title: Graph Measures of Node Strength for Characterizing Preictal Synchrony in Partial Epilepsy.
abstract: The reference electrophysiological pattern at seizure onset is the "rapid discharge," as visible on intracerebral electroencephalography (EEG). This discharge typically corresponds to a decrease of synchrony across brain areas. In contrast, the preictal period can exhibit patterns of increased synchrony, which can be quantified by network measures. Our objective was to compare preictal synchrony with a quantification of the rapid discharge as provided by the epileptogenicity index (EI). We investigated 24 seizures from 12 patients recorded by stereotaxic EEG (SEEG). Seizures were classified visually as containing preictal synchrony or not. We computed pairwise nonlinear correlation (h(2)) across channels in the 8 sec preceding the rapid discharge. The sum of ingoing and outgoing links (IN and OUT node strength), as well as the sum of all links (total strength, TOT) were computed for each region. We tested several filtering schemes, and quantified the capacity of each strength measure to serve as a detector of regions with high EI values using a receiver operating characteristic (ROC) analysis. We found that the best correspondence between node strength and EI was obtained for the OUT and TOT measures, for signals filtered in the 15-40 Hz band-that is, for the band corresponding to the spiky part of epileptic discharges. In agreement with these results, we also found that the ROC results were improved when considering only seizures with visible synchronous patterns in the preictal period. Our results suggest that measuring strength of preictal connectivity graphs can bring useful clinical information on the epileptogenic zone.
authors.affiliation: 1 Aix-Marseille Université, INSERM, Institut de Neurosciences des Systèmes , Marseille, France .
authors.forename: Sandra
authors.initials: S
authors.lastname: Courtens
authors.affiliation: 1 Aix-Marseille Université, INSERM, Institut de Neurosciences des Systèmes , Marseille, France .
authors.forename: Bruno
authors.initials: B
authors.lastname: Colombet
authors.affiliation: 1 Aix-Marseille Université, INSERM, Institut de Neurosciences des Systèmes , Marseille, France .|2 AP-HM, Hôpital de la Timone, Service de Neurophysiologie Clinique, Marseille, France .
authors.forename: Agnès
authors.initials: A
authors.lastname: Trébuchon
authors.affiliation: 3 Institut de Neurosciences de la Timone UMR 7289, Aix Marseille Université, CNRS, 13385 Marseille, France .
authors.forename: Andrea
authors.initials: A
authors.lastname: Brovelli
authors.affiliation: 1 Aix-Marseille Université, INSERM, Institut de Neurosciences des Systèmes , Marseille, France .|2 AP-HM, Hôpital de la Timone, Service de Neurophysiologie Clinique, Marseille, France .
authors.forename: Fabrice
authors.initials: F
authors.lastname: Bartolomei
authors.affiliation: 1 Aix-Marseille Université, INSERM, Institut de Neurosciences des Systèmes , Marseille, France .
authors.forename: Christian G
authors.initials: CG
authors.lastname: Bénar
mesh_terms: D000328:Adult; D001921:Brain / Q000503:physiopathology*; D003344:Cortical Synchronization; D004569:Electroencephalography / Q000379:methods*; D004828:Epilepsies, Partial / Q000503:physiopathology*; D006801:Humans; D012815:Signal Processing, Computer-Assisted
keywords: epilepsy; functional connectivity; graph theory; partial seizures; stereoelectroencephalography (SEEG)