Modeling Spatial Synchronization of Sleep for Detection of Idiopathic REM Sleep Behavior Disorder

Area of Science:

• Neuroscience
• Sleep Medicine
• Computational Biology

Background:

• Idiopathic REM sleep behavioral disorder (iRBD) is a key prodromal stage of Parkinson's Disease (PD).
• Current diagnosis relies on resource-intensive polysomnography (PSG) visual analysis.
• There is a need for objective markers to assess iRBD severity and subtypes.

Purpose of the Study:

• To develop and validate a novel method for quantifying brain activity synchronization during sleep in iRBD.
• To explore the utility of hypnodensities and EEG coherence as diagnostic markers.
• To investigate potential EEG-based biomarkers for phenoconversion to PD.

Main Methods:

• Developed a method using hypnodensities (automatic sleep stage probability distributions) to analyze EEG synchronization.
• Employed epoch-wise correlation and dynamic time warping (DTW) to measure hypnodensity similarity across various epoch sizes.
• Assessed EEG coherence in specific frequency bands and utilized gradient-boosted decision trees for classification.

Main Results:

• The developed model achieved an AUC of 0.82, demonstrating significant diagnostic capability.
• Key features included increased gamma activity coherence during REM sleep and lower DTW distance of N1 probability in iRBD patients.
• Abnormal brain activity synchronization, measured by EEG coherence and hypnodensity similarity, was identified in iRBD.

Conclusions:

• Brain activity synchronization patterns during sleep are demonstrably altered in iRBD.
• The proposed EEG-based approach, utilizing coherence and hypnodensity similarity, shows promise as an additional diagnostic marker for iRBD.
• These findings could contribute to improved prognostic models for predicting phenoconversion to Parkinson's Disease.