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

SemesterAutumn Semester 2012
LecturersK. Stephan
Periodicityyearly recurring course
Language of instructionEnglish


AbstractState-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 identificationof nonlinear neuronal systems from neurophysiological data.
ObjectiveTo obtain a good knowledge of the theoretical foundations of SPM
and DCM and 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 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.