227-0969-00L  Methods & Models for fMRI Data Analysis

SemesterAutumn Semester 2018
LecturersK. Stephan
Periodicityyearly recurring course
Language of instructionEnglish


AbstractThis 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.
ObjectiveTo obtain in-depth knowledge of the theoretical foundations of SPM
and DCM and of their application to empirical fMRI data.
ContentThis 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 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 studies in psychiatry, neurology and neuroeconomics.