Resting-state EEG measures cognitive impairment in Parkinson’s disease

Md Fahim Anjum, Arturo I. Espinoza, Rachel C. Cole, Arun Singh, Patrick May, Ergun Y. Uc, Soura Dasgupta, Nandakumar S. Narayanan

Research output: Contribution to journalArticlepeer-review

Abstract

Cognitive dysfunction is common in Parkinson’s disease (PD). We developed and evaluated an EEG-based biomarker to index cognitive functions in PD from a few minutes of resting-state EEG. We hypothesized that synchronous changes in EEG across the power spectrum can measure cognition. We optimized a data-driven algorithm to efficiently capture these changes and index cognitive function in 100 PD and 49 control participants. We compared our EEG-based cognitive index with the Montreal cognitive assessment (MoCA) and cognitive tests across different domains from National Institutes of Health (NIH) Toolbox using cross-validations, regression models, and randomization tests. Finally, we externally validated our approach on 32 PD participants. We observed cognition-related changes in EEG over multiple spectral rhythms. Utilizing only 8 best-performing electrodes, our proposed index strongly correlated with cognition (MoCA: rho = 0.68, p value < 0.001; NIH-Toolbox cognitive tests: rho ≥ 0.56, p value < 0.001) outperforming traditional spectral markers (rho = −0.30–0.37). The index showed a strong fit in regression models (R 2 = 0.46) with MoCA, yielded 80% accuracy in detecting cognitive impairment, and was effective in both PD and control participants. Notably, our approach was equally effective (rho = 0.68, p value < 0.001; MoCA) in out-of-sample testing. In summary, we introduced a computationally efficient data-driven approach for cross-domain cognition indexing using fewer than 10 EEG electrodes, potentially compatible with dynamic therapies like closed-loop neurostimulation. These results will inform next-generation neurophysiological biomarkers for monitoring cognition in PD and other neurological diseases.

Original languageEnglish (US)
Article number6
Journalnpj Parkinson's Disease
Volume10
Issue number1
DOIs
StatePublished - Dec 2024
Externally publishedYes

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Publisher Copyright:
© 2024, The Author(s).

PubMed: MeSH publication types

  • Journal Article

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