# Longitudinal resting-state EEG-based modeling predicts phenoconversion and delineates heterogeneity in isolated REM sleep behavior disorder

## TL;DR

This study uses EEG data to identify subtypes of REM sleep behavior disorder and predict which patients are likely to develop Lewy body diseases.

## Contribution

The study introduces longitudinal EEG-based modeling to predict phenoconversion and delineate heterogeneity in REM sleep behavior disorder.

## Key findings

- Two subtypes of REM sleep behavior disorder were identified with distinct EEG patterns and progression risks.
- Subtype 2 showed progressive cognitive decline and higher phenoconversion risk within 7.5 years.
- Longitudinal EEG modeling improved prediction of neurodegenerative progression in prodromal patients.

## Abstract

Isolated REM sleep behavior disorder is a high-risk prodromal syndrome for Lewy body diseases but highly heterogeneous. Neurophysiologic markers resolving this heterogeneity and predicting phenoconversion remain limited. We applied Subtype and Stage Inference (SuStaIn) modeling to longitudinal resting-state EEG from a prospective cohort of 285 participants. SuStaIn identified two subtypes: Subtype 1, a posterior beta–dominant, largely non-progressive phenotype associated with higher educational attainment and absence of phenoconversion, and Subtype 2, a frontal slow-wave–dominant phenotype characterized by progressive delta/theta increases and cognitive decline. Longitudinal validation demonstrated substantial subtype shifting in Subtype 1 and stage advancement predominantly in Subtype 2. In Kaplan–Meier analysis, subtype shifting and accelerated annual stage advancement (ΔStage ≥ 2) identified individuals at exceptionally high phenoconversion risk with survival approaching 0–15% by 7.5 years. These findings extend SuStaIn beyond cross-sectional inference by incorporating longitudinal subtype-shifting and stage-advancement features to stratify prodromal heterogeneity and predict neurodegenerative progression.