Principles of Neuroimaging A - 2016

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Principles of Neuroimaging A, Fall, 2016 - 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), Faculty Sponsor: Jamie Feusner (JFeusner@mednet.ucla.edu)

This schedule will change!

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

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 Neuro-imaging, Neurons, Brains

Monday 9/26/16

- Images. Speaker: Mark Cohen

Required Readings - Please complete these readings prior to class.

Wednesday 9/28/16

- Neurons & Signaling. 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

Week 2: Linear Systems, Convolution, Fourier Transforms

Wednesday 10/3/16

- Linear Systems I. Speaker: Mark Cohen

Wednesday 10/5/16

- Linear Systems II. 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.

Week 3: Math & Circuits I

Monday 10/10/16

- Circuits I. Speaker: Cameron Rodriguez

Wednesday 10/12/16

- Circuits II. Speaker: Cameron Rodriguez

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.

Other

Week 4 Signal Processing

Monday 10/17/16

- Signal Processing. Speaker: Cameron Rodriguez


Wednesday 10/19/16

- Noise Speaker: Cameron Rodriguez

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 5 - Information & Statistical Theory

Monday 10/24/16 - in C8-177

Noise Cont' & Information Theory Speakers: Cameron Rodriguez & John Villasenor

Wednesday 10/26/16

Statistical Theory Speaker: Mark Cohen

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

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


MIDTERM

Week 6 - Neurophysiology

Monday 10/31/16

Electrophysiology I Speaker: Agatha Lenartowicz

We will examine our first imaging modality, EEG (and MEG).

Slides for Neurophysiology Lectures

Wednesday 11/2/16

Electrophysiology II Speaker: Agatha Lenartowicz

Continuing with neurophysiology. ERPs & Source Localization.

Week 7 - Neurophysiology

Monday 11/7/16

Electrophysiology III Speaker: Agatha Lenartowicz

Continuing with neurophysiology. Connectivity & Oscillations.

Wednesday 11/9/16

Intracranial Recordings Speaker: Nanthia Suthana

Slides for Intracranial Lecture
Lachaux et al Reading
Harris et al Reading

Week 8 - Practical Neurophysiology

Monday 11/14/16

Building an EEG System - Circuit 1 - Going over circuit Speaker: Cameron Rodriguez

Wednesday 11/16/16

Data Processing Hands On Speaker: Cameron Rodriguez

Week 9 - Data Acquisition

Monday 11/21/16 - in C8-177

Data Acquisition Speaker: Agatha Lenartowicz

Please meet in CCN suite.

Wednesday 11/23/16

Thankgiving Wed - No Class!!

Happy Thanksgiving!

Week 10 - Data Processing

  • Please complete class evaluations via MyUCLA.
  • Also we have an inhouse feedback form regarding the course syllabus: Feedback Form

Your feedback is invaluable to us!!

Monday 11/28/16

Midterm Review, Data Processing Speaker: Agatha Lenartowicz

Hands on practice with EEG data, in Matlab using EEGLAB. Lab materials distributed by email 11/27/16.

Wednesady 11/30/16

Data Processing Speaker: Agatha Lenartowicz

Hands on practice with EEG data, in Matlab using EEGLAB.

Week 11 - Finals Week

Final will be distributed 11/2/16.

Monday 11/5/16

Final Exam