• Sarah E Goodale, Hernán F J González, Graham W Johnson, Kanupriya Gupta, William J Rodriguez, Robert Shults, Baxter P Rogers, John D Rolston, Benoit M Dawant, Victoria L Morgan, and Dario J Englot.
• Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee.
• Neurosurgery. 2020 Jun 1; 86 (6): 792801792-801.

BackgroundStereotactic 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.ObjectiveTo 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.MethodsIn 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.ResultsCompared 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%.ConclusionOur 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.Copyright © 2019 by the Congress of Neurological Surgeons.

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