Hsieh Hsinyu, Xu Qiang, Yang Fang, Zhang Qirui, Hao Jingru, Liu Gaoping, Liu Ruoting, Yu Qianqian, Zhang Zixuan, Xing Wei, Bernhardt Boris C, Lu Guangming, Zhang Zhiqiang
Department of Diagnostic Radiology, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210093, China.
Department of Radiology, Third Affiliated Hospital of Soochow University/Changzhou First People's Hospital, Changzhou 213004, China.
J Clin Med. 2022 Mar 15;11(6):1612. doi: 10.3390/jcm11061612.
This study aimed to delineate cortico-striato-thalamo-cerebellar network profiles based on static and dynamic connectivity analysis in genetic generalized and focal epilepsies with generalized tonic-clonic seizures, and to evaluate its potential for distinguishing these two epilepsy syndromes. A total of 342 individuals participated in the study (114 patients with genetic generalized epilepsy with generalized tonic-clonic seizures (GE-GTCS), and 114 age- and sex-matched patients with focal epilepsy with focal to bilateral tonic-clonic seizure (FE-FBTS), 114 healthy controls). Resting-state fMRI data were examined through static and dynamic functional connectivity (dFC) analyses, constructing cortico-striato-thalamo-cerebellar networks. Network patterns were compared between groups, and were correlated to epilepsy duration. A pattern-learning algorithm was applied to network features for classifying both epilepsy syndromes. FE-FBTS and GE-GTCS both presented with altered functional connectivity in subregions of the motor/premotor and somatosensory networks. Among these two groups, the connectivity within the cerebellum increased in the static, while the dFC variability decreased; conversely, the connectivity of the thalamus decreased in FE-FBTS and increased in GE-GTCS in the static state. Connectivity differences between patient groups were mainly located in the thalamus and cerebellum, and correlated with epilepsy duration. Support vector machine (SVM) classification had accuracies of 66.67%, 68.42%, and 77.19% when using static, dynamic, and combined approaches to categorize GE-GTCS and FE-GTCS. Network features with high discriminative ability predominated in the thalamic and cerebellar connectivities. The network embedding of the thalamus and cerebellum likely plays an important differential role in GE-GTCS and FE-FBTS, and could serve as an imaging biomarker for differential diagnosis.
本研究旨在基于对伴有全身强直阵挛发作的遗传性全身性癫痫和局灶性癫痫进行静态和动态连接性分析，描绘皮质 - 纹状体 - 丘脑 - 小脑网络图谱，并评估其区分这两种癫痫综合征的潜力。共有342人参与了该研究（114例伴有全身强直阵挛发作的遗传性全身性癫痫患者（GE - GTCS）、114例年龄和性别匹配的伴有局灶性至双侧强直阵挛发作的局灶性癫痫患者（FE - FBTS）以及114名健康对照者）。通过静态和动态功能连接性（dFC）分析来检查静息态功能磁共振成像（fMRI）数据，构建皮质 - 纹状体 - 丘脑 - 小脑网络。比较了各组之间的网络模式，并将其与癫痫病程相关联。将一种模式学习算法应用于网络特征以对两种癫痫综合征进行分类。FE - FBTS和GE - GTCS在运动/运动前区和体感网络的子区域均表现出功能连接性改变。在这两组中，小脑内的连接性在静态状态下增加，而dFC变异性降低；相反，在静态状态下，FE - FBTS中丘脑的连接性降低，GE - GTCS中丘脑的连接性增加。患者组之间的连接性差异主要位于丘脑和小脑，并与癫痫病程相关。当使用静态、动态和联合方法对GE - GTCS和FE - GTCS进行分类时，支持向量机（SVM）分类的准确率分别为66.67%、68.42%和77.19%。具有高鉴别能力的网络特征在丘脑和小脑连接性中占主导地位。丘脑和小脑的网络嵌入可能在GE - GTCS和FE - FBTS中发挥重要的鉴别作用，并可作为鉴别诊断的影像学生物标志物。
