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Approaches to the Network-Optimal Partition Problem for fMRI Data

Although much MRI research has been performed using existing atlases of brain regions, it is possible that these regions, which are anatomically determined, are not the truly optimal way to divide brain activity into parts. The goal of this research is to discover a way to take existing fMRI data and use it as a basis for a clustering method that would divide the brain into functional partitions independent of anatomy. Since Bayesian networks over anatomical regions have been shown to be informative models of brain activity, this paper describes attempts to solve a mathematical problem which is an abstraction of the above goal: to partition a group of variables (the voxels of the MRI data) into clusters such that the optimal Bayesian network given the clustering is as “good” as possible.