失眠症
功能连接
神经科学
心理学
慢性失眠
物理医学与康复
认知心理学
医学
计算机科学
精神科
睡眠障碍
作者
Jingwen Li,Shumei Li,Shaoqin Zeng,Xinzhi Wang,Mengchen Liu,Guang Xu,Xiaofen Ma
出处
期刊:Research Square - Research Square
日期:2024-04-29
标识
DOI:10.21203/rs.3.rs-4278831/v1
摘要
Abstract Background: Several studies have revealed altered intrinsic neural activity in chronic insomnia (CI). However, the temporal variability of intrinsic neural activity in CI is rarely mentioned. This study aimed to explore static and temporal dynamic alterations of regional homogeneity (ReHo) in CI and excavate the potential associations between these changes and clinical characteristics. Methods: Eighty-seven patients with CI and seventy-eight healthy controls (HCs) were included. Resting-state functional magnetic resonance imaging was performed on all subjects and both static and dynamic ReHo were used to detect local functional connectivity. We then tested the relationship between altered brain regions, disease duration, and clinical scales. The receiver operating characteristic curve analysis was used to reveal the potential capability of these indicators to screen CI patients from HCs. Results: CI showed increased dynamic ReHo in the right precuneus and decreased static ReHo in the right cerebellum_6. The dynamic ReHo values of the right precuneus were negatively correlated with the self-rating depression score and the static ReHo values of the right cerebellum_6 were positively correlated with the Montreal Cognitive Assessment-Naming scor e. In addition, the combination of the two metrics showed a potential capacity to distinguish CI patients from HCs, which was better than a single metric alone. Conclusions: The present study has revealed the altered local functional connectivity under static and temporal dynamic conditions in patients with CI, and found the relationships between these changes, mood-related scales, and cognitive-related scales. These may be useful in elucidating the neurological mechanisms of CI and accompanying symptoms.
科研通智能强力驱动
Strongly Powered by AbleSci AI