Detecting Epileptic Regions Based on Global Brain Connectivity Patterns

We present a method to detect epileptic regions based on functional connectivity differences between individual epilepsy patients and a healthy population. Our model assumes that the global functional characteristics of these differences are shared across patients, but it allows for the epileptic regions to vary between individuals. We evaluate the detection performance against intracranial EEG observations and compare our approach with two baseline methods that use standard statistics. The baseline techniques are sensitive to the choice of thresholds, whereas our algorithm automatically estimates the appropriate model parameters and compares favorably with the best baseline results. This suggests the promise of our approach for pre-surgical planning in epilepsy.MIT/Lincoln Laboratory CollaborationNational Alliance for Medical Image Computing (U.S.) (grant (NIH NIBIB NAMIC U54-EB005149))Neuroimaging Analysis Center (U.S.) (NIH NCRR NAC P41-RR13218)Neuroimaging Analysis Center (U.S.) (NIH NIBIB NAC P41-EB-015902)National Science Foundation (U.S.) (NSF CAREER Grant 0642971