Course Identification

fMRI Methods

Lecturers and Teaching Assistants

Dr. Tali Weiss, Dr. Edna Furman-Haran, Dr. Kobi Snitz, Prof. Roy Mukamel, Prof. Tzipi Horowitz Kraus, Dr. Talia Brandman
Noam Saadon-Grosman, Or Yizhar, Lior Gorodisky

Course Schedule and Location

First Semester
Wednesday, 14:15 - 16:00, FGS, Rm B

Sunday, 11:15 - 13:00, FGS, Rm B

Field of Study, Course Type and Credit Points

Life Sciences: Lecture; Elective; Regular; 3.00 points
Life Sciences (Molecular and Cellular Neuroscience Track): Lecture; Elective; Regular; 3.00 points
Life Sciences (Brain Sciences: Systems, Computational and Cognitive Neuroscience Track): Lecture; Elective; Regular; 3.00 points
Life Sciences (Computational and Systems Biology Track): Lecture; Elective; Regular; 3.00 points




Basic working knowledge of Matlab



Language of Instruction


Attendance and participation

Expected and Recommended

Grade Type

Numerical (out of 100)

Grade Breakdown (in %)

assignments related to the reading material in the course

Evaluation Type

Final assignment

Scheduled date 1


Estimated Weekly Independent Workload (in hours)



The goal of the course is to cover the major technical topics and methodological issues in the field of functional magnetic resonance imaging (fMRI) brain research.

With this background, students will be equipped with better tools to design and implement their own fMRI experiments, analyze fMRI data, and importantly appreciate the advantages and limitations of fMRI research.

Lab assignments will mainly involve analyzing fMRI data (OpenNeuro) using FSL package.

Specifically, course material will cover the following (and perhaps more if time allows):

1. Introduction and overview of MRI basics, the MRI scanner, and fMRI.

2. Experimental designs (block and event related designs, fMRI adaptation, naturalistic stimuli, resting state)

3. fMRI data analyses: single- and multi-subject (GLM, correlations, functional connectivity, resting state, uni- vs. multi-variate analyses) and statistical methods.

4. Current topic in fMRI (e.g. voodoo/spurious correlations/circular inference; functional localizers debate).

5. Human Connectome Project - analysis, visualization, and sharing tools


Lab practice will cover the following subjects:

1. Introduction to Psychtoolbox - preparing your experiment.

2. Analyzing fMRI data (OpenNeuro) using FSL package and Matlab



Learning Outcomes

Upon successful completion of this course students should be able to:

[1] Design and implement fMRI experiments, analyze fMRI experimental data using FSL package

[2] Get familiarised with "open database" as HCP, UK Biobank Brain Imaging, and ABCD study

[3] Be able to read, appreciate, and be critical of fMRI studies/research



Reading List

[1]Functional Magnetic Resonance Imaging: An Introduction to Methods

Peter Jezzard, Paul M Matthews, and Stephen M Smith

[2]Introduction to Resting State fMRI Functional Connectivity

Janine Bijsterbosch, Stephen M. Smith, and Christian F. Beckmann