RT Journal Article
SR Electronic
T1 Recurring functional interactions predict network architecture of interictal and ictal states in neocortical epilepsy
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 090662
DO 10.1101/090662
A1 Ankit N. Khambhati
A1 Danielle S. Bassett
A1 Brian S. Oommen
A1 Stephanie H. Chen
A1 Timothy H. Lucas
A1 Kathryn A. Davis
A1 Brian Litt
YR 2016
UL http://biorxiv.org/content/early/2016/11/30/090662.abstract
AB 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 hours of intracranial recordings in each of 22 neocortical epilepsy patients. Using this approach, we found: (i) subgraphs from ictal (seizure) and interictal (baseline) epochs are topologically similar, (ii) interictal subgraph topology and dynamics can predict brain regions that generate seizures, and (iii) subgraphs undergo slower and more coordinated fluctuations during ictal epochs compared to interictal epochs. Our observations suggest that the epileptic network drives dysfunction by controlling dynamics of functional interactions between brain regions that generate seizures and those that underlie normal function.