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.
Keywords: Brain networks; Functional connectivity; Intracranial EEG; Localization; Seizure.
Copyright © 2019 by the Congress of Neurological Surgeons.