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.

Programm:

Lectures (incl. practical sessions) take place on Tuesdays, 8:15 - 12:00, at the ETH - Building ETZ, Room E6

In this year's course, we will scan our own fMRI data in two sessions during the semester (more information will be communicated at the beginning of the semester).

 

First part - Basic analysis of fMRI data (UZH/ETH and MED):

24.09. 08:15-10:00 UZH/ETH: Scanning of own Experiment (Sandra Iglesias); MED: Setting up Matlab and SPM on own computers (Jakob Heinzle)

MandMFolder, Intro-slides, Tutorial slides

10:15-12:00  Foundations of functional fMRI: neurophysiology and physics (Jakob Heinzle)

 Lecture slides

01.10. 08:15-10:00 MED: Scanning of own Experiment (Sandra Iglesias); UZH/ETH: Setting up Matlab and SPM on own computers (Jakob Heinzle)

MandMFolder, Intro-slides, Tutorial slides

10:15-12:00 Why is fMRI important for medicine? (Klaas Enno Stephan)

Lecture slides

08.10.  08:15-10:00 Introduction to Spatial preprocessing of fMRI images (Sam Harrison)

Lecture slides

10:15-12:00 Analysis of own data - Preprocessing

Tutorial slides

Code

15.10.  08:15-10:00 The General Linear Model for fMRI analyses and inference I  (Klaas Enno Stephan)

Lecture GLM

Lecture Inference

10:15-12:00 Analysis of own data - Research question and GLM

Tutorial slides

Code

22.10.   08:15-10:00 The General Linear Model for fMRI analyses and inference II (Klaas Enno Stephan)

Lecture slides

10:15-12:00: Analysis of own data - First level analysis and inference

Tutorial slides

Code

29.10.  08:15-10:00 Event-related fMRI and design efficiency (Jakob Heinzle) 

Lecture slides

Code: HRF demos

10:15-12:00 Analysis of own data and preparation of short presentation (Jakob Heinzle, Sandra Iglesias)

05.11.  08:15-10:00 Experimental design and Resting State Analysis (Sara Tomiello, Sandra Iglesias)

Lecture slides Design

Lecture slides rsfMRI

10:15-12:00 Short presentation of results of analysis of own data (MED)

Second part - Advanced topics (UZH/ETH):

12.11.  08:15-10:00 Group level analysis (Sandra Iglesias)

Lecture slides

10:15-12:00 Tutorial

Tutorial slides

19.11.  08:15-10:00 Noise models in fMRI and noise correction (Matthias Müller-Schrader)

Lecture slides

10:15-12:00 PhysIO

Physio demo_code

26.11.  08:15-10:00 Bayesian inference and Bayesian model selection (Klaas Enno Stephan)

Lecture slides

10:15-12:00 Tutorial: BMA and BMS

Tutorial slides

Code

03.12.  08:15-10:00 Computational Neuroimaging (model-based fMRI) (Birte Toussaint)

Lecture slides

10:15-12:00 Tutorial

Tutorial slides

Code

10.12.  08:15-10:00 Introduction to Dynamic Causal Modelling (Stefan Frässle)

Lecture slides

10:15-12:00 DCM analysis

Tutorial slides

Code

17.12. 10:00-11:30 Exam (UZH/ETH)

Exam info

 

Exam dates and testat:

First part (Medizin Mantelstudium): No formal exam, but in order to get the testat, you need to present your data analysis in a short presentation during the tutorial on Tuesday, 05.11.2018, 10:15-12:00, Room: ETZ E6.

Main exam (all other students): Tuesday, 17.12.2018, 10:00-11:30, Room: ETZ E6

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

Contact:

Sandra Iglesias: iglesias@biomed.ee.ethz.ch

Jakob Heinzle: heinzle@biomed.ee.ethz.ch