Detecting Epileptic Regions Based on Global Brain Connectivity Patterns
Author(s)Sweet, Andrew; Venkataraman, Archana; Stufflebeam, Steven M.; Liu, Hesheng; Tanaka, Naoro; Madsen, Joseph R.; Golland, Polina; ... Show more Show less
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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.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013
Springer-Verlag Berlin Heidelberg
Sweet, Andrew, Archana Venkataraman, Steven M. Stufflebeam, Hesheng Liu, Naoro Tanaka, Joseph Madsen, and Polina Golland. “Detecting Epileptic Regions Based on Global Brain Connectivity Patterns.” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013, Part I, Edited by K. Mori et al. (Lecture Notes in Computer Science; volume 8149) Springer Berlin, (2013): 98–105.
Author's final manuscript