Resting-State EEG Biomarkers of Cognitive Impairment in Parkinson’s Disease: A Theory-Driven and Interpretable Model

Overview

Parkinson’s Disease (PD) is a chronic neurodegenerative disorder affecting nearly 12 million people worldwide, causing >329,000 deaths annually, with up to 80% of cases strongly influenced by cognitive impairment and PD dementia (PDD). Cognitive dysfunction impacts ~80% of individuals with PD, with an 8-year cumulative PDD prevalence of ~78% and mortality rates 3–4× higher than controls. Current subjective cognitive assessment is insensitive, detecting only ~25% of mild impairment cases. This study presents a novel, theory-driven machine learning framework, leveraging biological resting-state electroencephalogram (EEG) features and model interpretability to detect cognitive impairment in PD. Resting-state EEG recordings from 149 participants (100 PD, 49 demographically similar healthy controls) were analyzed. Cognitive status was assessed using the Montreal Cognitive Assessment. EEG signals (2–5 minutes) were preprocessed and decomposed into 35 neurophysiologically and statistically significant spectral (1-45 Hz), time–frequency, connectivity, entropy, and microstate-based features using Power Spectral Density, Fast Fourier Transform, Discrete Wavelet Transform, and functional connectivity metrics. These features correspond to cognitive domains and known neural processes, including frontal control, network coupling/synchronization, slowing rhythms, and microstate instability. Supervised predictive models were trained to distinguish PD- cognitively impaired from unimpaired. Repeated 5×5-fold cross-validation yielded a mean AUC of 0.810 and accuracy of 73% using 5 features. The maximum fold-level performance observed was an AUC of .960 with 85% accuracy. Interpretability analyses identified the frequency bands, cortical regions, and network interactions driving predictions, demonstrating that theory-driven EEG features can serve as biologically interpretable biomarkers of cognitive impairment in PD with translational relevance.

Competition history

• CSEF 2026 Behavioral & Social Sciences (Senior Division) · Entry S-03-17

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