1. Altered cingulate gyrus subregions functional connectivity in chronic insomnia disorder with anxiety.
- Author
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Zhang, Hongyu, Zhao, Zeran, Zhang, Shang, Luo, Wecheng, Liu, Xin, and Gong, Liang
- Subjects
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DEFAULT mode network , *SALIENCE network , *FUNCTIONAL magnetic resonance imaging , *LARGE-scale brain networks , *PREFRONTAL cortex - Abstract
Chronic insomnia disorder (CID) is commonly associated with mood disorders. The cingulate gyrus (CG) plays a critical role in the pathophysiology of CID and anxiety. However, the specific characteristics of altered brain networks in the CG in CID with anxiety remain unclear. This study aimed to investigate the characteristics of CG functional connectivity (FC) in CID with and without anxiety. Methods: Resting-state functional magnetic resonance imaging was conducted on 92 CID and 36 healthy controls (HC). CID was divided into CID with anxiety (CID-A, N = 37) and CID without anxiety (CID-NA, N = 55) groups based on anxiety scores. Using the Human Brainnetome Atlas, the subregion CG FC network was constructed. Compared with HC, CID showed significantly decreased CG FC with the precuneus, middle frontal gyrus (MFG), and hippocampus, while showing significantly increased CG FC with the middle temporal gyrus (MTG)/superior temporal gyrus (STG). In contrast, CID-A showed significantly decreased CG FC with the salience network (insular, putamen) and default mode network (MTG/STG and inferior parietal lobule), while showing significantly increased CG FC with the thalamus and MFG compared to CID-NA. Further, CID-A and CID-NA could be classified with 84.21 % accuracy by using the CG FCs as features. Among these features, the CG FC with MFG, thalamus, and putamen had the highest contribution weights. This study revealed specific changes in the brain network of the CG subregion in CID-A. Understanding these CG FC alterations can help identify potential biomarkers specific to CID-A, which may be valuable for early detection and differentiation from other CID subtypes. • To explore the cingulate gyrus (CG) subregions FC alteration in CID with anxiety (CID-A). • CID-A shown specific CG FC with default mode network and salience network. • Machine learning with CG FC features classifies CID-A accurately at 84.21 %. • The CG subregional FC alterations may contribute to potential biomarkers for CID-A. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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