Statistical Modelling 2 (2002), 235–249

Semiparametric Bayesian models for human brain mapping

Ludwig Fahrmeir,
Department of Statistics, Ludwig-Maximilians-University Munich,
Ludwigstraße 33,
D-80539 Munich,
Germany
eMail: fahrmeir@stat.uni-muenchen.de

C. Gössl,
Max-Planck-Institute of Psychiatry,
Munich,
Germany

Abstract:

Functional magnetic resonance imaging (fMRI) has led to enourmous progress in human brain mapping. Adequate analysis of the massive spatiotemporal data sets generated by this imaging technique, combining parametric and non-parametric components, imposes challenging problems in statistical modelling. Complex hierarchical Bayesian models in combination with computer-intensive Markov chain Monte Carlo inference are promising tools.

The purpose of this paper is twofold. First, it provides a review of general semiparametric Bayesian models for the analysis of fMRI data. Most approaches focus on important but separate temporal or spatial aspects of the overall problem, or they proceed by stepwise procedures. Therefore, as a second aim, we suggest a complete spatiotemporal model for analysing fMRI data within a unified semiparametric Bayesian framework. An application to data from a visual stimulation experiment illustrates our approach and demonstrates its computational feasibility.

Keywords:

Functional magnetic resonance imaging; human brain mapping; MCMC; semiparametric models, spatiotemporal models
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