Network dynamics of the brain and influence of the epileptic seizure onset zone
- Samuel P. Burnsa,1,
- Sabato Santanielloa,1,2,3,
- Robert B. Yaffea,b,
- Christophe C. Jounyc,
- Nathan E. Cronec,
- Gregory K. Bergeyc,
- William S. Andersona,d, and
- Sridevi V. Sarmaa,b,3
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Edited by Nicholas R. Anderson, Novel Research Associates, Fairfield, CA, and accepted by the Editorial Board October 7, 2014 (received for review February 5, 2014)
Significance
In epilepsy, seizures elicit changes in the functional connectivity of the brain that shed insight into the seizures’ nature and onset zone. We investigated the brain connectivity of patients with partial epileptic seizures from continuous multiday recordings and found that (i) the connectivity defines a finite set of brain states, (ii) seizures are characterized by a consistent progression of states, and (iii) the seizure onset zone is isolated from the surrounding regions at seizure onset but becomes most connected toward seizure termination. Our results suggest that a finite-dimensional state space model may characterize the dynamics of the epileptic brain and ultimately help localize the seizure onset zone, which is currently done by clinicians through visual inspection of electrocorticographic recordings.
Abstract
The human brain is a dynamic networked system. Patients with partial epileptic seizures have focal regions that periodically diverge from normal brain network dynamics during seizures. We studied the evolution of brain connectivity before, during, and after seizures with graph-theoretic techniques on continuous electrocorticographic (ECoG) recordings (5.4 ± 1.7 d per patient, mean ± SD) from 12 patients with temporal, occipital, or frontal lobe partial onset seizures. Each electrode was considered a node in a graph, and edges between pairs of nodes were weighted by their coherence within a frequency band. The leading eigenvector of the connectivity matrix, which captures network structure, was tracked over time and clustered to uncover a finite set of brain network states. Across patients, we found that (i) the network connectivity is structured and defines a finite set of brain states, (ii) seizures are characterized by a consistent sequence of states, (iii) a subset of nodes is isolated from the network at seizure onset and becomes more connected with the network toward seizure termination, and (iv) the isolated nodes may identify the seizure onset zone with high specificity and sensitivity. To localize a seizure, clinicians visually inspect seizures recorded from multiple intracranial electrode contacts, a time-consuming process that may not always result in definitive localization. We show that network metrics computed from all ECoG channels capture the dynamics of the seizure onset zone as it diverges from normal overall network structure. This suggests that a state space model can be used to help localize the seizure onset zone in ECoG recordings.
Footnotes
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↵1S.P.B. and S.S. contributed equally to this work.
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↵2Present address: Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269.
- ↵3To whom correspondence may be addressed. Email: ssarma2@jhu.edu or sabato@engr.uconn.edu.
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Author contributions: S.P.B., N.E.C., G.K.B., W.S.A., and S.V.S. designed research; S.P.B., S.S., R.B.Y., C.C.J., W.S.A., and S.V.S. performed research; S.P.B., S.S., and R.B.Y. analyzed data; and S.P.B., S.S., N.E.C., G.K.B., W.S.A., and S.V.S. wrote the paper.
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The authors declare no conflict of interest.
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This article is a PNAS Direct Submission. N.R.A. is a guest editor invited by the Editorial Board.
