Spectral features of resting-state EEG in Parkinson's Disease: A multicenter study using functional data analysis

Objective: This study aims 1) To analyse differences in resting-state electroencephalogram (rs-EEG) spec-tral features of Parkinson's Disease (PD) and healthy subjects (non-PD) using Functional Data Analysis (FDA) and 2) To explore, in four independent cohorts, the external validity and reproducibility of the find-ings using both epoch-to-epoch FDA and averaged-epochs approach. Methods: We included 169 subjects (85 non-PD; 84 PD) from four centres. Rs-EEG signals were prepro-cessed with a combination of automated pipelines. Sensor-level relative power spectral density (PSD), dominant frequency (DF), and DF variability (DFV) features were extracted. Differences in each feature were compared between PD and non-PD on averaged epochs and using FDA to model the epoch-to -epoch change of each feature. Results: For averaged epochs, significantly higher theta relative PSD in PD was found across all datasets. Also, higher pre-alpha relative PSD was observed in three of four datasets in PD patients. For FDA, similar

Automated summary

The objective of this study is to analyze the differences in resting-state electroencephalogram (rs-EEG) spectral features between Parkinson's Disease (PD) and healthy subjects using Functional Data Analysis (FDA), and to explore the external validity and reproducibility of the findings in four independent cohorts. The study included 169 subjects from four centers, and differences in features were compared using both averaged epochs and epoch-to-epoch FDA models. The results showed significantly higher theta relative power spectral density in PD patients across all datasets, as well as higher pre-alpha relative power spectral density in three out of four datasets.