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

Anatomically informed Bayesian spatial priors for fMRI analysis