Methods & Models for fMRI Analysis


Would you like to obtain good knowledge of the theoretical foundations of SPM and DCM and their application to empirical fMRI data?

This course teaches state-of-the-art methods and models for fMRI data analysis. 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 novel approach 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 neuroeconomic and clinical studies.


Lectures (incl. practical sessions) take place on Tuesdays, 8:15 - 12:00, at the ETH - Building ETZ, Room F91

First part - Basic analysis of fMRI data:

20.09.  Foundations of functional MRI: neurophysiology and physics (Jakob Heinzle)

Downloads: Intro 2016, Slides, Tutorial

27.09.  Spatial preprocessing of fMRI images (Lars Kasper)

Downloads: Slides, Tutorial, Code

04.10.  The General Linear Model for fMRI analyses (Frederike Petzschner, Jakob Heinzle)

Downloads: Slides, Tutorial, Code, SPM12 Manual: Chapter 31

11.10.  Classical (frequentist) inference and multiple comparison correction (Justin Chumbley)

Downloads: Slides1, Slides2

18.10.  Experimental design and Resting state fMRI analysis (Sandra Iglesias)

Downloads: Slides1, Slides2, Tutorial, Code, SPM12 Manual: Chapter 36

25.10.  Event-related fMRI and design efficiency (Klaas Enno Stephan)

Downloads: Slides

01.11.  Group level analysis (Sandra Iglesias)

Downloads: Slides, Tutorial, Code, SPM12 Manual: Chapter 32

Second part - Advanced topics:

08.11.  Noise models in fMRI and noise correction (Lars Kasper)

Downloads: Slides, Code&Data

15.11.  Multivariate models and machine learning for fMRI (Jakob Heinzle)

Downloads: Slides

22.11.  Bayesian inference and Bayesian model selection (Klaas Enno Stephan)

Downloads: Slides

29.11.  Computational Neuroimaging (model-based fMRI) (Andreea Diaconescu)

Downloads: Slides, Code

06.12.  Approximate Bayesian inference: Variational Bayes & MCMC (Eduardo Aponte)

Downloads: Slides

13.12.  Basics of Dynamic Causal Modelling (Jakob Heinzle)

Downloads: Slides, Code

20.12.  Advanced aspects of Dynamic Causal Modelling (Stefan Frässle)

Downloads: Slides


Exam dates:

First part (Medizin Mantelstudium): Tuesday, 15.11.2016, 10:15-11:45, Room: ETZ G 91 (one floor above lecture hall)

Testat: You will get your Testat on the 22.11. at 11:45, after the lecture (Room: ETZ F91)!


Main exam (all other students): Tuesday, 10.01.2017, 13:00-14:30, Room: ETZ F91


Material for the exam: Bring with you something to write (pen), your Legi and an ID. No other material is allowed!

Download: Exam information and example questions


Sandra Iglesias:

Jakob Heinzle: