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October 20, 2026 · via Europe PMC

The network localization and underlying biological basis of heterogeneous structural and functional brain abnormalities in insomnia disorder.

Xue W, Gao Z, Shi Y, +9 more, Xiao Y.

Journal ArticleAbstract only — full text under license

AI summary — for doctors, generated from the abstract only

A pooled analysis of 63 neuroimaging studies maps insomnia's heterogeneous brain abnormalities onto specific attention, sensorimotor and default-mode networks.

What was studied

Coordinate-based network mapping of findings from 63 studies (3391 patients, 3433 controls) onto a normative connectome, then decoding with neurotransmitter, cell-type and cognitive maps.

Key findings

  • Structural abnormalities involved ventral attention, sensorimotor, dorsal attention and default-mode networks.
  • The resting-state network was concentrated in the left middle frontal gyrus and bilateral mid-cingulate cortex.
  • Molecular mapping implicated cholinergic, histaminergic, glutamatergic, noradrenergic and opioid systems.

Clinical relevance

This offers a network-level framework for insomnia that could eventually support imaging biomarkers and circuit-targeted treatments.

Who should care

Pulmonary Medicine

Limitations

It is a meta-analytic mapping dependent on the published coordinates and a normative connectome rather than patient-level data, and decoding is correlational.

No change in practice now; this is hypothesis-generating for future biomarker and neuromodulation work.

This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.

Abstract

Insomnia disorder (ID) has become a prevalent sleep disorder worldwide, yet its underlying neural mechanisms remain unclear due to heterogeneous neuroimaging findings and uncertain network localization. We identified brain structural and functional abnormality locations associated with ID from 63 published neuroimaging studies, including 3391 patients and 3433 healthy controls. Using coordinate network mapping combined with a normative connectome database, we mapped these abnormalities to large-scale brain networks of ID across imaging modalities and further decoded them by integrating microscale chemo-architectural, cellular, and cognitive maps. The structural network widely involved the ventral attention (VAN), sensorimotor (SMN), dorsal attention (DAN) and default mode network (DMN); the task-induced network showed a similar but less extensive pattern, predominantly implicating the VAN, DMN, SMN and subcortical network (SCN); in contrast, the resting-state network was largely confined to the left middle frontal gyrus and the bilateral mid-cingulate cortex. Molecular mapping revealed both shared and distinct neurotransmitter signatures across the three networks, involving cholinergic, histaminergic, glutamatergic, noradrenergic, and opioid systems. Cellular decoding showed relatively selective associations of the structural and task-based networks with glial cells and excitatory neurons, whereas the resting-state network exhibited broader associations with glial cells and multiple excitatory and inhibitory neuronal subtypes. Cognitive decoding linked structural and task-based networks to sensorimotor processing and the resting-state network to memory-related functions. Our findings reconcile heterogeneous neuroimaging results and reveal neurobiological substrates of ID from a network perspective, providing a promising biomarker and paving the way for circuit-based precision therapies of ID.

DOI

10.1093/sleep/zsag277

PMID

42899689

Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.

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