Gössl, Christoff; Auer, Dorothee P.; Fahrmeir, Ludwig
Bayesian spatio-temporal inference in functional magnetic resonance imaging.
Collaborative Research Center 386, Discussion Paper 192
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.