ArticlePsychoradiology2026
Personalized functional network connectivity abnormalities in chronic insomnia disorder.
Article in Psychoradiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Modeling Complex Effects and Individual Variability in Multi-Paradigm fMRI with Nonlinear Mixed Models.bioRxiv : the preprint server for biology · 2026Article
- Subtyping insomnia disorder with a population graph attention autoencoder: revealing two distinct biotypes.Frontiers in neuroscience · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.