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EEG functional connectivity for Parkinson's disease diagnosis and non-motor symptom prediction: a machine learning
Fanzun Meng1, Shuo Liu1, Xiaoyu Xiao1
1Department of Geriatric Medicine & Laboratory of Gerontology and Anti-Aging Research, Qilu Hospital of Shandong University, Jinan, Shandong, China.
Introduction:
Parkinson's disease (PD) is associated with severe non-motor symptoms, including mood disorders and sleep disturbances, whose neural mechanisms are linked to dysfunction in large-scale brain networks. Functional connectivity measured by electroencephalography (EEG) can noninvasively map abnormalities at the network level.
Methods:
This study employed the debiased weighted phase lag index (dwPLI) to mitigate the effects of volumetric conductivity and investigated EEG functional connectivity in PD patients (n = 14) and healthy controls (n = 14) during resting and emotional states. Diagnostic performance was evaluated using L1-regularized logistic regression combined with a strict subject-level leave-one-subject-out cross-validation method.
Results:
The accuracy of the beta band model combining the four states reached 82.14% (p < 0.05, permutation test), while the accuracy of the gamma band model under the eyes-open state reached 89.29% (p < 0.01). Furthermore, theta band connectivity during the sadness state significantly predicted PSQI scores in PD patients.
Discussion:
These findings suggest that emotion-induced network dynamics may reflect sleep disturbances in PD, and establish EEG functional connectivity as a candidate neurophysiological biomarker for PD, with potential applications in both diagnostic classification and the assessment of non-motor symptoms, although validation in larger cohorts is still required.
