Principles of Neuroimaging A - 2016

Principles of Neuroimaging A, Fall, 2016 - Class Schedule and Syllabus

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

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.

File:Neurons.jpg

Required Readings - Please complete these readings prior to class.

• The Active Brain
• Neuron function slides shown in class
• Caveat Lector - the misuse of neuroimaging

Suggested Further Reading

• "If Neuroimaging is the Answer, What is the Question?" Kosslyn, 1999
• Neuroanatomy Programmed Learning
• Squire, Fundamentals of Neuroscience
• Kandel, et al., "Principles of Neural Science"

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

• Replacement of the axoplasm of giant nerve fibres with artificial solutions

Week 2: Linear Systems, Convolution, Fourier Transforms

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

• van Drongelen: Chapter 1
• Mathematical Tools

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

We will continue with the linear systems lecture and move to circuits

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:

• Circuits 1 & 2
• Filter Design in 30 Seconds
• van Drongelen: Chapter 2 and 10
• Circuit Lab A Free Circuit Web Base Simulator

• • 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

• Video intro lecture on charge, current and voltage.
• Well organized text on electrical concepts by Tony R. Kuphaldt
• Circuit Theory - Wikibook

Suggested, Optional Readings from DSPguide.com:

• Linear Systems
• Convolution
• Discrete Fourier Transform (DFT)

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.

• Circuits 1 & 2

Other

• Getting Started with Arduino

Week 4 Signal Processing

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:

• van Drongelen: Chapters 2 through 4
• Noise Slides

Week 5 - Information & Statistical Theory

Wednesday 10/28/16

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.

File:MeasureForMeasure.jpg

Suggested reading

• Statsoft online text (free)
• 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 3 - Statistics in matlab

Problem set using stats and MATLAB

More practice with stats and MATLAB

Week 6 - Neurophysiology

Wednesday 11/2/16

Electrophysiology Speaker: Agatha Lenartowicz

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

Week 8 - Practical Neurophysiology

Wednesday 11/16/16

Building an EEG System - Circuit 2 Speaker: Cameron Rodriguez

• Through holesoldering.
• Printed Circuit Board Basics.
• Installing and Setting Up Eagle.
• Using Eagle Schematic.
• Using Eagle Board Layout.

• PCB Layout 1 - According to Pete;
• PCB Layout 2 - According to Pete;

Week 9 - Magnetic Resonance Fundamentals

Wednesday 11/23/16

Thankgiving Wed - No Class

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

above: Figure 1 from Hahn, 1950

Suggested Further Reading

File:PSatSeq.jpg

Required Readings

• MRI Slides