Dynamic and topological properties of large-scale brain networks in rapid eye movement behavior disorder

Abstract

Introduction: There is a lack of research in the existing literature when it comes to analyzing the dynamics of resting-state functional magnetic resonance imaging to understand the underlying mechanisms of isolated rapid eye movement sleep behavior disorder (iRBD). This study aims to contribute to our understanding of abnormalities in brain network dynamics in iRBD and their association with alpha-synucleinopathy. Additionally, I employed graph theoretical metrics to obtain a topological insight into the brain network of iRBD. Methods: Resting-state fMRI data from 55 iRBD patients and 97 healthy controls (HCs) were utilized. A sliding window approach, functional connectivity analysis, and graph theory analysis were applied to the data. I calculated the mean, standard deviation, skewness, and kurtosis of the time series for both dynamic functional connectivity (dFC) and four graph metrics (clustering coefficient, global efficiency, assortativity coefficients, and eigenvector centrality). Subsequently, I compared the those metrices between iRBDs and HCs. Relationships between clinical scales and abnormal dFC were assessed using a general linear model. Results: iRBD patients exhibited abnormal mean dFC, particularly in the default mode network, sensorimotor network, basal ganglia network, and cerebellum. Kurtosis of dFC revealed abnormalities between the middle temporal gyrus and cerebellum. Group differences were also observed in the mean eigenvector centrality of the precentral gyrus and thalamus. Conclusion: The mean of dFC identified impairments putatively in movement functions and various compensatory mechanisms. Moreover, mean eigenvector centrality revealed topological changes in motor-related network in iRBDs. The use of kurtosis as a potential index for extracting dynamic information may provide additional insights into pathophysiology in iRBDs.