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

Abstract

Background: 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. Objectives: We developed and evaluated an EEG-based biomarker to index cognitive functions in PD from a few minutes of resting-state EEG. Methods: 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. Results: 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, p value < 0.001 with MoCA; rho ≥ 0.56, p 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 (R2 = 0.46) with MoCA, yielded 80% accuracy in detecting cognitive impairment, and was effective in both PD and control participants. Conclusions: 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.

Salience network and cognitive impairment in Parkinson’s disease

“…Spearman’s correlations revealed significant relationships for MoCA and disease duration, age, and education (Table 2). We included variables that had a strong univariate relationship with MoCA in multivariate models of connectivity (Anjum et al, 2023; Dalrymple-Alford et al, 2010; Freitas et al, 2013; Gill et al, 2008; Hendershott et al, 2017; Kandiah et al, 2014; Litvan et al, 2012; Nasreddine et al, 2005; Singh et al, 2021; Zadikoff et al, 2008).…”

Pathobiology of Cognitive Impairment in Parkinson Disease: Challenges and Outlooks

“…The latter was associated with increased response latencies and decreased mid-frontal δ power across all tasks [315]. In general, cognition-related changes in EEG activities were observed over multiple spectral rhythms [316]. PDD patients showed decreased θ frequency in the temporal right-frontal left and temporal right-frontal-right regions while in the α frequency band.…”