Department of Neurology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea.
Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.
Sci Rep. 2023 Jun 5;13(1):9146. doi: 10.1038/s41598-023-36415-7.
We compared neural activities and network properties between the antihistamine-induced seizures (AIS) and seizure-free groups, with the hypothesis that patients with AIS might have inherently increased neural activities and network properties that are easily synchronized. Resting-state electroencephalography (EEG) data were collected from 27 AIS patients and 30 healthy adults who had never had a seizure. Power spectral density analysis was used to compare neural activities in each localized region. Functional connectivity (FC) was measured using coherence, and graph theoretical analyses were performed to compare network properties between the groups. Machine learning algorithms were applied using measurements found to be different between the groups in the EEG analyses as input features. Compared with the seizure-free group, the AIS group showed a higher spectral power in the entire regions of the delta, theta, and beta bands, as well as in the frontal areas of the alpha band. The AIS group had a higher overall FC strength, as well as a shorter characteristic path length in the theta band and higher global efficiency, local efficiency, and clustering coefficient in the beta band than the seizure-free group. The Support Vector Machine, k-Nearest Neighbor, and Random Forest models distinguished the AIS group from the seizure-free group with a high accuracy of more than 99%. The AIS group had seizure susceptibility considering both regional neural activities and functional network properties. Our findings provide insights into the underlying pathophysiological mechanisms of AIS and may be useful for the differential diagnosis of new-onset seizures in the clinical setting.
我们比较了抗组胺药诱发癫痫发作(AIS)和无癫痫发作组之间的神经活动和网络特性,假设 AIS 患者可能具有固有增加的神经活动和易于同步的网络特性。从 27 名 AIS 患者和 30 名从未有过癫痫发作的健康成年人中收集静息态脑电图(EEG)数据。使用功率谱密度分析比较每个局部区域的神经活动。使用相干性测量功能连接(FC),并进行图论分析比较组间的网络特性。使用 EEG 分析中发现的组间不同的测量值作为输入特征,应用机器学习算法。与无癫痫发作组相比,AIS 组在 delta、theta 和 beta 波段的整个区域以及 alpha 波段的额叶区域显示出更高的频谱功率。AIS 组的整体 FC 强度更高,theta 波段的特征路径长度更短,beta 波段的全局效率、局部效率和聚类系数更高。支持向量机、k-最近邻和随机森林模型以超过 99%的高精度将 AIS 组与无癫痫发作组区分开来。AIS 组考虑到区域神经活动和功能网络特性,具有癫痫易感性。我们的研究结果为 AIS 的潜在病理生理机制提供了深入了解,并可能有助于临床环境中新发癫痫的鉴别诊断。