Bayesian mapping of brain regions using compound Markov random field priors

Human brain mapping, i.e. the detection of functional regions and
their connections, has experienced enormous progress through the
use of functional magnetic resonance imaging (fMRI). The massive
spatio-temporal data sets generated by this imaging technique
impose challenging problems for statistical analysis. Many
approaches focus on adequate modeling of the temporal component.
Spatial aspects are often considered only in a separate
postprocessing step, if at all, or modeling is based on Gaussian
random fields. A weakness of Gaussian spatial smoothing is possible
underestimation of activation peaks or blurring of sharp
transitions between activated and non-activated regions. In this
paper we suggest Bayesian spatio-temporal models, where spatial
adaptivity is improved through inhomogeneous or compound Markov
random field priors. Inference is based on an approximate MCMC
technique. Performance of our approach is investigated through a
simulation study, including a comparison to models based on
Gaussian as well as more robust spatial priors in terms of
pixelwise and global MSEs. Finally we demonstrate its use by an
application to fMRI data from a visual stimulation experiment for
assessing activation in visual cortical areas.