Personalized functional network connectivity abnormalities in chronic insomnia disorder

Background: Chronic insomnia disorder (CID) is associated with disrupted functional brain networks, yet prior research has focused primarily on group-level analyses. This study employed personalized functional network mapping to identify connectivity abnormalities in CID. Methods: Resting-state functional magentic resonance imaging (rs-fMRI) data were collected from 86 CID patients and 38 good sleeper controls (GSCs). Using non-negative matrix factorization (NMF), we derived individualized large-scale brain networks for each participant to uncover subject-specific connectivity changes in CID. We also constructed functional network connectivity (FNC) matrices using Pearson correlation coefficients and compared global and local graph-theory metrics across groups based on these individualized networks. Results: FNC analysis revealed significant differences between CID patients and GSCs within the default mode network (DMN), ventral attention network, visual network (VIS), and other key brain regions. CID exhibited altered global network topology and significant differences in local topological properties. At the global level, CID demonstrated significantly higher small-worldness (Sigma) and normalized clustering coefficient (Gamma). At the nodal level, CID showed increased local efficiency and clustering coefficient, as well as decreased nodal efficiency in the DMN, along with increased degree centrality in the VIS. Conclusion: By focusing on individualized functional connectivity, this approach reveals unique "fingerprint" alterations in CID. These findings provide novel insights into CID's neurobiological mechanisms and underscore the value of personalized network approaches for understanding and treating sleep disorders.