FMRI Course 2005

BIOS642 / BME499 / PSYCH808 : "Introduction to Functional MRI "

The fMRI Laboratory will once again run a two-week course this upcoming summer to introduce interested students and faculty to fMRI. As in the past, the course will cover aspects of the physics of MRI, experimental design, data acquisition, and data analysis, and it will once again include a laboratory component.

The course will run for two consecutive weeks, August 8 to August 19 Each weekday there will be a morning lecture and an afternoon laboratory session.

Here is the syllabus.

Please reply to Eve Gochis if you intend to attend or if you have a
serious conflict with the proposed schedule that prevents your attendance.

We will make every attempt to accomodate everyone who comes to lecture into the labs. However, if space is short in the lab, students who are officially registered will have priority.

Description:

This course will present the basic skills to design and analyze functional magnetic reasonance imaging (fMRI) experiments. At the end of the course a student should be able to design, acquire and analyze a fMRI study. There are four modules of the course: (1) Computer skills, (2) Physics of fMRI, (3) Experimental Design, and (4) Statistics.

We start with reviewing the basic skills neccessary to manipulate image data, using Matlab and the Unix operating system. Next we introduce the basics of MRI, principles of T1, T2 and T2* contrast, and how images are formed; in the remainder of the physics section we cover the BOLD effect and artifacts that corrupt the signal. In the experimental design section we start by introducing blocked andevent-related designs, and how to create designs with optimal statistical power; we cover safety issues and how to screen subjects to enter and be scanned in a MR magnet. The remainder of the experimental design section is focused on practicalities of placing a subject in the scanner and how to use the paradigm presentation software, E-prime. We start the statistics section with a review of the basics of hypothesis testing and linear regression, then present the statistical tools specific to neuroimaing; we cover the analysis of fMRI data, starting from preprocessing to eliminate systematic noise (e.g. subject movement, physiological effects), fitting of models (e.g. intrasubject versus group analysis), diagnosing of model fit and finally statistical inference on statistic maps.

Prerequisites:

The intended audience is a graduate student with basic mathematical and statistical background. Prerequites are an introductory statistics course; advanced statistics course will be an asset. It is strongly recommended that the students be able to complete a Matlab tutorial such as:
(UFL) (MTU) (The Mathworks) ... (or any others on the web)

Textbooks:

There is no textbook required for the class. The following texts are recommended for reference:

Functional Magnetic Resonance Imaging
by Scott A. Huettel, Allen W. Song, Gregory McCarthy,
(c) Sinauer 2004

Introduction to Functional Magnetic Resonance Imaging : Principles and Techniques
by Richard B. Buxton ,
(c) Cambridge University Press, 2002

Functional MRI: An Introduction to Methods
by Peter Jezzard (Editor), Paul M. Matthews (Editor), Stephen M. Smith (Editor)
(c) Oxford University Press, 2001

Instructors:

Douglas Noll, Thomas Nichols, Luis Hernandez-Garcia

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