Disrupted gray matter structural covariance networks in chronic insomnia disorder - Li, Zhonglin; Shen, Yu; Liu, Jiao; Zou, Zhi; Wu, Xiaoling; Gu, Yuang; Gao, Hui; Zhang, Miao; Liu, Ao; Qiao, Qi; Jia, Shulei; Lin, Xinbei; et al. (Frontiers in Psychiatry, 2026)
BackgroundChronic insomnia disorder (CID) is associated with changes in gray matter volume (GMV) and structural connectivity in several brain regions. However, alterations in the topological properties of the structural covariant network (SCN) remain poorly understood in CID.MethodsVoxel-based morphometry and graph theory were applied to examine the topological characteristics of the GMV-based SCN in 82 patients with CID and 73 healthy controls. Group comparison of GMV and multiple regression with pittsburgh sleep quality index (PSQI) were conducted, with hamilton depression acale, hamilton depression scale, total intracranial volume, age, sex, and years of education as covariates. The brain SCN was constructed by thresholding Pearson correlations between the corrected GMVs of 90 brain regions, defined via the automated anatomical labeling atlas. Both the global and nodal topological properties of the brain SCN were analyzed, controlling for the same set of covariates.ResultsThe bilateral precentral gyrus (PreCG) showed both increased GMV and a negative correlation with PSQI scores (p < 0.001, uncorrected). No significant differences were found in the global network topological properties between groups. CID patients exhibited increased nodal betweenness centrality in the right paracentral lobule (PCL), and decreased nodal degree and efficiency in the left postcentral gyrus (PoCG) (p < 0.05, false discovery rate corrected). Furthermore, we observed alterations in both the number and distribution of network hubs. Notably, the constellation of regions exhibiting altered nodal parameters (the right PCL and left PoCG) also functioned as reconfigured network hubs.ConclusionsThis study establishes an association between sleep disturbances in CID and aberrations in both the GMV of specific sensory-motor network nodes (PreCG, PCL, PoCG) and their SCN topological properties, thereby providing new directions for elucidating the disorder’s pathophysiology.