Meng Lin, Wang Deyu, Ma Jun, Shi Yu, Zhao Hongbo, Wang Yanlin, Shi Qingqing, Zhu Xiaodong, Ming Dong
Academy of Medical Engineering and Translational Medicine, Medical School, Faculty of Medicine, Tianjin University, Tianjin, China; Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration, Tianjin, China.
Academy of Medical Engineering and Translational Medicine, Medical School, Faculty of Medicine, Tianjin University, Tianjin, China.
Neurobiol Dis. 2025 Jun 15;210:106915. doi: 10.1016/j.nbd.2025.106915. Epub 2025 Apr 22.
Despite prior studies on early-stage Parkinson's disease (PD) brain connectivity and temporal patterns, differences between tremor-dominant (TD) and postural instability/gait difficulty (PIGD) motor subtypes remain poorly understood. Our study aims to understand the contribution of altered brain network dynamics to heterogeneous motor phenotypes in PD for improving personalized treatment.
Electroencephalography (EEG) microstate dynamics were firstly used to capture spatiotemporal brain network changes. A deep learning model was developed to classify PD motor subtypes where spatial variability and electrode location data were incorporated into the analysis.
Compared to healthy individuals, both PD-TD and PD-PIGD patients showed increased local segregation of brain regions. The PD-PIGD subtype had more severe and extensive disorganization in microstate A dynamics, suggesting greater disruption in auditory and motor-related networks. Incorporating spatial information significantly improved the accuracy of subtype classification, with an AUC of 0.972, indicating that EEG microstate dynamic spatial patterns reflect distinct PD motor pathologies. The increased spatial variability in the PD-PIGD group was more closely associated with motor impairments.
This study presents a novel framework for differentiating PD motor subtypes and emphasizes dynamic brain network features as potential markers for understanding motor symptom variability in PD, which may contribute to the development of personalized treatment strategies.
ChiCTR2300067657.
尽管先前已有关于早期帕金森病（PD）脑连接性和时间模式的研究，但震颤为主型（TD）和姿势不稳/步态障碍型（PIGD）运动亚型之间的差异仍知之甚少。我们的研究旨在了解脑网络动态改变对PD异质性运动表型的影响，以改善个性化治疗。
首先使用脑电图（EEG）微状态动力学来捕捉时空脑网络变化。开发了一种深度学习模型，将空间变异性和电极位置数据纳入分析，以对PD运动亚型进行分类。
与健康个体相比，PD-TD和PD-PIGD患者的脑区局部隔离均增加。PD-PIGD亚型在微状态A动力学方面有更严重和广泛的紊乱，表明听觉和运动相关网络受到更大干扰。纳入空间信息显著提高了亚型分类的准确性，曲线下面积（AUC）为0.972，表明EEG微状态动态空间模式反映了不同的PD运动病理。PD-PIGD组中增加的空间变异性与运动障碍的相关性更强。
本研究提出了一种区分PD运动亚型的新框架，并强调动态脑网络特征作为理解PD运动症状变异性的潜在标志物，这可能有助于制定个性化治疗策略。
ChiCTR2300067657。
