title: OPTIMIZING BRAIN CONNECTIVITY NETWORKS FOR DISEASE CLASSIFICATION USING EPIC.
abstract: 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) intoNanatomical regions. White matter fiber pathways from tractography are used to compute anN×Nmatrix, 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 theNanatomical 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).
authors.affiliation: Imaging Genetics Center, Inst. for Neuroimaging and Informatics, Keck Sch. of Med. of USC, Los Angeles, CA, USA.
authors.forename: Gautam
authors.initials: G
authors.lastname: Prasad
authors.affiliation: Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, UCLA, Los Angeles, CA, USA.
authors.forename: Shantanu H
authors.initials: SH
authors.lastname: Joshi
authors.affiliation: Imaging Genetics Center, Inst. for Neuroimaging and Informatics, Keck Sch. of Med. of USC, Los Angeles, CA, USA.
authors.forename: Paul M
authors.initials: PM
authors.lastname: Thompson
keywords: Cortical parcellation; classification; connectivity matrix; partition; simulated annealing