Principles of Neuroimaging A - 2014
Principles of Neuroimaging A, Fall, 2014 - Class Schedule and Syllabus
- 1 Neuroimaging journal Club (required for NITP certificate)
- 2 This schedule will change!
- 3 Lecture Videos
- 4 Course Reading
- 5 Week 1: Orientation to Neuroimaging, Neurons, Brains; Linear Systems
- 6 Week 2: Math & Circuits I
- 7 Week 3 Circuits and Noise
- 8 Week 4: Circuits III & Optics
- 9 Week 5: Optics II & Finding Data in the Noise
- 10 Week 6: Mid-term and Stats begins!
- 11 Week 7:Stats and more stats
- 12 Week 8: E-Phys I
- 13 Week 13: MRI
Neuroimaging journal Club (required for NITP certificate)
This schedule will change!
- Coming up next!: M284B Principles of Neuroimaging B
- Hopefully, we can start recording the class sessions for those who cannot make a few lectures.
- Signal Processing for Neuroscientists by Wim van Drongelen
- This can be found as a PDF on scribd.com, for a small fee of $8.99
- Matlab for Neuroscientists
- Link for download found here for a small fee: http://www.scribd.com/doc/88212458/Matlab-Matlab-for-Neuroscientists
- Cartoon Guide to Statistics
- Link for download found here for a small fee: http://www.scribd.com/doc/148072668/Cartoon-Guide-to-Statistics
- NOTE: if you subscribe for a Scribd account for a day, you can download as many documents as you like for one fee.
Week 1: Orientation to Neuroimaging, Neurons, Brains; Linear Systems
- 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.
Required Readings - Please complete these readings prior to class.
Suggested Further Reading
- This paper, by Malhi, is a nice orientation in methods of neuroimaging. *Making sense of neuroimaging in psychiatry
Transforms and the Convolution Theorem. Speaker: Mark Cohen
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 I wrote on imaginary numbers that might be a helpful review.
There is a nice Wikibook on Calculus.
Suggested Further Reading
- Introduction to matlab
Slides shown in 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.
Week 2: Math & Circuits I
This problem set refers largely to last week's contents: Problem Set 1. Due by email 10/20/14. Please remember that the preferred way for us to receive problem sets is via email to Mark and to John Dell'Itlalia .
- Circuits I. Mark Cohen
We will continue with the linear systems lecture and move to 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...)
- You may or may not find this comprehensible without chapters 5 through 9.
We will discuss:
- Passive Circuit Elements: Resistors, Capacitors, Inductors
- Active Elements
- - Amplifiers
- - Transistors
- - Op Amps
- Solutions with Matrices
Suggested Further Reading
Suggested, Optional Readings from DSPguide.com:
- Note: These chapters are light on math and try to focus on a conceptual understanding
Time and Frequency / Spectral Filters
Practice using the Fourier transform:
- Circuits II. Mark Cohen
Week 3 Circuits and Noise
Monday 10/20/14 - Circuits (cont'd)
Circuits. Cameron Rodriguez
Cameron will continue with the basic circuits material, moving on to demos and real-world active circuits. This will continue with the previously uploaded slide set.
Wednesday 10/22/14 - Noise-
- Noise. Mark Cohen
It is what you don't want - usually - but things change in quantized systems
- Additive noise
- White Noise
- Boltzmann noise
- Colored Noise
- Gaussian Noise
- Coherent noise
- Sampling Errors
- 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.
- van Drongelen: Chapters 2 through 4
Week 4: Circuits III & Optics
Class Project Design
OUCH! Class canceled for personal reasons
My sincere apologies
Week 5: Optics II & Finding Data in the Noise
In class EEG project build
In class EEG project build and demo
Week 6: Mid-term and Stats begins!
Sick day for the instructor
Mid-term distributed by email.
- 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.
- 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
- 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
Week 7:Stats and more stats
- Statistical Fundamentals II. Speaker: Catherine Sugar
- Statistical Fundamentals III. Speaker: Catherine Sugar
Week 8: E-Phys I
EEG in Neuroscience I. "Speaker": Agatha Lenartowicz
Thanksgiving break, no class!!!
Week 13: MRI
Monday 12/7/14 - MRI
-MRI I Speaker: Cohen
Magnetic Resonance Imaging (MRI) is probably the most influential and most felxible 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
OUTLINE Required Readings
- MRI Slides
- These notes by Joseph Hornak are highly professional and complete coverage of MRI.
- eMRI is another excellent online MRI learning resource
- Erwin Hahn - Spin Echoes: Essential reading for the MRI community
Suggested Further Reading