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.

中文翻译：

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基于立体脑电量化和自动聚类的前额叶癫痫分类

目的额叶癫痫是根据额叶的解剖功能细分组织的。迄今为止，前额叶癫痫发作一直是少数详细研究的主题。本研究的目的是根据癫痫发作量化确定前额叶癫痫发作的子类别，并寻找符号学差异。方法 2000~2018年连续接受立体脑电图（SEEG）治疗的耐药性前额叶癫痫患者。在我们的患者中研究的不同前额叶区域是背外侧前额叶皮层 (DLPFC)、腹外侧前额叶皮层 (VLPFC)、背内侧前额叶皮层 (DMPFC)、腹内侧前额叶皮层 (VMPFC) 和眶额叶皮层 (OFC)。癫痫发作区 (SOZ) 由每位患者的一两次癫痫发作确定，使用致癫痫指数（EI）方法。分析了 16 种临床发作表现的存在与否。使用 k-means 自动分类方法对前额叶网络进行分类。结果 共分析了 31 名患者的 51 次癫痫发作。最佳聚类是前额叶癫痫的 4 个亚组：“纯 DLPF”组、“纯 VMPF”组、“纯 OFC”组和“全局前额叶”组。前 3 组显示平均 EI 仅在一个主要结构中被认为是致癫痫 (>0.4)，而第四组在几乎所有前额叶结构中显示出高平均 EI。“全局前额叶”组每次癫痫发作（前额叶或额外）的致癫痫结构的中位数为 5 个，其他组为 2 个。我们发现最常见的迹象是意识改变、自动化/刻板印象、综合手势运动行为和多动性运动行为。我们发现不同组间发作征象的分布没有显着差异。结论我们的研究表明，虽然大多数前额叶癫痫表现为几个解剖学上不同结构的网络，但我们能够确定四组前额叶癫痫发作的亚叶组织。