Department of Neurology and Institute of Neurology, The First Affiliated Hospital of Fujian Medical University, Fuzhou 350005, China; Department of Neurology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou 350212, China.
Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou 450000, Henan, China; NHC Key Laboratory of Prevention and Treatment of Cerebrovascular Diseases, Zhengzhou, Henan, China.
Neurobiol Dis. 2024 Nov;202:106692. doi: 10.1016/j.nbd.2024.106692. Epub 2024 Oct 5.
The neuropsychiatric symptoms are common in Wilson's disease (WD) patients. However, it remains unclear about the associated functional brain networks. In this study, source localization-based functional connectivity analysis of close-eye resting-state electroencephalography (EEG) were implemented to assess the characteristics of functional networks in 17 WD patients with neurological involvements and 17 healthy controls (HCs). The weighted phase-lag index (wPLI) was subsequently calculated in source space across five different frequency bands and the resulting connectivity matrix was transformed into a weighted graph whose structure was measured by five graphical analysis indicators, which were finally correlated with clinical scores. Compared to HCs, WD patients revealed disconnected sub-networks in delta, theta and alpha bands. Moreover, WD patients exhibited significantly reduced global clustering coefficients and small-worldness in all five frequency bands. In WD group, the severity of neurological symptoms and structural brain abnormalities were significantly correlated with disrupted functional networks. In conclusion, our study demonstrated that functional network deficits in WD can reflect the severity of their neurological symptoms and structural brain abnormalities. Resting-state EEG may be used as a marker of brain injury in WD.
神经精神症状在威尔逊病（WD）患者中很常见。然而，与功能性脑网络相关的问题仍不清楚。在这项研究中，我们对 17 名有神经受累的 WD 患者和 17 名健康对照者（HCs）进行了基于源定位的闭眼静息态脑电图（EEG）功能连接分析，以评估功能网络的特征。随后在源空间中计算了五个不同频带的加权相位滞后指数（wPLI），并将得到的连接矩阵转换为加权图，该图的结构由五个图形分析指标进行测量，最后与临床评分相关联。与 HCs 相比，WD 患者在 delta、theta 和 alpha 频段表现出不连续的子网络。此外，WD 患者在所有五个频段的全局聚类系数和小世界特征明显降低。在 WD 组中，神经症状的严重程度和结构脑异常与功能网络的破坏显著相关。总之，我们的研究表明，WD 中的功能网络缺陷可以反映其神经症状和结构脑异常的严重程度。静息态 EEG 可作为 WD 脑损伤的标志物。
