title: Network dynamics of the brain and influence of the epileptic seizure onset zone.
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.
authors.affiliation: Institute for Computational Medicine and.
authors.forename: Samuel P
authors.initials: SP
authors.lastname: Burns
authors.affiliation: Institute for Computational Medicine and ssarma2@jhu.edu sabato@engr.uconn.edu.
authors.forename: Sabato
authors.initials: S
authors.lastname: Santaniello
authors.affiliation: Institute for Computational Medicine and Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD 21218; and.
authors.forename: Robert B
authors.initials: RB
authors.lastname: Yaffe
authors.affiliation: Epilepsy Center, Department of Neurology, and.
authors.forename: Christophe C
authors.initials: CC
authors.lastname: Jouny
authors.affiliation: Epilepsy Center, Department of Neurology, and.
authors.forename: Nathan E
authors.initials: NE
authors.lastname: Crone
authors.affiliation: Epilepsy Center, Department of Neurology, and.
authors.forename: Gregory K
authors.initials: GK
authors.lastname: Bergey
authors.affiliation: Institute for Computational Medicine and Department of Neurosurgery, The Johns Hopkins School of Medicine, Baltimore, MD 21287.
authors.forename: William S
authors.initials: WS
authors.lastname: Anderson
authors.affiliation: Institute for Computational Medicine and Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD 21218; and ssarma2@jhu.edu sabato@engr.uconn.edu.
authors.forename: Sridevi V
authors.initials: SV
authors.lastname: Sarma
mesh_terms: D000293:Adolescent; D000328:Adult; D019540:Area Under Curve; D001921:Brain / Q000503:physiopathology*; D001931:Brain Mapping / Q000379:methods*; D002675:Child, Preschool; D004566:Electrodes; D004569:Electroencephalography / Q000379:methods; D004827:Epilepsy / Q000503:physiopathology*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors; D055815:Young Adult
keywords: ECoG signals; eigenvector centrality; focal epilepsy; network analysis; seizure localization