Group MRF for fMRI activation detection

Noise confounds present serious complications to accurate data analysis in functional magnetic resonance imaging (fMRI). Simply relying on contextual image information often results in unsatisfactory segmentation of active brain regions. To remedy this, we propose a novel Group Markov Random Field (Group MRF) that extends the neighborhood system to other subjects to incorporate group information in modeling each subject's brain activation. Our approach has the distinct advantage of being able to regularize the states of both intra- and inter-subject neighbors without having to create a stringent one-to-one voxel correspondence as in standard fMRI group analysis. Also, our method can be efficiently implemented as a single MRF, hence enabling activation maps of a group of subjects to be simultaneously and collaboratively segmented. We validate on both synthetic and real fMRI data and demonstrate superior performance over standard analysis techniques.