Bayesian spatio-temporal inference in functional magnetic resonance imaging

Mapping of the human brain by means of functional magnetic
resonance imaging (fMRI) is an emerging field in medical sciences.
Current techniques to detect activated areas of the brain mostly
proceed in two steps. First, conventional methods of correlation,
regression and time series analysis are used to assess activation
by a separate, pixelwise comparison of the MR signal time courses
to the reference function of a presented stimulus. Spatial aspects
caused by correlations between neighboring pixels are considered in
a second step, if at all. Aim of this article is to present
hierarchical Bayesian approaches that allow to simultaneously
incorporate temporal and spatial dependencies between pixels
directly in the model formulation. For reasons of computational
feasibility, models have to be comparatively parsimonious, without
oversimplifying. We introduce parametric and semiparametric spatial
and spatio-temporal models that proved appropriate and illustrate
their performance by application to fMRI data from a visual
stimulation experiment.