ArticleFrontiers in human neuroscience2026
EEG functional connectivity for Parkinson's disease diagnosis and non-motor symptom prediction: a machine learning approach.
Article in Frontiers in human neuroscience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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 ( Results: The accuracy of the beta band model combining the four states reached 82.14% ( 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.
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