Electroencephalographic Characterization by Covariance Analysis in Men with Parkinson’s Disease Reveals Sex- and Age-Related Differences

Abstract

Parkinson’s disease (PD) is the fastest growing neurological disease associated with ageing; its symptomatology varies between sexes. Several quantitative electroencephalography analyses have been used to study the early stages and progression of PD. In this study, we aim to characterize the brain activity by considering the five brainwaves in an eyes-closed resting state, using covariance wavelet analysis (CWA) of electroencephalographic records (EEGs) to analyze the influence of sex and age. To effectively eliminate artifacts from the EEG dataset and extract pertinent brain activity, we employ the inverse wavelet analysis. EEGs from men with PD were divided into two age groups (PD 60 years old) with their respective age-matched controls (CL). Brain activity patterns in frequency and power domains were analyzed with the CWA. Main frequency profiles, global wavelet curves, power anomalies, and power per brainwave were used to illustrate the CWA patterns. Power anomalies were used to generate anteroposterior power gradients. In PD α brainwave decreased, while the δ brainwave increased. The θ brainwave increased and was dominant over the α brainwave in PD > 60 men. The anteroposterior power gradient in PD 60 men, the anteroposterior gradient was negative. In PD > 60 men, the θ brainwave increased and became dominant. Men with PD had twice the θ brainwave increase. An inverse relationship between α and δ brainwaves was detected in a PD < 60 sex comparison. A conventional EEG spectral analysis using CWA indicated significant differences in brain activity patterns in the PD/CL groups affected by sex and age, yielding previously unknown information.

Keywords

• covariance wavelet analysis
• electroencephalographic records
• Parkinson’s disease
• brainwaves