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.
- dynamic network neuroscience
- epileptic network
- non-negative matrix factorization
- functional subgraphs
- prediction
- interictal
Footnotes
The authors declare no competing financial interests.
A.N.K. and B.L. were supported by the National Institutes of Health Awards #R01-NS063039 and #1U24 NS 63930-01A1, the Citizens United for Research in Epilepsy (CURE) Julie’s Hope Award, and the Mirowski Foundation. D.S.B. was supported by the John D. and Catherine T. MacArthur Foundation, the Alfred P. Sloan Foundation, the Army Research Laboratory and the Army Research Office through contract numbers W911NF-10-2-0022 and W911NF-14-1-0679, the National Institute of Mental Health Grant 2-R01-DC-009209-11, the National Institute of Child Health and Human Development Grant 1R01HD086888-01, the Office of Naval Research, and National Science Foundation Grants BCS-1441502, BCS-1430087, and PHY-1554488.
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