| Title: | Neuroanatomical dimensions in major depression linked to cognition, adverse life events, self-harm, metabolomics and genetics |
| Journal: | Communications Medicine |
| Published: | 15 Nov 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41258534/ |
| DOI: | https://doi.org/10.1038/s43856-025-01219-5 |
| URL: | https://www.nature.com/articles/s43856-025-01219-5_reference.pdf |
Publication 18918
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Abstract
BackgroundMajor depressive disorder (MDD) is a leading cause of disability worldwide, yet its diagnosis relies on clinical symptoms alone.MethodsUsing the semi-supervised machine learning algorithm, Heterogeneity through Discriminative Analysis (HYDRA), we had identified two neuroanatomical dimensions in deeply phenotyped (i.e., comprehensively assessed across neuroimaging, clinical, and behavioural domains), medication-free participants with MDD from the COORDINATE-MDD consortium. In the present study, we apply this pre-trained HYDRA model to the UK Biobank (UKB) to validate these dimensions in a large general population and a subsample with current depressive symptoms.ResultsDimension 2 (D2), compared to Dimension 1 (D1), is characterized by reduced grey and white matter volumes and limited treatment response to antidepressant and placebo medications. Out-of-sample validation in the UKB general population (n = 37,235) confirms these neuroanatomical features and reveals D2 associations with cognitive impairments, adverse life events, self-harm and suicide attempts, a pro-atherogenic lipid profile, and genetic links to neurodegenerative traits. Similar profiles are observed in the UKB subsample with current depressive symptoms (n = 1455).ConclusionsD1 and D2 represent distinct neurobiological mechanisms underlying MDD. The validation in a general population-based cohort and in a cohort sample with depressive symptoms delineates mechanisms underlying heterogeneity in MDD.</p>
50 Authors
- Wenyi Xiao
- Rachel D. Woodham
- Yuhan Cui
- Junhao Wen
- Mathilde Antoniades
- Dhivya Srinivasan
- Yong Fan
- Guray Erus
- Jose A. Garcia
- Stephen R. Arnott
- Taolin Chen
- Ki Sueng Choi
- Cherise Chin Fatt
- Benicio N. Frey
- Vibe G. Frokjaer
- Melanie Ganz
- Beata R. Godlewska
- Stefanie Hassel
- Keith Ho
- Andrew M. McIntosh
- Kun Qin
- Susan Rotzinger
- Matthew D. Sacchet
- Jonathan Savitz
- Haochang Shou
- Ashish Singh
- Aleks Stolicyn
- Irina Strigo
- Stephen C. Strother
- Duygu Tosun
- Dongtao Wei
- Ian M. Anderson
- W. Edward Craighead
- J. F. William Deakin
- Boadie W. Dunlop
- Rebecca Elliott
- Qiyong Gong
- Ian H. Gotlib
- Catherine J. Harmer
- Sidney H. Kennedy
- Gitte M. Knudsen
- Helen S. Mayberg
- Martin P. Paulus
- Jiang Qiu
- Madhukar H. Trivedi
- Heather C. Whalley
- Chao-Gan Yan
- Allan H. Young
- Christos Davatzikos
- Cynthia H. Y. Fu
Enabling scientific discoveries that improve human health