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

Md Fahim Anjum; Arturo Espinoza; Rachel Cole; Arun Singh; Patrick May; Ergun Uc; Soura Dasgupta; Nandakumar Narayanan

doi:10.21203/rs.3.rs-2666578/v1

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

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

Md Fahim Anjum, Arturo Espinoza, Rachel Cole, Arun Singh, Patrick May, Ergun Uc, Soura Dasgupta and Nandakumar Narayanan

Research square

03/20/2023

DOI: 10.21203/rs.3.rs-2666578/v1

PMCID: PMC10055637

PMID: 36993450

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https://doi.org/10.21203/rs.3.rs-2666578/v1View

Preprint (Author's original)This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

Abstract

Cognitive dysfunction is common in Parkinson's disease (PD) and is diagnosed by complex, time-consuming psychometric tests which are affected by language and education, subject to learning effects, and not suitable for continuous monitoring of cognition. 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 the National Institutes of Health (NIH) Toolbox using cross-validation schemes, regression models, and randomization tests. We observed cognition-related changes in EEG activities over multiple spectral rhythms. Utilizing only 8 best-performing EEG electrodes, our proposed index strongly correlated with cognition (rho = 0.68, value < 0.001 with MoCA; rho ≥ 0.56, value < 0.001 with cognitive tests from the NIH Toolbox) outperforming traditional spectral markers (rho = -0.30 - 0.37). The index showed a strong fit in regression models (R = 0.46) with MoCA, yielded 80% accuracy in detecting cognitive impairment, and was effective in both PD and control participants. Our approach is computationally efficient for real-time indexing of cognition across domains, implementable even in hardware with limited computing capabilities, making it potentially compatible with dynamic therapies such as closed-loop neurostimulation, and will inform next-generation neurophysiological biomarkers for monitoring cognition in PD and other neurological diseases.

Details

Title: Subtitle: Resting-state EEG measures cognitive impairment in Parkinson's disease
Creators: Md Fahim Anjum - University of California, San Francisco
Arturo Espinoza - University of Iowa
Rachel Cole - University of Iowa
Arun Singh - University of South Dakota
Patrick May - University of Iowa
Ergun Uc - University of Iowa
Soura Dasgupta - University of Iowa
Nandakumar Narayanan - University of Iowa
Resource Type: Preprint
Publication Details: Research square
DOI: 10.21203/rs.3.rs-2666578/v1
PMID: 36993450
PMCID: PMC10055637
Language: English
Date posted: 03/20/2023
Academic Unit: Neurology; Electrical and Computer Engineering; Iowa Neuroscience Institute
Record Identifier: 9984381024502771

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