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Cell-type and spatiotemporal transcriptional signatures of white matter morphometric similarity network alterations in major depressive disorder.

Overview

Authors: Yue Wu1, Jinglei Xu1, Haolin Wang1, Yulong Shen2, Ying Zhai1, Minghuan Lei1, Zhihui Zhang1, Qian Wu1, Qi An1, Wenjie Cai1, Libo Su3, Yanmin Peng4, Quan Zhang1, Feng Liu1
ORCID iDs: Feng Liu
  1. Department of Radiology and Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, China
  2. School of Laboratory Medicine, Division of Medical Technology, Tianjin Medical University, Tianjin, China
  3. School of Medical Technology, Tianjin Medical University, Tianjin, China
  4. School of Medical Imaging and Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University, Tianjin, China
Journal: Psychological medicine, volume 56, article e179
Dates: received 14 December 2025; accepted 20 April 2026; published online 4 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1017/s0033291726104711 · PMID 42237602 · PMCID PMC13247791 · OpenAlex W7163567765
Open access: hybrid, a free copy (OpenAlex)
Status: data only
Categories: genetics / omics (modality), structural MRI / diffusion (modality), human (organism), depression (population), cellular / molecular (subfield)
Methods: Statistics, Preprocessing, Connectivity, Graphs, fMRI & imaging, Smoothing, state filtering, decompositions
Keywords: major depressive disorder, network neuroscience, oligodendrocyte and myelination pathways, transcriptomic integration, white matter morphometric similarity networks
MeSH: Major Depressive Disorder*, Nerve Net*, Transcriptome*, White Matter*, Adult, Female, Gene Expression Profiling, Humans, Magnetic Resonance Imaging, Male, Middle Aged (* major topic)
Topic: Advanced Neuroimaging Techniques and Applications (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex
Funding: National Natural Science Foundation of China (82572306)
Citations: not cited yet (Europe PMC); 73 references in the paper

Abstract

Background: White matter (WM) abnormalities are implicated in major depressive disorder (MDD), yet the organization of white matter morphometric similarity networks (WM-MSNs) – which capture interregional similarity in voxel-wise WM morphology – and the transcriptional mechanisms associated with their disruption remain insufficiently understood.

Methods: Using T1-weighted MRI from a large multisite sample (1,154 individuals with MDD and 1,026 healthy controls), we constructed individualized WM-MSNs. Group differences were assessed at the edge, global, and nodal levels. To identify molecular pathways underlying these alterations, nodal abnormalities were linked to regional gene expression profiles from the Allen Human Brain Atlas using spatially informed transcriptomic association, followed by functional, cell-type-specific, and developmental enrichment analyses.

Results: MDD showed distributed but selective reorganization of WM-MSNs. Network-based statistics revealed two significant components, with 118 edges exhibiting increased morphometric similarity and 45 showing decreased similarity. Globally, MDD demonstrated higher small-worldness, clustering coefficient, global efficiency, and local efficiency, together with shorter characteristic path length. Nodal disruptions were concentrated in major commissural and association tracts – including the corpus callosum, cingulum, uncinate fasciculus, and tapetum. Transcriptomic integration indicated enrichment for gene signatures related to oligodendrocyte function, myelination, lipid metabolism, axonal organization, and cellular stress-related molecular processes, with implicated genes showing broad developmental-stage expression.

Conclusions: MDD is associated with robust alterations in individualized WM-MSNs that converge with transcriptional signatures linked to myelination, metabolic processes, axonal structure, and cellular stress, linking macroscale network disruption to underlying molecular architecture and providing cross-scale insights into WM pathology in depression.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 14 authors, 5 keywords, 11 MeSH terms, 1 funder, 73 references.

Cite

This paper

Wu, Y., Xu, J., Wang, H., Shen, Y., Zhai, Y., Lei, M., Zhang, Z., Wu, Q., An, Q., Cai, W., Su, L., Peng, Y., Zhang, Q., & Liu, F. (2026). Cell-type and spatiotemporal transcriptional signatures of white matter morphometric similarity network alterations in major depressive disorder. Psychological medicine, 56, e179. https://doi.org/10.1017/s0033291726104711

BibTeX

@article{wu2026cell,
author = {Wu, Yue and Xu, Jinglei and Wang, Haolin and Shen, Yulong and Zhai, Ying and Lei, Minghuan and Zhang, Zhihui and Wu, Qian and An, Qi and Cai, Wenjie and Su, Libo and Peng, Yanmin and Zhang, Quan and Liu, Feng},
title = {{Cell-type and spatiotemporal transcriptional signatures of white matter morphometric similarity network alterations in major depressive disorder}},
journal = {Psychological medicine},
year = {2026},
month = jun,
volume = {56},
pages = {e179},
publisher = {Cambridge University Press},
issn = {0033-2917},
doi = {10.1017/s0033291726104711},
url = {https://doi.org/10.1017/s0033291726104711},
pmid = {42237602},
pmcid = {PMC13247791}
}

RIS

TY - JOUR
AU - Wu, Yue
AU - Xu, Jinglei
AU - Wang, Haolin
AU - Shen, Yulong
AU - Zhai, Ying
AU - Lei, Minghuan
AU - Zhang, Zhihui
AU - Wu, Qian
AU - An, Qi
AU - Cai, Wenjie
AU - Su, Libo
AU - Peng, Yanmin
AU - Zhang, Quan
AU - Liu, Feng
TI - Cell-type and spatiotemporal transcriptional signatures of white matter morphometric similarity network alterations in major depressive disorder
T2 - Psychological medicine
J2 - Psychol Med
PY - 2026
DA - 2026/06/04
VL - 56
SP - e179
SN - 0033-2917
PB - Cambridge University Press
DO - 10.1017/s0033291726104711
UR - https://doi.org/10.1017/s0033291726104711
LA - en
ER -

CSL-JSON

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