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227-0969-00L 6 Credits MSC , NDS D-HEST , D-MAVT , D-PHYS , D-ITET
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Methods & Models for fMRI Data Analysis

Lecturers & Examiners: Prof. Dr. Klaas Stephan
VVZ CR n/a

Last Updated: 2026-02-05 15:35:59

Abstract

This course teaches methods and models for fMRI data analysis, covering all aspects of statistical parametric mapping (SPM), incl. preprocessing, the general linear model, statistical inference, multiple comparison corrections, event-related designs, and Dynamic Causal Modelling (DCM), a Bayesian framework for identification of nonlinear neuronal systems from neurophysiological data.

Objective

To obtain in-depth knowledge of the theoretical foundations of SPM and DCM and of their practical application to empirical fMRI data.

Content

This course teaches state-of-the-art methods and models for fMRI data analysis in lectures and exercises. It covers all aspects of statistical parametric mapping (SPM), incl. preprocessing, the general linear model, frequentist and Bayesian inference, multiple comparison corrections, and event-related designs, and Dynamic Causal Modelling (DCM), a Bayesian framework for identification of nonlinear neuronal systems from neurophysiological data. A particular emphasis of the course will be on methodological questions arising in the context of clinical studies in psychiatry and neurology. Practical exercises serve to consolidate the skills taught in lectures.

Resources

Learning Materials (Links)

General Information

Language
English
Levels
MSC , NDS
Frequency
Yearly recurring

Examination

Type
end-of-semester examination

Course Components

Type Title Time & Place Hours
lecture Methods & Models for fMRI Data Analysis
The lecturers will communicate the exact lesson times of ONLINE courses.
  • Tue 08:00-12:00 (ON LI NE)
4 h weekly

Offered In