227-0969-00L Methods & Models for fMRI Data Analysis
Semester | Autumn Semester 2012 |
Lecturers | K. Stephan |
Periodicity | yearly recurring course |
Language of instruction | English |
Abstract | 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 identificationof nonlinear neuronal systems from neurophysiological data. |
Objective | To obtain a good knowledge of the theoretical foundations of SPM and DCM and their application to empirical fMRI data. |
Content | 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. |