Data Sheet 1_EEG functional connectivity for Parkinson’s disease diagnosis and non-motor symptom prediction: a machine learning approach.docx

Functional connectivity measured by electroencephalography (EEG) can noninvasively map abnormalities at the network level.</p>Methods<p>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.</p>Results<p>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.</p>Discussion<p>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.</p>