Functional magnetic resonance imaging (fMRI)2015VT
No of lectures
Ph.D. students in image processing, statistics, cognitive science and computer science.
The course was last given
This is the first time.
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).
- A basic programming course.
- A basic course in linear algebra.
- Basic statistical knowledge (linear regression, t-test).
The course consists of 10 lectures, 4 computer laborations and one individual
- 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.
- 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)
Handbook of functional MRI data analysis, Poldrack, Mumford, Nichols, Cambridge University Press, ISBN 978-0-521-51766-9
Computer laborations 3 hp
Individual project 3 hp
Page responsible: Director of Graduate Studies