Principles of Neuroimaging A - 2014

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Principles of Neuroimaging A, Fall, 2014 - Class Schedule and Syllabus

Contents

Neuroimaging journal Club (required for NITP certificate)

Contact Katherine Lawrence katherine.E.Lawrence@ucla.edu or Janelle Liu janelle.j.liu@ucla.edu

This schedule will change!

Back to main course page for Principles of Neuroimaging
Coming up next!: M284B Principles of Neuroimaging B

Lecture Videos

Hopefully, we can start recording the class sessions for those who cannot make a few lectures.

Course Reading

Required Reading

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

Supplemental Reading

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

Monday 9/28/15

- 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

Wednesday 10/8/14

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.

Required Readings

Suggested Further Reading

Introduction to matlab

Slides shown in class

Linearity and the Fourier Transform

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.

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 .

Week 2: Math & Circuits I

Monday 10/13/14

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

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...)

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

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:

Fourier transform and Convolution Worksheet.
Sound file for worksheet above.

Wednesday 10/15/14

- Circuits II. Mark Cohen

Other

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
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:

Week 4: Circuits III & Optics

Monday 10/27/14

Class Project Design

Wednesday 10/29/14

OUCH! Class canceled for personal reasons

My sincere apologies

Week 5: Optics II & Finding Data in the Noise

Monday 11/3/14

In class EEG project build

Wednesday 11/5/14

In class EEG project build and demo

Week 6: Mid-term and Stats begins!

Monday 11/10/14

Sick day for the instructor

Mid-term distributed by email.

Wednesday 11/12/14

- 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.

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

Week 7:Stats and more stats

Monday 11/17/14

- Statistical Fundamentals II. Speaker: Catherine Sugar

Wednesday 11/19/14

- Statistical Fundamentals III. Speaker: Catherine Sugar

Week 8: E-Phys I

Monday 11/24/14

EEG in Neuroscience I. "Speaker": Agatha Lenartowicz

Wednesday 11/26/14

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

HahnFig1.png above: Figure 1 from Hahn, 1950

Suggested Further Reading

Required Readings