Department of Neurology, University of Connecticut, Farmington, CT, 06070, USA.
Department of Neurology, UConn Health, 263 Farmington Avenue, Farmington, CT, 06030-5357, USA.
Sci Rep. 2022 Jul 22;12(1):12526. doi: 10.1038/s41598-022-16877-x.
Seizures likely result from aberrant network activity and synchronization. Changes in brain network connectivity may underlie seizure onset. We used a novel method of rapid network model estimation from intracranial electroencephalography (iEEG) data to characterize pre-ictal changes in network structure prior to seizure onset. We analyzed iEEG data from 20 patients from the iEEG.org database. Using 10 s epochs sliding by 1 s intervals, a multiple input, single output (MISO) state space model was estimated for each output channel and time point with all other channels as inputs, generating sequential directed network graphs of channel connectivity. These networks were assessed using degree and betweenness centrality. Both degree and betweenness increased at seizure onset zone (SOZ) channels 37.0 ± 2.8 s before seizure onset. Degree rose in all channels 8.2 ± 2.2 s prior to seizure onset, with increasing connections between the SOZ and surrounding channels. Interictal networks showed low and stable connectivity. A novel MISO model-based network estimation method identified changes in brain network structure just prior to seizure onset. Increased connectivity was initially isolated within the SOZ and spread to non-SOZ channels before electrographic seizure onset. Such models could help confirm localization of SOZ regions.
癫痫发作可能是由于异常的网络活动和同步引起的。脑网络连接的变化可能是癫痫发作的基础。我们使用一种从颅内脑电图 (iEEG) 数据中快速估计网络模型的新方法，以描述癫痫发作前网络结构的变化。我们分析了 iEEG.org 数据库中 20 名患者的 iEEG 数据。使用 10 秒的时间窗，以 1 秒的间隔滑动，对于每个输出通道和时间点，使用所有其他通道作为输入，对通道连接进行了多次输入、单输出 (MISO) 状态空间模型估计，生成了顺序有向网络图。使用度和介数来评估这些网络。在癫痫发作前 37.0 ± 2.8 秒，在癫痫发作起始区 (SOZ) 通道中，度和介数都增加了。在癫痫发作前 8.2 ± 2.2 秒，所有通道的度都增加了，SOZ 和周围通道之间的连接增加。发作间期网络显示出低且稳定的连通性。一种新的基于 MISO 模型的网络估计方法可以在癫痫发作前识别脑网络结构的变化。连通性的增加最初局限于 SOZ，并在电描记图癫痫发作前扩散到非 SOZ 通道。这种模型可以帮助确认 SOZ 区域的定位。
