EEG functional connectivity for Parkinson’s disease diagnosis and non-motor symptom prediction: a machine learning approach
TL;DR
The 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.
Abstract
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.