OPTIMIZING BRAIN CONNECTIVITY NETWORKS FOR DISEASE CLASSIFICATION USING EPIC.

We propose a method to adaptively select an optimal cortical segmentation for brain connectivity analysis that maximizes feature-based disease classification performance. In standard structural connectivity analysis, the cortex is typically subdivided (parcellated) into N anatomical regions. White matter fiber pathways from tractography are used to compute an N × N matrix, which represents the pairwise connectivity between those regions. We optimize this representation by sampling over the space of possible region combinations and represent each configuration as a set partition of the N anatomical regions. Each partition is assigned a score using accuracy from a support vector machine (SVM) classifier of connectivity matrices in a group of patients and controls. We then define a high-dimensional optimization problem using simulated annealing to identify an optimal partition for maximum classification accuracy. We evaluate the results separately on test data using cross-validation. Specifically, we demonstrate results on the ADNI-2 dataset, where we optimally parcellate the cortex to yield an 85% classification accuracy using connectivity information alone. We refer to our method as evolving partitions to improve connectomics (EPIC).