Prefrontal seizure classification based on stereo-EEG quantification and automatic clustering - Archive ouverte HAL
Article Dans Une Revue Epilepsy & Behavior Année : 2020

Prefrontal seizure classification based on stereo-EEG quantification and automatic clustering

Résumé

Purpose: Frontal seizures are organized according to anatomo-functional subdivisions of the frontal lobe. Prefrontal seizures have been the subject of few detailed studies to date. The objective of this study was to identify subcategories of prefrontal seizures based on seizure onset quantification and to look for semiological differences. Methods: Consecutive patients who underwent stereoelectroencephalography (SEEG) for drug-resistant prefrontal epilepsy between 2000 and 2018 were included. The different prefrontal regions investigated in our patients were dorsolateral prefrontal cortex (DLPFC), ventrolateral prefrontal cortex (VLPFC), dorsomedial prefrontal cortex (DMPFC), ventromedial prefrontal cortex (VMPFC), and orbitofrontal cortex (OFC). The seizure onset zone (SOZ) was determined from one or two seizures in each patient, using the epileptogenicity index (EI) method. The presence or absence of 16 clinical ictal manifestations was analyzed. Classification of prefrontal networks was performed using the k-means automatic classification method. Results: A total of 51 seizures from 31 patients were analyzed. The optimal clustering was 4 subgroups of prefrontal seizures: a "pure DLPF" group, a "pure VMPF" group, a "pure OFC" group, and a "global prefrontal" group. The first 3 groups showed a mean EI considered epileptogenic (>0.4) only in one predominant structure, while the fourth group showed a high mean EI in almost all prefrontal structures. The median number of epileptogenic structures per seizure (prefrontal or extrafrontal) was 5 for the "global prefrontal" group and 2 for the other groups. We found that the most common signs were altered consciousness, automatisms/stereotypies, integrated gestural motor behavior, and hyperkinetic motor behavior. We found no significant difference in the distribution of ictal signs between the different groups. Conclusion: Our study showed that although most prefrontal seizures manifest as a network of several anatomically distinct structures, we were able to determine a sublobar organization of prefrontal seizure onset with four groups.

Domaines

Neurosciences
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Dates et versions

hal-03273628 , version 1 (07-09-2022)

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Stéphanie Machado, Francesca Bonini, Aileen Mcgonigal, Rinki Singh, Romain Carron, et al.. Prefrontal seizure classification based on stereo-EEG quantification and automatic clustering. Epilepsy & Behavior, 2020, 112, pp.107436. ⟨10.1016/j.yebeh.2020.107436⟩. ⟨hal-03273628⟩
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