Analysis of clinical difference in Parkinson’s disease subtype based on non-parametric brain network

doi:10.3785/j.issn.1008-973X.2025.11.024

Journal of ZheJiang University (Engineering Science)

2025, Vol. 59

Issue (11): 2451-2458 DOI: 10.3785/j.issn.1008-973X.2025.11.024

Analysis of clinical difference in Parkinson’s disease subtype based on non-parametric brain network

Shuo YANG1,2,3(

),Xu LOU1,3,Shuo LIU1,3,Jiarui LI1,3,Lei WANG1,2,3,*(

)

1. School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300130, China
2. State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China
3. Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300130, China

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Abstract

The resting-state EEG and brain network characteristics of different Parkinson’s disease subtypes were analyzed using a non-parametric approach. Non-parametric brain networks were computed from wavelet-based Granger causality within θ, individualized α (IAF), and β bands. Leverage centrality and efficiency density were computed and discussed in relation to clinical manifestations. The postural-instability and gait-difficulty (PIGD) subtype exhibited elevated leverage centrality in frontal and occipital cortices, signifying enhanced information-processing efficiency. Network efficiency within the IAF band was significantly negatively correlated with psychiatric and behavioral manifestations in the PIGD subtype. The tremor-dominant (TD) subtype exhibited higher efficiency density than the PIGD subtype across all frequency bands. Leverage centrality was significantly positively correlated with activities of daily living within the β band. The indeterminate (IT) subtype showed no significant differences in leverage centrality or efficiency density across brain regions and frequency bands, indicating relatively high consistency. The observed clinical heterogeneity across Parkinson’s disease subtypes is closely associated with aberrant individualized EEG signature and abnormal brain network activity.

Key words： brain network EEG feature extraction leverage centrality Parkinson's disease subtype

Received: 12 February 2025 Published: 30 October 2025

CLC:

R 318

Fund: 国家自然科学基金资助项目（52320105008，51877067）；河北省重点研发计划资助项目（21372002D）.

Corresponding Authors: Lei WANG E-mail: sureyang@126.com;wanglei@hebut.edu.cn

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Cite this article:

Shuo YANG,Xu LOU,Shuo LIU,Jiarui LI,Lei WANG. Analysis of clinical difference in Parkinson’s disease subtype based on non-parametric brain network. Journal of ZheJiang University (Engineering Science), 2025, 59(11): 2451-2458.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2025.11.024 OR https://www.zjujournals.com/eng/Y2025/V59/I11/2451

基于非参数脑网络的帕金森亚型临床差异分析

通过非参数方法分析帕金森病不同亚型患者的静息态脑电的脑网络特征. 基于θ、个体化α（IAF）以及β频段的小波变换格兰杰因果构建非参数脑网络，提取杠杆中心性和效率密度，探讨其与临床表现的关联. 研究发现，姿势不稳和步态困难型（PIGD）在额叶和枕叶的杠杆中心性较高，体现出显著的信息处理优势；PIGD亚型在IAF频段的网络性能与精神和行为表现呈显著负相关. 震颤主导型（TD）的效率密度在全频段优于PIGD亚型，且在β频段，杠杆中心性与日常生活能力显著正相关. 不确定型（IT）在不同脑区和频段间的杠杆中心性和效率密度无明显差异，表现出较高的一致性. 结果表明，不同帕金森病亚型的临床表现差异与个体化脑电特征及脑网络活动的异常密切相关.

关键词： 脑网络, 脑电图, 特征提取, 杠杆中心性, 帕金森亚型

Tab.1 Clinical scale for patient with different Parkinson's disease subtype

Fig.1 Resting-state EEG power spectra and individualized alpha peak frequency in different Parkinson’s disease subtype

Fig.2 Quality index

Fig.3 Efficiency density difference among Parkinson’s disease subtypes at different frequency bands

Fig.4 Leverage centrality difference in different brain regions and frequency bands among Parkinson’s disease subtypes

Fig.5 Correlation between clinical scales and brain network characteristics


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