted – in some cases unblurring and recapturing much of the original data. ''Deconvolution'' is the general rubrick under which we will try to analyze this process.<br /> <br /> Mathematical transforms are, in general, ways to change the representation of equations into forms that are much easier to solve, or that offer additional insight into the underlying properties. We will look at a few transforms, particularly the ''LaPlace Transform'' and the ''Fourier Transform.'' The latter is simply a means of expressing and quantifying the frequencies contained in a signal. The maths for these includes a little bit of trigonometry and some basic calculus. By the time we start on these topics, you should make yourself responsible for knowing how to integrate sines and cosines, and reviewing properties of the natural logarithm, e. I will introduce, in class, the concepts and algebra of imaginary numbers, which we will need as well.<br /> <br /> [[File:Fourier-cat.jpg | x250px]]<br /> <br /> The essential results of the Fourier transform find their way into literally every means we have of neuroimaging, the statistical processing of images, concepts of noise and a host of other applications in neuroscience. I truly believe, that although you may find this material difficult, you will be happy about knowing it for the rest of your career as a scientist, making it well worth the effort.<br /> <br /> Our first direct application of the analytic tools will be in the analysis and then creation of electrical circuits. We do this for several reasons. Unlike many real-world devices, electrical circuit elements: resistors, batteries, capacitors, inductors and operational amplifiers, act very much like their idealized representations, storing and converting energy in very predictable ways. The tools that have grown to analyze such circuit elements are very mature and quite powerful, making prediction of their behavior straightforward. For this reason, many real-world physics and imaging problems are ''modeled'' using electrical circuit elements where we can predict their input-output properties.<br /> <br /> The second reason for looking at electrical circuits is that they are present in more or less every lab instrument you are likely to use. Towards the end of the first quarter, we will build, in class, an EEG system based on your understanding of these devices. This will also give us an entrée into the important study of noise, which is present in any experiments. We will look at the many sources of noise in neuroimaging and experiments, and consider ways in which modeling the noise can help us to reduce it. Conversely, we will discuss ways in which we can study the characteristics of the noise in order to better understand either our devices, or the actual features of our images.<br /> <br /> We will cover principles of optics, emphasizing the issues of resolution, optical spectrum (frequency ranges), distortion and digital imaging. One way to think about the effects of lenses is as convolution filters (''see above'') that ''color'' the signal. Color, as used here, is a rather broad concept. The process of ''whitening'' the signal can be considered a deconvolution. Undoing the lens convolution is a way of removing the blur or distortion produced by a lens. As we go on, we will see this theme of convolution blurring and deconvolution sharpening applied to the many modalities used in modern neuroimaging. Similarly, statistical variance or noise can be reduced or at least better understood in this context, sharpening our statistical inferences and improving detection power.<br /> <br /> Our next foray will be into electroencephalography (EEG), which is a simply a measure of the differences in electrical voltage from point to point on the scalp or brain. In addition to looking at the biological basis of the EEG, we will build and test an EEG system in class and we will look at some software approaches to interpreting the EEG both as spatially-resolved (''i.e., image'') data and as cognitive/physiological signals.<br /> <br /> =Grading=<br /> Your final grades will be determined by the problem sets, the midterm and final and by your class participation. Generally the rubrick is:<br /> :*Participation 10%<br /> :*Problem Sets 25%<br /> :*Midterm 30%<br /> :*Final 35%.<br /> As you can see, your participation in the class is of major import, as I believe that everyone learns from other people's questions and comments.<br /> <br /> As M284 is a required course for some students continuation in several Ph.D. programs, grading will necessarily be rigorous.</div>

Alenartowicz