Abstract
Background: Quantitative electroencephalography (qEEG) has been suggested as a biomarker for cognitive decline in Parkinson’s disease (PD).Objective: Determine if applying a wavelet-based qEEG algorithm to 21-electrode, resting-state EEG recordings obtained in a routine clinical setting has utility for predicting cognitive impairment in PD.Methods: PD subjects, evaluated by disease stage and motor score, were compared to healthy controls (N = 20 each). PD subjects with normal (PDN, MoCA 26–30, N = 6) and impaired (PDD, MoCA ≤ 25, N = 14) cognition were compared. The wavelet-transform based time-frequency algorithm assessed the instantaneous predominant frequency (IPF) at 60 ms intervals throughout entire recordings. We then determined the relative time spent by the IPF in the four standard EEG frequency bands (RTF) at each scalp location. The resting occipital rhythm (ROR) was assessed using standard power spectral analysis.Results: Comparing PD subjects to healthy controls, mean values are decreased for ROR and RTF-Beta, greater for RTF-Theta and similar for RTF-Delta and RTF-Alpha. In logistic regression models, arithmetic combinations of RTF values [e.g., (RTF-Alpha) + (RTF-Beta)/(RTF-Delta + RTF-Theta)] and RTF-Alpha values at occipital or parietal locations are most able to discriminate between PD and controls. A principal component (PC) from principal component analysis (PCA) using RTF-band values in all subjects is associated with PD status (p = 0.004, β = 0.31, AUC = 0.780). Its loadings show positive contribution from RTF-Theta at all scalp locations, and negative contributions from RTF-Beta at occipital, parietal, central, and temporal locations. Compared to cognitively normal PD subjects, cognitively impaired PD subjects have lower median RTF-Alpha and RTF-Beta values, greater RTF-Theta values and similar RTF-Delta values. A PC from PCA using RTF-band values in PD subjects is associated with cognitive status (p = 0.002, β = 0.922, AUC = 0.89). Its loadings show positive contributions from RTF-Theta at all scalp locations, negative contributions from RTF-Beta at central locations, and negative contributions from RTF-Delta at central, frontal and temporal locations. Age, disease duration and/or sex are not significant covariates. No PC was associated with motor score or disease stage.Significance: Analyzing standard EEG recordings obtained in a community practice setting using a wavelet-based qEEG algorithm shows promise as a PD biomarker and for predicting cognitive impairment in PD.
Highlights
Parkinson’s disease (PD) is characterized by the presence of cardinal motor symptoms including resting tremor, rigidity, bradykinesia and postural instability
relative time of this frequency (RTF)-Band Values Are Associated With Parkinson’s Disease and Control Status We initially evaluated whether mean and median RTF-band values at all scalp locations were different between control and PD subjects
We found that A.PC2 is associated with PD and control status (model p = 0.0037; β = 0.31, 95% Confidence interval (CI): 0.037, 0.59; A.PC2 p value: 0.027; Intercept not significantly different from zero; McFadden’s adjusted R2 = 0.280; Goodness-of-fit-test Pearson’s χ2 = 46.85, p = 0.154; Link test: pass; Aikake information criterion (AIC) = 51.02; Area under ROC curve (AUC): 0.780; Classification: 70% correct, 60% sensitivity, 80% specificity) (Figures 4B,C)
Summary
Parkinson’s disease (PD) is characterized by the presence of cardinal motor symptoms including resting tremor, rigidity, bradykinesia and postural instability. Non-motor symptoms including cognitive impairment, anxiety, depression, REM-sleepbehavior disorder, olfactory dysfunction, autonomic dysfunction, orthostatic hypotension, urinary incontinence and constipation are common and often precede the onset of motor symptoms. Cognitive impairment becomes more prevalent as the disease progresses and dementia often develops (Aarsland and Kurz, 2010). The prevalence of dementia in PD may be as high as 75% for disease duration greater than 10 years (Hely et al, 2008). Analyses of non-stationary EEG data, data where the statistical characteristics change with time, using quantitative electroencephalography (qEEG) methods have been investigated for their ability to distinguish the parkinsonian state and/or predict dementia in PD. Quantitative electroencephalography (qEEG) has been suggested as a biomarker for cognitive decline in Parkinson’s disease (PD)
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