Fast, fully Bayesian spatiotemporal inference for fMRI data.

ABSTRACT: We propose a spatial Bayesian variable selection method for detecting blood oxygenation level dependent activation in functional magnetic resonance imaging (fMRI) data. Typical fMRI experiments generate large datasets that exhibit complex spatial and temporal dependence. Fitting a full statistical model to such data can be so computationally burdensome that many practitioners resort to fitting oversimplified models, which can lead to lower quality inference. We develop a full statistical model that permits efficient computation. Our approach eases the computational burden in two ways. We partition the brain into 3D parcels, and fit our model to the parcels in parallel. Voxel-level activation within each parcel is modeled as regressions located on a lattice. Regressors represent the magnitu

SUBMITTER: Musgrove DR 

PROVIDER: S-EPMC5006116 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature