Authors
Angerbauer,, R., Piet,, M., Choi, K., Park,, S.O., Jung, Park, K. &., & A.J.
https://doi.org/10.1093/sleepadvances/zpaf081Abstract
Idiopathic rapid eye movement (REM) sleep behavior disorder (iRBD) is characterized by the loss of atonia during REM sleep, causing dream-enacting behavior. Although iRBD occurs without any clear signs of neurodegenerative disorders, most patients with iRBD eventually develop Parkinson’s disease or dementia with Lewy bodies, typically accompanied by cognitive decline. Hence, identifying a biomarker that reflects a neurophysiological state of iRBD has therapeutic potential. Here, we show that spatially clustered alpha (8-12 Hz) oscillatory activities in the scalp can predict cognitive performance in patients with iRBD. A cohort of 62 Korean patients with iRBD underwent resting-state electroencephalography (rsEEG) recordings and participated in the Montreal Cognitive Assessment (MoCA) tests, a common measure for cognitive function. Spectral analysis of the rsEEG data revealed that overall power and transient bursting parameters of alpha activity negatively correlated with MoCA test scores. These results accounted for potential confounding factors such as the spatial distribution of the electrodes, age, sex, emotional states, and medication use. This finding was specific to the alpha activity because theta (4-8 Hz) and beta (12-30 Hz) oscillatory activities were not correlated with the cognitive test scores. Thus, these results suggest that clustered resting-state alpha activity is associated with cognitive impairments in iRBD. Our findings emphasize the importance of rsEEG dynamics in cognitive assessment and highlight the potential utility of rsEEG as an early biomarker for cognitive decline in iRBD patients. Keywords iRBD, EEG, alpha power, alpha burst, MoCA Statement of Significance It is crucial to identify reliable neurophysiological biomarkers for cognitive decline in iRBD, as it often precedes Parkinson’s disease and Lewy body dementia. Previous EEG studies have yielded inconsistent results, especially due to the lack of control for confounding factors and inadequate correction for spatial correlations between electrodes. This study overcame these limitations by integrating both static and transient EEG metrics, applying cluster-based spatial correction, and statistically controlling for age, sex, medication use and the coincidence of depression. We demonstrated that spatially clustered alpha activity negatively correlates with cognitive performance, potentially offering a novel biomarker for cognitive decline in iRBD. These findings can improve early diagnostics for cognitive decline and guide future research into neuroprotective strategies for individuals with iRBD.