BIOMEDE 499 - Special Topics
Fall 2022, Section 098 - Introduction to Functional MRI
Instruction Mode: Section 098 is  In Person (see other Sections below)
Subject: Biomedical Engineering (BIOMEDE)
Department: CoE Biomedical Engineering
See additional student enrollment and course instructor information to guide you in your decision making.


3 (Non-LSA credit).
Requirements & Distribution:
With permission of instructor.
Other Course Info:
This course counts toward the 60 credits of math/science required for a Bachelor of Science degree.
May be repeated for credit.
Primary Instructor:
Start/End Date:
Full Term 8/29/22 - 12/9/22 (see other Sections below)
NOTE: Drop/Add deadlines are dependent on the class meeting dates and will differ for full term versus partial term offerings.
For information on drop/add deadlines, see the Office of the Registrar and search Registration Deadlines.


This course will present the basic skills to design and analyze functional magnetic resonance 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
  4. Statistics

We start with reviewing the basic skills necessary to manipulate image data, using Mat lab and the Unix operating system. Next we introduce the basics of MRI, principals of T1, T2 and T2*, 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 and event-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 neuroimaging; 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.

The intended audience is a graduate student with basic mathematical and statistical background. Prerequisites are an introductory statistics course; advanced statistics course and experience with Matlab will be an asset.


BIOMEDE 499 - Special Topics
Schedule Listing
098 (LEC)
 In Person
8/29/22 - 12/9/22
Note: Please note this is a 3 credit hour course. Cross listed with Psych 808.005.

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