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Functional magnetic resonance imaging (fMRI)2015VT
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Course plan
No of lectures
8
Recommended for
Ph.D. students in image processing, statistics, cognitive science and computer science.
The course was last given
This is the first time.
Goals
The course aims to give a thorough introduction to functional magnetic resonance imaging (fMRI), especially aimed at the technical details. After completing the course, students should be able to understand the basics of MR scanners, how to setup simple fMRI experiments and how to analyze fMRI data in different ways (using frequentist, Bayesian and machine learning approaches).
Prerequisites
- A basic programming course.
- A basic course in linear algebra.
- Basic statistical knowledge (linear regression, t-test).
Organization
The course consists of 10 lectures, 4 computer laborations and one individual
project.
- Computer laboration 1: Analyze task-based fMRI data using frequentistic
statistical analysis, using one of the three common software packages (SPM,
FSL, AFNI).
- Computer laboration 2: Analyze task-based fMRI data using Bayesian
statistical analysis.
- Computer laboration 3: Analyze resting state fMRI data using different
methods.
- Computer laboration 4: Analyze task-based fMRI data using different machine
learning methods.
The course will not involve any data collection. Instead, freely available fMRI
data will be downloaded from repositories like OpenfMRI.org.
Contents
- Intro + basic image processing (Lecture 1)
- Basic concepts of MRI and fMRI, applications of fMRI (Lecture 2)
- Preprocessing of fMRI data (Lecture 3)
- Frequentistic statistical analysis of fMRI data (Lectures 4 + 5)
- Bayesian statistical analysis of fMRI data (Lecture 6)
- Resting state fMRI (Lecture 7)
- Machine learning in fMRI (Lecture 8)
Literature
Handbook of functional MRI data analysis, Poldrack, Mumford, Nichols, Cambridge University Press, ISBN 978-0-521-51766-9
Lecturers
Anders Eklund
Examiner
Anders Eklund
Examination
Computer laborations 3 hp
Individual project 3 hp
Credit
6 hp
Comments
Page responsible: Director of Graduate Studies
