Chen Xuemei, Zhang Xiao, Qin Bailing, Huang Dongying, Luo Cuimi, Huang Huachun, Zhou Qin, Chen Zirong, Zheng Jinou
Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Epilepsy Behav. 2025 Feb;163:110228. doi: 10.1016/j.yebeh.2024.110228. Epub 2024 Dec 26.
The fundamental pathophysiologic understanding of different seizure types in Temporal lobe epilepsy (TLE) remains unclear. This study aimed to assess the distinct alterations of structural network in TLE patients with different seizure types and their relationships with cognitive and psychiatric symptoms.
Seventy-three patients with unilateral TLE, including 25 with uncontrolled focal to bilateral tonic-clonic seizures (FBTCS), 25 with controlled FBTCS and 23 with focal impaired awareness seizures (FIAS), as well as 26 healthy controls (HC), underwent the diffusion tensor imaging (DTI) scan. Network-based statistic (NBS) and graph theory analyses were employed to investigate the structural network and its topological properties. Partial correlation analyses were conducted to examine the relationships between clinical variables and disrupted network characteristics. Additionally, the support vector machine (SVM) algorithm was utilized for the classification of controlled and uncontrolled FBTCS.
Compared to HC, TLE seizure type subgroups presented differently aberrant SC within the frontostriatal network. Additionally, alterations in the rich club organization and global network metrics were observed only in FBTCS. Notably, a significant decrease in all nodal metrics of the right amygdala were observed within the uncontrolled FBTCS group compared to the other three groups. Additionally, the disrupted nodal properties were significantly correlated with the age of onset, duration of epilepsy and psychiatric symptoms in FBTCS. Furthermore, the classifier achieved notably high accuracy (98%) in distinguishing between controlled and uncontrolled FBTCS.
Our findings may contribute to elucidating the neuropathological mechanisms of different seizure types in TLE and their impacts on cognitive and psychiatric status. SVM algorithm combined with nodal properties holds promise for predicting the poor seizure control of FBTCS.
颞叶癫痫（TLE）中不同发作类型的基本病理生理学机制仍不清楚。本研究旨在评估不同发作类型的TLE患者结构网络的独特改变及其与认知和精神症状的关系。
73例单侧TLE患者，包括25例有未控制的局灶性至双侧强直阵挛发作（FBTCS）、25例有控制的FBTCS和23例有局灶性意识障碍发作（FIAS），以及26名健康对照（HC），接受了扩散张量成像（DTI）扫描。采用基于网络的统计（NBS）和图论分析来研究结构网络及其拓扑特性。进行偏相关分析以检验临床变量与破坏的网络特征之间的关系。此外，支持向量机（SVM）算法用于区分有控制和无控制的FBTCS。
与HC相比，TLE发作类型亚组在前额叶纹状体网络内呈现出不同的异常结构连接。此外，仅在FBTCS中观察到富俱乐部组织和全局网络指标的改变。值得注意的是，与其他三组相比，在无控制的FBTCS组中右侧杏仁核的所有节点指标均显著降低。此外，在FBTCS中，破坏的节点特性与发病年龄、癫痫持续时间和精神症状显著相关。此外，该分类器在区分有控制和无控制的FBTCS方面取得了显著高的准确率（98%）。
我们的研究结果可能有助于阐明TLE中不同发作类型的神经病理机制及其对认知和精神状态的影响。SVM算法结合节点特性有望预测FBTCS的发作控制不佳。
