TY - JOUR T1 - Recurring Functional Interactions Predict Network Architecture of Interictal and Ictal States in Neocortical Epilepsy JF - eneuro JO - eNeuro DO - 10.1523/ENEURO.0091-16.2017 VL - 4 IS - 1 SP - ENEURO.0091-16.2017 AU - Ankit N. Khambhati AU - Danielle S. Bassett AU - Brian S. Oommen AU - Stephanie H. Chen AU - Timothy H. Lucas AU - Kathryn A. Davis AU - Brian Litt Y1 - 2017/01/01 UR - http://www.eneuro.org/content/4/1/ENEURO.0091-16.2017.abstract N2 - Human epilepsy patients suffer from spontaneous seizures, which originate in brain regions that also subserve normal function. Prior studies demonstrate focal, neocortical epilepsy is associated with dysfunction, several hours before seizures. How does the epileptic network perpetuate dysfunction during baseline periods? To address this question, we developed an unsupervised machine learning technique to disentangle patterns of functional interactions between brain regions, or subgraphs, from dynamic functional networks constructed from approximately 100 h of intracranial recordings in each of 22 neocortical epilepsy patients. Using this approach, we found: (1) subgraphs from ictal (seizure) and interictal (baseline) epochs are topologically similar, (2) interictal subgraph topology and dynamics can predict brain regions that generate seizures, and (3) subgraphs undergo slower and more coordinated fluctuations during ictal epochs compared to interictal epochs. Our observations suggest that seizures mark a critical shift away from interictal states that is driven by changes in the dynamical expression of strongly interacting components of the epileptic network. ER -