Resting-State SEEG May Help Localize Epileptogenic Brain Regions

Abstract

BACKGROUND Stereotactic electroencephalography (SEEG) is a minimally invasive neurosurgical method to localize epileptogenic brain regions in epilepsy but requires days in the hospital with interventions to trigger several seizures. OBJECTIVE To make initial progress in the development of network analysis methods to identify epileptogenic brain regions using brief, resting-state SEEG data segments, without requiring seizure recordings. METHODS In a cohort of 15 adult focal epilepsy patients undergoing SEEG, we evaluated functional connectivity (alpha-band imaginary coherence) across sampled regions using brief (2 min) resting-state data segments. Bootstrapped logistic regression was used to generate a model to predict epileptogenicity of individual regions. RESULTS Compared to nonepileptogenic structures, we found increased functional connectivity within epileptogenic regions (P < .05) and between epileptogenic areas and other structures (P < .01, paired t-tests, corrected). Epileptogenic areas also demonstrated higher clustering coefficient (P < .01) and betweenness centrality (P < .01), and greater decay of functional connectivity with distance (P < .05, paired t-tests, corrected). Our functional connectivity model to predict epileptogenicity of individual regions demonstrated an area under the curve of 0.78 and accuracy of 80.4%. CONCLUSION Our study represents a preliminary step towards defining resting-state SEEG functional connectivity patterns to help localize epileptogenic brain regions ahead of neurosurgical treatment without requiring seizure recordings.

Seizure‐onset regions demonstrate high inward directed connectivity during resting‐state: An SEEG study in focal epilepsy

“…15 We found that epileptogenic structures demonstrated higher connectivity than nonepileptogenic regions, and connectivity measures may predict epileptogenicity with modest accuracy. 15 Beyond small sample size, one limitation is that information may be lost using dichotomized regional classification, without considering other areas involved in seizure networks. 14,16 Although imaginary coherence is a linear measurement, 17 it is unknown if nonlinear, information theorybased connectivity may demonstrate improved accuracy in identifying epileptogenic regions.…”

“…Twenty-five patients with medically refractory focal epilepsy were included in this study, including 15 individuals from our prior study. 15 Patients had video monitoring and SEEG recordings obtained at Vanderbilt University Medical Center (VUMC) between 2017 and 2019. This investigation was approved by VUMC Institutional Review Board, and informed written consent was obtained from patients.…”

“…Furthermore, it is unclear if higher connectivity at epileptogenic regions is inward and/or outward, and if directed connectivity measurements may improve our ability to predict epileptogenicity. 15 The present study builds upon our prior study, 15 and now seeks to better characterize both nondirected and directed connectivity in regions throughout the epilepsy network using brief, resting-state SEEG recordings in patients with focal epilepsy. We will evaluate differences between ictogenic, early propagation, irritative, and uninvolved brain regions.…”

Current Conceptual Understanding of the Epileptogenic Network From Stereoelectroencephalography-Based Connectivity Inferences

“…In another study, alpha, theta, and delta imaginary coherence within a region and between regions was significantly higher in epileptogenic regions compared to non-epileptogenic regions, with the alpha-frequency band showing the biggest differences (29). Furthermore, several graph theory measures, including nodal betweenness centrality, edge betweenness centrality, and clustering coefficient were significantly higher in epileptogenic regions.…”

Microelectrode recordings in human epilepsy: a case for clinical translation

“…Given that the vast majority of intracranial recordings are in the interictal state, it would seem logical to try and utilize these ‘steady-state’ recordings for clinical localization ( Goodale et al , 2020 ), a concept that is supported at the neuronal level by animal literature showing interictal neuronal network abnormalities in animal models. Early work in interictal microelectrode recordings attempted to ascertain interictal properties of ‘epileptic’ and ‘nonepileptic’ neurons in the interictal state ( Ward and Schmidt, 1961 ; Isokawa-Akesson et al , 1987 , 1989 ), mainly in mesial temporal structures.…”