Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213, USA.
Department of Neurobiology, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, 310013, P. R. China.
Adv Sci (Weinh). 2022 Jun;9(18):e2200887. doi: 10.1002/advs.202200887. Epub 2022 May 12.
Localization of epileptogenic zone currently requires prolonged intracranial recordings to capture seizure, which may take days to weeks. The authors developed a novel method to identify the seizure onset zone (SOZ) and predict seizure outcome using short-time resting-state stereotacticelectroencephalography (SEEG) data. In a cohort of 27 drug-resistant epilepsy patients, the authors estimated the information flow via directional connectivity and inferred the excitation-inhibition ratio from the 1/f power slope. They hypothesized that the antagonism of information flow at multiple frequencies between SOZ and non-SOZ underlying the relatively stable epilepsy resting state could be related to the disrupted excitation-inhibition balance. They found flatter 1/f power slope in non-SOZ regions compared to the SOZ, with dominant information flow from non-SOZ to SOZ regions. Greater differences in resting-state information flow between SOZ and non-SOZ regions are associated with favorable seizure outcome. By integrating a balanced random forest model with resting-state connectivity, their method localized the SOZ with an accuracy of 88% and predicted the seizure outcome with an accuracy of 92% using clinically determined SOZ. Overall, this study suggests that brief resting-state SEEG data can significantly facilitate the identification of SOZ and may eventually predict seizure outcomes without requiring long-term ictal recordings.
目前,癫痫灶的定位需要长时间的颅内记录来捕捉发作,可能需要数天到数周的时间。作者开发了一种新的方法,使用短时间的静息状态立体脑电图(SEEG)数据来识别癫痫起始区(SOZ)并预测癫痫发作结果。在 27 例耐药性癫痫患者的队列中,作者通过定向连通性估计信息流,并从 1/f 功率斜率推断出兴奋-抑制比。他们假设,在相对稳定的癫痫静息状态下,SOZ 和非 SOZ 之间多个频率的信息流拮抗可能与兴奋-抑制平衡的破坏有关。他们发现,与 SOZ 相比,非 SOZ 区域的 1/f 功率斜率更平坦,非 SOZ 区域向 SOZ 区域的信息流占主导地位。SOZ 和非 SOZ 区域之间静息状态信息流的差异越大,癫痫发作结果越好。通过将平衡随机森林模型与静息状态连通性相结合,他们的方法以 88%的准确率定位 SOZ,并以 92%的准确率预测临床确定的 SOZ 的癫痫发作结果。总的来说,这项研究表明,短暂的静息状态 SEEG 数据可以显著促进 SOZ 的识别,并最终无需长期发作记录即可预测癫痫发作结果。