Recurring Functional Interactions Predict Network Architecture of Interictal and Ictal States in Neocortical Epilepsy
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 01, 2017
- Source ID
- 10.1523/eneuro.0091-16.2017
Entities
People
- Ankit Khambhati
- Brian Litt
- Brian S. Oommen
- Danielle Bassett
- Kathryn A Davis
- Stephanie H. Chen
- Timothy H. Lucas
Organizations
- Alfred P. Sloan Foundation
- Citizens United for Research in Epilepsy
- John D. and Catherine T. MacArthur Foundation
- National Institute of Mental Health
- National Institutes of Health
- National Science Foundation
- Office of Naval Research
- United States Army Research Laboratory