  Existing Bayesian spatial priors for functional magnetic resonance imaging
(fMRI) data correspond to stationary isotropic smoothing filters that may
oversmooth at anatomical boundaries. We propose two anatomically informed
Bayesian spatial models for fMRI data with local smoothing in each voxel based
on a tensor field estimated from a T1-weighted anatomical image. We show that
our anatomically informed Bayesian spatial models results in posterior
probability maps that follow the anatomical structure.
