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(New page: =Class will meet Mondays and Wednesdays at 2pm in Engineering V room 5101 [http://www.ccn.ucla.edu/wiki/index.php/Image:EngineeringV.png Map]= =Course Schedule & Syllabus= [[Class Schedule...)
 
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=Class will meet Mondays and Wednesdays at 2pm in Engineering V room 5101 [http://www.ccn.ucla.edu/wiki/index.php/Image:EngineeringV.png Map]=
Principles of Neuroimaging A, Fall, 2010 - Class Schedule and Syllabus
=Course Schedule & Syllabus=
[[Class Schedule |
==Link to schedule==
]]
* [[Class_Schedule_2010 |  '''Course Schedule & Syllabus M284A (link)''' ]]
* [[Principles_of_Neuroimaging_B_-_2011 |  '''Course Schedule & Syllabus M284B (link)''' ]]
=General Information=
==Course Goals==
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. My philosophy on this, is that 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.
===Teaching Philosophy===
At the graduate level, the courses are not about grades, but about learning at a professional level. I do not emphasize exams and papers except for 1) the institutional requirement that I have a means for evaluation and 2) because preparing for these tends to force one to think and consolidate information. 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, [http://www.research-service.com/custom-research-paper.html research paper] and to place their work in the broadest context possible.


Because the emphasis is on skills learning, as much as on content, I 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.
:'''[[Principles_of_Neuroimaging_-_2010-2011 | Back to main course page for Principles of Neuroimaging]]'''


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.
:'''[[Principles_of_Neuroimaging_B_-_2011 | M284B Principles of Neuroimaging B]]'''


I would like to 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.
=Lecture Videos=
*[[Media:Monday_10_18_10_-_Statistics_for_Imaging_I._Speaker_Catherine_Sugar.mp4|Monday 10-18-10 - Statistics for Imaging I. Speaker Catherine Sugar]]
*[[Media:Wednesday_10_20_10_-_Statistics_for_Imaging_II._Speaker_Catherine_Sugar.mp4|Wednesday 10-20-10 - Statistics for Imaging II. Speaker Catherine Sugar]]
*[[Media:Wednesday_10_27_10_-_Optics_II._Speaker_Zachary_Taylor.mp4|Wednesday 10-27-10 - Optics II. Speaker Zachary Taylor]]
*[[Media:Wednesday_11_03_10_-_Noise_Speaker_Mark_Cohen.mp4|Wednesday 11-03-10 - Noise Speaker Mark Cohen]]
*[[Media:Monday_11_8_10_-_Wide_field_Optical_imaging._Speaker__Nader_Pouratian.mp4|Monday 11-8-10 - Wide field Optical imaging. Speaker  Nader Pouratian]]
*[[Media:Wednesday_11_10_10_-_Circuits_I._Mark_Cohen.mp4|Wednesday 11-10-10 - Circuits I. Mark Cohen]]
*[[Media:Wednesday_11_17_10_-_Human_Electrophysiology_Speaker_John_Stern.mp4|Wednesday 11-17-10 - Human Electrophysiology Speaker John Stern]]
*[[Media:Monday_11_22_10_-_Design_of_an_EMG_Preamp._Speaker_Mark_Cohen.mp4|Monday 11-22-10 - Design of an EMG Preamp. Speaker Mark Cohen]]
*[[Media:Wednesday_12_1_10_-_review_Speaker_Mark_Cohen.mp4|Wednesday 12-1-10 - review Speaker Mark Cohen]]


===Required Text===
=Week 1: Orientation to Neuroimaging, Neurons, Brains=
This year, we will be 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.
==''Monday 9/27/10'' - Orientation & Neurons. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]==
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.
[[Image:Neurons.jpg|right]]
''Required Readings''
:*[http://www.ccn.ucla.edu/wiki/images/8/81/The_Active_Brain.pdf The Active Brain]
:*[[media:NeuronFunction+AnatomyNITP.pdf‎| Neuron function slides shown in class]]
:*[http://ccn.ucla.edu/wiki/images/5/5a/CAVEAT_LECTOR.pdf Caveat Lector - the misuse of neuroimaging]
''Suggested Further Reading''
:*[http://www.brainmapping.org/NITP/PNA/Readings/Protected/Kosslyn1999.pdf "If Neuroimaging is the Answer, What is the Question?" Kosslyn, 1999]
:*[http://da.biostr.washington.edu:80/cgi-bin/DA/PageMaster?atlas:NeuroSyllabus+ffpathIndex/Splash^Page^Syllabus+2 Neuroanatomy Programmed Learning]
:*[http://www.amazon.com/Fundamental-Neuroscience-Second-Larry-Squire/dp/0126603030 Squire, Fundamentals of Neuroscience]
:*[http://www.amazon.com/Principles-Neural-Science-Eric-Kandel/dp/0838577016 Kandel, et al., "Principles of Neural Science"]
: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]
:*[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1359308/pdf/jphysiol01232-0142.pdf Replacement of the axoplasm of giant nerve fibres with artificial solutions]


===Further Reading===
==''Wednesday 9/29/10'' - The Organization of the Human Brain. ''Speaker'': [http://ccn.ucla.edu/bmcweb/bmc_bios/SusanBookheimer/ Susan Bookheimer]==
There are many links to reading materials on the [[Class Schedule | syllabus page]]. If they are optional, it will say so.
'''A probe mail was sent this afternoon to all students in the class. If you did not receive this (subject, "A Probing Question"), let [mailto:mscohen@ucla.edu me] know'''
For the statistics sections, I STRONGLY recommend
*[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.
Some other excellent resource texts include:
*[http://www.amazon.com/MATLAB-Behavioral-Scientists-David-Rosenbaum/dp/0805863192/ref=ed_oe_p Matlab for Behavioral Scientists]
*[http://www.amazon.com/Matlab-Neuroscientists-Introduction-Scientific-Computing/dp/0123745519/ref=sr_1_1?ie=UTF8&s=books&qid=1231366863&sr=1-1 Matlab for Neuroscientists]
*[http://www.amazon.com/Understanding-Digital-Signal-Processing-2nd/dp/0131089897/ref=sr_1_1?ie=UTF8&s=books&qid=1231979157&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.
*[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).


===Problem Sets===
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:
Problem sets will occur about once per week. Generally, you will have a week to work on them. Frankly, these are mostly pretty simple in order to learn the mechanics. You will use these skills in the midterm and final.
*Phylogenetic Layering
*Functional Specialization
*Principles Divisions of the Brain
*Brain Systems


Please send these to Mark Cohen (mscohen@ucla.edu) and Kevin McEvoy (kmcevoy@ucla.edu). Problem Sets 4 and 5 go to Ariana Anderson (ariana@stat.ucla.edu). If you do not get a response that your mail has been received, call or otherwise follow up with the instructors.
''Required Readings''
:*[http://da.biostr.washington.edu:80/cgi-bin/DA/PageMaster?atlas:NeuroSyllabus+ffpathIndex/Splash^Page^Syllabus+2 Neuroanatomy Programmed Learning]
:*[[media:NITPanatomy-Bookheimer.pdf | Slides shown in Class]]
''Suggested Further Reading''


==Instructor Information==
[[media:PNIA2010-PS1.pdf|'''Problem Set 1 Neuroanatomy. Due in class 10/6.''']] Please remember that the preferred way for us to receive problem sets is ''via email'' to [mailto:mscohen@ucla.edu Mark] and to [mailto:alheadbme@ucla.edu Austin].
Mark Cohen can be reached at [mailto:mscohen@ucla.edu mscohen@ucla.edu]. Telephone is 310-980-7453.
----
Office hours will be after class on Mondays and Wednesdays in room 17-369 of the NPI.
We will be studying linear systems next week. This coming week until Monday would be a good time to review your calculus fundamentals:
:''Derivatives of Polynomials''
:''Integrals of polynomials''
:''Basic trig + derivatives and integrals of sine and cosine functions''
 
When we start on the linear systems section, we will be using these fundamentals to develop the LaPlace and Fourier transforms, which involve the use of imaginary numbers. The math content for that section is largely contained in this link: [[Media: MathematicalTools.pdf | Mathematical Tools]].
 
Please let me know by email or other means if this material looks too difficult.
 
You will need to have matlab installed and running to do the next problem set.
 
=Week 2: Linear Systems=
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:
:Modeling of Neural Systems
:Extraction of Signal from Noise
:Design of Circuits
:Image Enhancement
:Understanding of Image artifacts, and others.
 
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.
 
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.
 
Here is [http://www.brainmapping.org/NITP/PNA/Readings/ImaginaryNumbers.pdf A primer on imaginary numbers] that might be a helpful review.
 
==''Monday 10/4/10'' - Transforms and the Convolution Theorem. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]==
 
''Required Readings''
:*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapter 1
:*[[Media: Mathematical_tools.pdf|Mathematical Tools]] - updated 10/4/10 after class


Our TA is Kevin McEvoy ([mailto:kmcevoy@ucla.edu kmcevoy@ucla.edu]). Kevin's office hours are Wednesdays from 1-2pm in the Reed Research Center, room A236. Reed is located across from Ronald Regan Hospital and adjacent to NPI (search for "Reed" on the [http://maps.ucla.edu/campus/ campus map]). Discussion section is held on Mondays from 4:30-5:30pm in NPI 28-181.
''Suggested Further Reading''
:'''Problem Set 2A - Introduction to matlab'''


Ariana Anderson will host sections on statistics (''tbd'')
''Slides shown in class''
:[[media:LinearityM285.pdf|Linearity and the Fourier Transform]] - updated 10/4/10 after class


==Organizational notes==
Please see [http://www.brainmapping.org/NITP/PNA/html/Linearity.html MATLAB linearity demo]
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.


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.
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.


==Class List sign up==
==''Wednesday 10/6/10'' - Fourier Transform Properties. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]==
As soon as possible, please add yourself to the list of students in the class.
[[Image:xkcd_fourier.jpg|right]]
[http://ccn.ucla.edu/mailman/listinfo/neuroimaging Class List]
*Example transform derivations
*The Convolution theorem
*Oddness (and Even-ness)
*The Fourier Shift Theorem
Please see [http://www.brainmapping.org/NITP/PNA/html/ShowConvolutions.html MATLAB demo of Fourier transforms and convolution]


=Catalog Course Description=
Optional Readings:
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.
:*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapters 5 through 9
**Note: This reading may be heavy going. I will not be going into nearly this much detail in class, but your time on this will be very well spent. We will be revisiting this material later in the course in week 5.


=Pre-Requisites=
'''Suggested, Optional Readings from [http://www.dspguide.com DSPguide.com]:'''
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.
:*[http://www.dspguide.com/CH5.PDF Linear Systems]
:*[http://www.dspguide.com/CH6.PDF Convolution]
:*[http://www.dspguide.com/CH8.PDF Discrete Fourier Transform (DFT)]
:''Note: These chapters are lite on math and try to focus on a conceptual understanding''


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.
:'''Problem Set 2B modeling in matlab'''
 
[[Media: ProblemSet3A.pdf|Problem Set 2A]] and
[http://www.brainmapping.org/NITP/PNA/html/TwoDimensions.html Problem Set 2B]


==Stats==
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.<br>
----
----
Here are a few questions that you should be easily able to find the answers to:<br>
Practice using the Fourier transform:
Given a sample of student heights at UCLA in inches:<br>
:[http://www.brainmapping.org/NITP/PNA/ConvFThtml/ConvolutionWorksheet.pdf Fourier transform and Convolution Worksheet]. [http://www.brainmapping.org/NITP/PNA/ConvFThtml/ConvFT.html (''Solutions'').]
: H("males") = [74, 71, 67, 69, 71, 70, 65, 67, 71, 68, 69, 66], and<br>
:[http://www.brainmapping.org/NITP/PNA/ConvFThtml/Something.wav Sound file for worksheet above.]
: H("females") = [62, 66, 68, 62, 65, 62, 63, 64]<br><ul>
----
<li>What is the modal height of the males?</li>
 
<li>What is the difference in mean height between males and females?</li>
I suggest very strongly that you brush up on linear algebra during this week in anticipation of Dr. Sugar's lectures in statistics. In particular, I would like you to have an understanding of :
<li>Which of the following ''should'' be used to test if the average height of UCLA males and females differ significantly at "p"<0.01?
 
<ol>
:''Matrices as solutions to linear equations - determinants and inverses''
<li> Increase the number of females in the sample be eight, then perform a ''t''-test on the means</li>
:''Matrix multiplication''
<li> Continue collecting more data until the probability of a two-tailed ''t''-test statistic comparing males and females is less tan 0.01.</li>
 
<li> Collect the heights of "all" males and females at UCLA and then calculate the ''t''--statistic to determine if the heights differ at the assigned probability level</li>
For these, I can recommend the Hefferon text noted above.
<li> Collect height data from an age-matched sample in the surrounding community.</li>
 
<li> Add to the sample until there are exactly 100 males and 100 females, and calculate if the heights differ by more than 1%.</li>
=Week 3: Noise and Basic Statistics=
<li> None of the above.</li>
==''Monday 10/11/10'' - Noise. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]==
<li> All of the above</li>
It is what you ''don't'' want.
</ol>
:Additive noise
</ul>
:White Noise
:Boltzmann noise
:Colored Noise
:Gaussian Noise
:Coherent noise
:Sampling Errors
:Aliasing
:Quantization noise
:Spectral filtering
 
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.
 
Readings:
:*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapters 2 through 4
 
:*Slides used in Class:
[http://www.brainmapping.org/NITP/PNA/Readings/Noise.pdf Noise Slides]
:'''Problem set 3 - properties of noise'''
 
==''Wednesday 10/13/10'' - Statistical Fundamentals. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]==
We will consider the general problems of statistical inference, with a concentration on developing an intuitive understanding of statistical concepts.
[[Image:MeasureForMeasure.jpg|right]]
 
:*[[media: CohenClassIntroStats10_13_10.pdf | Slides used in class (set 1)]]
 
''Review of'':
:*Descriptive Statistics: mean, mode, variance, standard deviation
:*Statistical Inference. The Binomial and Normal Distribution
:*Basic Tests: t-test, linear correlation
:*Modeling and non-linear relations
:*Bayes rule
 
Suggested reading
:*[http://www.statsoft.com/textbook/stbasic.html Statsoft online text (''free'')]
:*[http://www.amazon.com/Cartoon-Guide-Statistics-Larry-Gonick/dp/0062731025 The Cartoon Guide to Statistics - Gonick $17.95 new]
: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
 
:'''Problem Set 5 - Statistics in matlab'''
::[[media: Problem_Set_1.doc|Problem set using stats and MATLAB]]
::[[media: Problem_Set_1B.doc|More practice with stats and MATLAB]]
 
=Week4: Statistics for Imaging=
==''Monday 10/18/10'' - Statistics for Imaging I. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]==
#Outline
[[image:BVTradeoff.jpg|right]]
''Required Readings''
:*[[media: Mumford_stat_modeling.pdf | Statistical Modeling and Inference (pdf)]]
:*[[media: CohenClassSlides10_18_10.pdf | Slides used in class (set 2)]]
 
:*The General Linear Model
*Linear Algebra applied to Statistical Solutions
:*Analysis of Variance
 
''Suggested Further Reading''
 
==''Wednesday 10/20/10'' - Statistics for Imaging II. ''Speaker'': [http://www.npistat.com/about.asp Catherine Sugar]==
*Fixed and Random Effects
*Repeated measures
:*Bonferroni and Other Corrections
*Non-Parametric Methods
*Autocorrelation
*Unknown Distributions
 
''Required Readings''
:*[[media: CohenClassSlides10_20_10.pdf | Slides used in class (set 3)]]
 
''Suggested Further Reading''
 
=Week 5: Optics=
The prototypical imaging means: Direct visualization. These lectures will cover the principles of light transmission, refraction, reflection and dispersion and will develop a quantitative approach to the analysis of optical systems. We will cover the theory of lenses, imperfections in focus, such as chromatic aberration, and a model of optical devices that builds on our understanding of convolution.
==''Monday 10/25/10'' - Optics I. ''Speaker'': [mailto:zdeis@seas.ucla.edu Zachary Taylor]==
[[Image:Reflection.jpg|right]]
The overall goal of this lecture is to establish that:
''- Physical constants have tangible meanings''
''- Plane waves form a physically unrealizable but extremely good approximation to real systems''
''- Boundaries bend light''
''- Physical constants, plane wave mechanics, and boundaries can be used to describe the operation of a lens''
''- The PSF gives a good indication of the overall performance of an imaging system''
''- All of these concepts have analogues in other areas of engineering (ie circuits, mechanical vibrations, etc.)''''
 
'''Outline:'''
:* Constitutive parameters (ε, μ, η, n, etc.)
:* Plane wave basics
:* Plane waves at boundaries
:* Lenses
:* Advanced imaging properties of lenses
:* Point spread function.
 
''Required Readings''
Zach has very kindly agreed to post his [http://www.brainmapping.org/NITP/PNA/Readings/OpticsTaylor3-10-10.pdf Optics lecture notes].
''Suggested Further Reading''
 
==''Wednesday 10/27/10'' - Optics II. ''Speaker'': [mailto:zdeis@seas.ucla.edu Zachary Taylor]==
 
''Required Readings''
 
''Suggested Further Reading''
 
 
=Week 6: Optical Neuroimaging=
== MIDTERM POSTED ==
:Click [[media:MidTermFall2010.pdf‎ | here for the Midterm. Due in class Mon. 11/8]]
 
==''Monday 11/1/10'' - Optical Applications. ''Speaker'': tbd==
 
''Required Readings''
 
''Suggested Further Reading''
 
 
==''Wednesday 11/3/10'' - Optical and flourescence methods in dynamic neural systems. ''Speaker'': [mailto:kmcevoy@ucla.edu Kevin McEvoy]==
#Outline
''Required Readings''
:*[http://ccn.ucla.edu/wiki/images/d/d3/NeuroimagingCellularLevel_KMcEvoy_2010.pdf Lecture Slides]
 
=Week 7: Optical Intrinsic Imaging, Beginning Circuits=
==''Monday 11/8/10'' - Wide field Optical imaging. ''Speaker'': [http://www.uclahealth.org/body.cfm?xyzpdqabc=0&id=479&action=detail&ref=95328 Nader Pouratian]==
 
''Required Readings''
 
''Suggested Further Reading''
 
 
==''Wednesday 11/10/10'' - Circuits I. [http://www.brainmapping.org/MarkCohen Mark Cohen]==
Why circuits?
:(Virtually) Every device you use in your research is electronic. You access your primary data only indirectly
:The device you ''really'' want in your lab doesn't exist. You very well may have to make it.
: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).
 
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&client=safari&rls=en-us&ei=uVSPSfaxE5nMsAPf-tmSCQ&resnum=1&q=art+of+electronics&um=1&ie=UTF-8&cid=8820839049329255765#ps-sellers "Horowitz and Hill: ''The Art of Electronics.''"] 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...)
 
I found a nice [http://video.google.com/videoplay?docid=5645396659673218353&q=Physics+for+Future+Presidents+Electricity&total=5&start=0&num=10&so=0&type=search&plindex=0#0h20m30s intro lecture on charge, current and voltage].
 
Readings:
:*[[Media: Circuits.pdf|Circuits 1 & 2]]
:*[http://www.brainmapping.org/NITP/PNA/Readings/Circuits.pdf Slides shown in class (''revised 10:30pm 1/28/2010'')]
:*[http://www.elsevier.com/wps/find/bookdescription.cws_home/710026/description#description van Drongelen:] Chapter 2 and 10
**You may or may not find this comprehensible without chapters 5 through 9.
 
We will discuss:
:*Passive Circuit Elements: Resistors, Capacitors, Inductors
:*Gain
:*Transformers
:*Rectifiers
:*Active Elements
::- ''Amplifiers''
::- ''Transistors''
::- ''Op Amps''
:*Solutions with Matrices
''Suggested Further Reading''
 


==Programming==
=Week 8: Electricity and Electronics. Human Electrophysiology=
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.<br>
==''Monday 11/15/10'' - Electricity and Electronics. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]==
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.
[[Image:Opamp.jpg|right]]
*Laplace transform analysis
*Op Amp Circuits
*Active Filters
*Noise Control


''Many'' MATLAB tutorials can be found online. Here is a good
''Required Readings''
[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, & loops).


Another useful option is the ''demo'' feature that can be accessed within MATLAB by typing 'demo' at the command prompt.
''Suggested Further Reading''
Please note that I have the components we used for the class demos available for you to play with at your leisure.


>> demo
==''Wednesday 11/17/10'' - Human Electrophysiology ''Speakers'': [http://greenlab.npih.ucla.edu/ROSTER.html Jonathan Wynn], [http://dgsom.healthsciences.ucla.edu/institution/personnel?personnel_id=9140 John Stern]==
''Evoked Responses'' - Guest Lecturer: [http://greenlab.npih.ucla.edu/ROSTER.html Jonathan Wynn]
*A look at real EEG data
*Preprocessing:
**filtering
**artifact detection/removal
*averaging
*single events
*interpretation


This will open a help window of all available demos. Here are a few demos I recommend (kmc):
''Clinical EEG'' - Guest Lecturer: [http://dgsom.healthsciences.ucla.edu/institution/personnel?personnel_id=9140 John Stern]
*Importing Data from Files
*Normal and Abnormal EEG
*Using Basic Plotting Functions
*EEG as a marker for brain state
*Working with Arrays
**sleep staging
*Manipulating Multidimensional Arrays
**alpha and relaxation
*Neurofeedback???


== Mathematics ==
=Week 9: Practical Electronic Circuits=
Can you solve for y or <math>\mathbf{Y}</math> in these equations?<br>
This week we will design, build and test a practical device for recording of human electrical potentials: The electromyogram, or EMG. This device must manage the many challenges of interfacing with small biological signals: Sensitivity, Gain, Noise, Linearity, Filtering. The recording we (''hopefully'') will make will demonstrate issues of linearity and neural coding.
<math>y = \frac{d(e^x)}{dx}</math><br>
<math>y = \int\sin x\,dx</math><br>


<math>\mathbf{Y}=\left[\begin{array}{cc}
==''Monday 11/22/10'' - Design of an EMG Preamp. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]==
2 & 4\\
#Outline
5 & 7\end{array}\right]^{-1}</math><br>
If <math>y = 3x^2 + 6x + 2</math>, what is <math>\frac{d(e^x)}{dx}</math>?
<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:
<ul>
<li>[http://www.math.hmc.edu/calculus/tutorials/ Harvey Mudd mathematics online tutorial]
<li>[http://www.sosmath.com/matrix/matrix.html S.O.S. Mathematics]</li>
<li>[http://www.research-service.com/custom-essay-writing.html Custom essays]</li>
<li>[http://www.mathworks.com/access/helpdesk/help/techdoc/matlab.html MATLAB online tutorial]</li>
<li>[ftp://joshua.smcvt.edu/pub/hefferon/book/book.pdf Programmed text in Linear Algebra - ''Hefferon'']
<li>[http://www.professays.com/ custom essay writing]</li>
<li>[http://www.hqessays.com/ custom essay writing service]</li>
</ul>
These are all excellent free sources. Please feel free to suggest more.


==Functional Neuroanatomy==
''Required Readings''
Stand by. We hope to have a student-run functional neuroanatomy study group.
[http://claimid.com/chris251984 Chris Harris]


=Class Meetings=
''Suggested Further Reading''
Class will meet from 2 to 4 pm on Mondays and Wednesdays. Our classroom will be Room 5101 9n Engineering V.


=Concepts and Teaching Plan=
:'''Problem Set on circuits''' - Due Monday 11/30
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?
::[[media: Circuits_Problem_Set.pdf|Circuits problem set]]


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.
==''Wednesday 11/24/10'' - Building and Using Electronic Devices: ''EMG''. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]==
#Outline


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.
''Required Readings''


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.
''Suggested Further Reading''


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.


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.
=Week 10: Filters=
==''Monday 12/1/10'' - Circuits, cont'd. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]==


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.
==''Wednesdat 12/3/10'' - Autocorrelation, Filters and Color/Course review. ''Speaker'': [http://www.brainmapping.org/MarkCohen Mark Cohen]==


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.
''Required Readings''
Most of what we will look at today is in chapter 7 & 8 of Van Drongelen.


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.
''Suggested Further Reading''


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.


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.
==''Monday 12/8/10'' - '''Final distributed electronically'''

Latest revision as of 03:46, 16 January 2014

Principles of Neuroimaging A, Fall, 2010 - Class Schedule and Syllabus

Back to main course page for Principles of Neuroimaging
M284B Principles of Neuroimaging B

Lecture Videos

Week 1: Orientation to Neuroimaging, Neurons, Brains

Monday 9/27/10 - Orientation & Neurons. Speaker: Mark Cohen

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.

File:Neurons.jpg

Required Readings

Suggested Further Reading

This paper, by Malhi, is a nice orientation in methods of neuroimaging. *Making sense of neuroimaging in psychiatry

Wednesday 9/29/10 - The Organization of the Human Brain. Speaker: Susan Bookheimer

A probe mail was sent this afternoon to all students in the class. If you did not receive this (subject, "A Probing Question"), let me know

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:

  • Phylogenetic Layering
  • Functional Specialization
  • Principles Divisions of the Brain
  • Brain Systems

Required Readings

Suggested Further Reading

Problem Set 1 Neuroanatomy. Due in class 10/6. Please remember that the preferred way for us to receive problem sets is via email to Mark and to Austin.

We will be studying linear systems next week. This coming week until Monday would be a good time to review your calculus fundamentals:

Derivatives of Polynomials
Integrals of polynomials
Basic trig + derivatives and integrals of sine and cosine functions

When we start on the linear systems section, we will be using these fundamentals to develop the LaPlace and Fourier transforms, which involve the use of imaginary numbers. The math content for that section is largely contained in this link: Mathematical Tools.

Please let me know by email or other means if this material looks too difficult.

You will need to have matlab installed and running to do the next problem set.

Week 2: Linear Systems

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:

Modeling of Neural Systems
Extraction of Signal from Noise
Design of Circuits
Image Enhancement
Understanding of Image artifacts, and others.

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.

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.

Here is A primer on imaginary numbers that might be a helpful review.

Monday 10/4/10 - Transforms and the Convolution Theorem. Speaker: Mark Cohen

Required Readings

Suggested Further Reading

Problem Set 2A - Introduction to matlab

Slides shown in class

Linearity and the Fourier Transform - updated 10/4/10 after class

Please see MATLAB linearity demo

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.

Wednesday 10/6/10 - Fourier Transform Properties. Speaker: Mark Cohen

File:Xkcd fourier.jpg
  • Example transform derivations
  • The Convolution theorem
  • Oddness (and Even-ness)
  • The Fourier Shift Theorem

Please see MATLAB demo of Fourier transforms and convolution

Optional Readings:

    • Note: This reading may be heavy going. I will not be going into nearly this much detail in class, but your time on this will be very well spent. We will be revisiting this material later in the course in week 5.

Suggested, Optional Readings from DSPguide.com:

Note: These chapters are lite on math and try to focus on a conceptual understanding
Problem Set 2B modeling in matlab

Problem Set 2A and Problem Set 2B

Practice using the Fourier transform:

Fourier transform and Convolution Worksheet. (Solutions).
Sound file for worksheet above.

I suggest very strongly that you brush up on linear algebra during this week in anticipation of Dr. Sugar's lectures in statistics. In particular, I would like you to have an understanding of :

Matrices as solutions to linear equations - determinants and inverses
Matrix multiplication

For these, I can recommend the Hefferon text noted above.

Week 3: Noise and Basic Statistics

Monday 10/11/10 - Noise. Speaker: Mark Cohen

It is what you don't want.

Additive noise
White Noise
Boltzmann noise
Colored Noise
Gaussian Noise
Coherent noise
Sampling Errors
Aliasing
Quantization noise
Spectral filtering

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.

Readings:

  • Slides used in Class:
Noise Slides
Problem set 3 - properties of noise

Wednesday 10/13/10 - Statistical Fundamentals. Speaker: Catherine Sugar

We will consider the general problems of statistical inference, with a concentration on developing an intuitive understanding of statistical concepts.

File:MeasureForMeasure.jpg

Review of:

  • Descriptive Statistics: mean, mode, variance, standard deviation
  • Statistical Inference. The Binomial and Normal Distribution
  • Basic Tests: t-test, linear correlation
  • Modeling and non-linear relations
  • Bayes rule

Suggested reading

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
Problem Set 5 - Statistics in matlab
Problem set using stats and MATLAB
More practice with stats and MATLAB

Week4: Statistics for Imaging

Monday 10/18/10 - Statistics for Imaging I. Speaker: Catherine Sugar

  1. Outline
File:BVTradeoff.jpg

Required Readings

  • The General Linear Model
  • Linear Algebra applied to Statistical Solutions
  • Analysis of Variance

Suggested Further Reading

Wednesday 10/20/10 - Statistics for Imaging II. Speaker: Catherine Sugar

  • Fixed and Random Effects
  • Repeated measures
  • Bonferroni and Other Corrections
  • Non-Parametric Methods
  • Autocorrelation
  • Unknown Distributions

Required Readings

Suggested Further Reading

Week 5: Optics

The prototypical imaging means: Direct visualization. These lectures will cover the principles of light transmission, refraction, reflection and dispersion and will develop a quantitative approach to the analysis of optical systems. We will cover the theory of lenses, imperfections in focus, such as chromatic aberration, and a model of optical devices that builds on our understanding of convolution.

Monday 10/25/10 - Optics I. Speaker: Zachary Taylor

File:Reflection.jpg

The overall goal of this lecture is to establish that: - Physical constants have tangible meanings - Plane waves form a physically unrealizable but extremely good approximation to real systems - Boundaries bend light - Physical constants, plane wave mechanics, and boundaries can be used to describe the operation of a lens - The PSF gives a good indication of the overall performance of an imaging system - All of these concepts have analogues in other areas of engineering (ie circuits, mechanical vibrations, etc.)''

Outline:

  • Constitutive parameters (ε, μ, η, n, etc.)
  • Plane wave basics
  • Plane waves at boundaries
  • Lenses
  • Advanced imaging properties of lenses
  • Point spread function.

Required Readings Zach has very kindly agreed to post his Optics lecture notes. Suggested Further Reading

Wednesday 10/27/10 - Optics II. Speaker: Zachary Taylor

Required Readings

Suggested Further Reading


Week 6: Optical Neuroimaging

MIDTERM POSTED

Click here for the Midterm. Due in class Mon. 11/8

Monday 11/1/10 - Optical Applications. Speaker: tbd

Required Readings

Suggested Further Reading


Wednesday 11/3/10 - Optical and flourescence methods in dynamic neural systems. Speaker: Kevin McEvoy

  1. Outline

Required Readings

Week 7: Optical Intrinsic Imaging, Beginning Circuits

Monday 11/8/10 - Wide field Optical imaging. Speaker: Nader Pouratian

Required Readings

Suggested Further Reading


Wednesday 11/10/10 - Circuits I. Mark Cohen

Why circuits?

(Virtually) Every device you use in your research is electronic. You access your primary data only indirectly
The device you really want in your lab doesn't exist. You very well may have to make it.
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).

If you have not had any of this background, you might want to have a look at this handout, 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 Online Books: All About Circuits IF you want practical hands-on knowledge about this material, my all-time favorite text is "Horowitz and Hill: The Art of Electronics." 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...)

I found a nice intro lecture on charge, current and voltage.

Readings:

    • You may or may not find this comprehensible without chapters 5 through 9.

We will discuss:

  • Passive Circuit Elements: Resistors, Capacitors, Inductors
  • Gain
  • Transformers
  • Rectifiers
  • Active Elements
- Amplifiers
- Transistors
- Op Amps
  • Solutions with Matrices

Suggested Further Reading


Week 8: Electricity and Electronics. Human Electrophysiology

Monday 11/15/10 - Electricity and Electronics. Speaker: Mark Cohen

File:Opamp.jpg
  • Laplace transform analysis
  • Op Amp Circuits
  • Active Filters
  • Noise Control

Required Readings

Suggested Further Reading Please note that I have the components we used for the class demos available for you to play with at your leisure.

Wednesday 11/17/10 - Human Electrophysiology Speakers: Jonathan Wynn, John Stern

Evoked Responses - Guest Lecturer: Jonathan Wynn

  • A look at real EEG data
  • Preprocessing:
    • filtering
    • artifact detection/removal
  • averaging
  • single events
  • interpretation

Clinical EEG - Guest Lecturer: John Stern

  • Normal and Abnormal EEG
  • EEG as a marker for brain state
    • sleep staging
    • alpha and relaxation
  • Neurofeedback???

Week 9: Practical Electronic Circuits

This week we will design, build and test a practical device for recording of human electrical potentials: The electromyogram, or EMG. This device must manage the many challenges of interfacing with small biological signals: Sensitivity, Gain, Noise, Linearity, Filtering. The recording we (hopefully) will make will demonstrate issues of linearity and neural coding.

Monday 11/22/10 - Design of an EMG Preamp. Speaker: Mark Cohen

  1. Outline

Required Readings

Suggested Further Reading

Problem Set on circuits - Due Monday 11/30
Circuits problem set

Wednesday 11/24/10 - Building and Using Electronic Devices: EMG. Speaker: Mark Cohen

  1. Outline

Required Readings

Suggested Further Reading


Week 10: Filters

Monday 12/1/10 - Circuits, cont'd. Speaker: Mark Cohen

Wednesdat 12/3/10 - Autocorrelation, Filters and Color/Course review. Speaker: Mark Cohen

Required Readings Most of what we will look at today is in chapter 7 & 8 of Van Drongelen.

Suggested Further Reading


==Monday 12/8/10 - Final distributed electronically

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