Large-scale network metrics improve the classification performance of rapid-eye-movement sleep behavior disorder patients

Abstract

Clinical decision support systems based on machine-learning algorithms are largely applied in the context of the diagnosis of neurodegenerative diseases (NDDs). While recent models yield robust classifications in supervised two classes-problems accurately separating Parkinson's disease (PD) from healthy control (HC) subjects, few works looked at prodromal stages of NDDs. Idiopathic Rapid-eye Movement (REM) sleep behavior disorder (iRBD) is considered a prodromal stage of PD with a high chance of phenoconversion but with heterogeneous symptoms that hinder accurate disease prediction. Machine learning (ML) based methods can be used to develop personalized trajectory models, but these require large amounts of observational points with homogenous features significantly reducing the possible imaging modalities to non-invasive and cost-effective techniques such as high-density electrophysiology (hdEEG). In this work, we aimed at quantifying the increase in accuracy and robustness of the classification model with the inclusion of network-based metrics compared to the classical Fourier-based power spectral density (PSD). We performed a series of analyses to quantify significance in cohort-wise metrics, the performance of classification tasks, and the effect of feature selection on model accuracy. We report that amplitude correlation spectral profiles show the largest difference between iRBD and HC subjects mainly in delta and theta bands. Moreover, the inclusion of amplitude correlation and phase synchronization improves the classification performance by up to 11% compared to using PSD alone. Our results show that hdEEG features alone can be used as potential biomarkers in classification problems using iRBD data and that large-scale network metrics improve the performance of the model. This evidence suggests that large-scale brain network metrics should be considered important tools for investigating prodromal stages of NDD as they yield more information without harming the patient, allowing for constant and frequent longitudinal evaluation of patients at high risk of phenoconversion.

EEG-based Machine Learning Models for the Prediction of Phenoconversion Time and Subtype in iRBD

“…The copyright holder for this preprint this version posted September 5, 2023. ; https://doi.org/10.1101/2023.09.04.23294964 doi: medRxiv preprint it has been suggested that metrics describing network interactions of large-scale inter-areal synchronization between brain oscillations could improve classification accuracy in iRBD patients. 30 Accordingly, overall functional connectivity for each frequency band was extracted by averaging the wPLI values of all 1770 electrode pairs. 8,31 Furthermore, Shannon entropy (SE) was defined with 10 bins of amplitude values.…”