A Subpopulation of Spikes Predicts Successful Epilepsy Surgery Outcome. Issue 3 (10th December 2022) - Thomas, John; Kahane, Philippe; Abdallah, Chifaou; Avigdor, Tamir; Zweiphenning, Willemiek J. E. M.; Chabardes, Stephan; Jaber, Kassem; Latreille, Véronique; Minotti, Lorella; Hall, Jeff; Dubeau, François; Gotman, Jean; et al.
Abstract : Objective: Epileptic spikes are the traditional interictal electroencephalographic (EEG) biomarker for epilepsy. Given their low specificity for identifying the epileptogenic zone (EZ), they are given only moderate attention in presurgical evaluation. This study aims to demonstrate that it is possible to identify specific spike features in intracranial EEG that optimally define the EZ and predict surgical outcome. Methods: We analyzed spike features on stereo‐EEG segments from 83 operated patients from 2 epilepsy centers (37 Engel IA) in wakefulness, non‐rapid eye movement sleep, and rapid eye movement sleep. After automated spike detection, we investigated 135 spike features based on rate, morphology, propagation, and energy to determine the best feature or feature combination to discriminate the EZ in seizure‐free and non‐seizure‐free patients by applying 4‐fold cross‐validation. Results: The rate of spikes with preceding gamma activity in wakefulness performed better for surgical outcome classification (4‐fold area under receiver operating characteristics curve [AUC] = 0.755 ± 0.07) than the seizure onset zone, the current gold standard (AUC = 0.563 ± 0.05, p = 0.015) and the ripple rate, an emerging seizure‐independent biomarker (AUC = 0.537 ± 0.07, p = 0.006). Channels with a spike‐gamma rate exceeding 1.9/min had an 80% probability of being in the EZ. Combining features did not improve the results. Interpretation: Resection of brain regions with high spike‐gamma rates in wakefulness is associated with a high probability of achieving seizure freedom. This rate could be applied to determine the minimal number of spiking channels requiring resection. In addition to quantitative analysis, this feature is easily accessible to visual analysis, which could aid clinicians during presurgical evaluation. ANN NEUROL 2023;93:522–535