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阵发性运动诱发性运动障碍的脑电图特征。

An Electroencephalography Profile of Paroxysmal Kinesigenic Dyskinesia.

机构信息

Department of Neurology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China.

Shanghai Key Laboratory of Psychotic Disorders, Brain Health Institute, National Center for Mental Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China.

出版信息

Adv Sci (Weinh). 2024 Mar;11(12):e2306321. doi: 10.1002/advs.202306321. Epub 2024 Jan 16.

Abstract

Paroxysmal kinesigenic dyskinesia (PKD) is associated with a disturbance of neural circuit and network activities, while its neurophysiological characteristics have not been fully elucidated. This study utilized the high-density electroencephalogram (hd-EEG) signals to detect abnormal brain activity of PKD and provide a neural biomarker for its clinical diagnosis and PKD progression monitoring. The resting hd-EEGs are recorded from two independent datasets and then source-localized for measuring the oscillatory activities and function connectivity (FC) patterns of cortical and subcortical regions. The abnormal elevation of theta oscillation in wildly brain regions represents the most remarkable physiological feature for PKD and these changes returned to healthy control level in remission patients. Another remarkable feature of PKD is the decreased high-gamma FCs in non-remission patients. Subtype analyses report that increased theta oscillations may be related to the emotional factors of PKD, while the decreased high-gamma FCs are related to the motor symptoms. Finally, the authors established connectome-based predictive modelling and successfully identified the remission state in PKD patients in dataset 1 and dataset 2. The findings establish a clinically relevant electroencephalography profile of PKD and indicate that hd-EEG can provide robust neural biomarkers to evaluate the prognosis of PKD.

摘要

发作性运动诱发性运动障碍(PKD)与神经回路和网络活动的紊乱有关,但其神经生理特征尚未完全阐明。本研究利用高密度脑电图(hd-EEG)信号来检测 PKD 的异常脑活动,并为其临床诊断和 PKD 进展监测提供神经生物标志物。静息态 hd-EEG 分别来自两个独立的数据集,然后进行源定位以测量皮质和皮质下区域的振荡活动和功能连接(FC)模式。在广泛的脑区中,θ振荡的异常升高代表了 PKD 的最显著生理特征,这些变化在缓解患者中恢复到健康对照组水平。PKD 的另一个显著特征是未缓解患者的高γ FC 减少。亚型分析报告说,增加的θ振荡可能与 PKD 的情绪因素有关,而减少的高γ FC 与运动症状有关。最后,作者建立了基于连接组的预测模型,并成功地在数据集 1 和数据集 2 中识别了 PKD 患者的缓解状态。这些发现确立了 PKD 的临床相关脑电图特征,并表明 hd-EEG 可以提供强大的神经生物标志物来评估 PKD 的预后。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d616/10966565/e50e7601ca62/ADVS-11-2306321-g006.jpg

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