Semiparametric Bayesian models for human brain mapping

Functional magnetic resonance imaging (fMRI) has led to enormous
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.