Neuroanatomical dimensions in major depression linked to cognition, adverse life events, self-harm, metabolomics and genetics

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

doi:10.1038/s43856-025-01219-5

Neuroanatomical dimensions in major depression linked to cognition, adverse life events, self-harm, metabolomics and genetics

Commun Med (Lond). 2025 Nov 15;5(1):502. doi: 10.1038/s43856-025-01219-5.

Authors

Wenyi Xiao^#¹, Rachel D Woodham^#², Yuhan Cui^#³, Junhao Wen³, Mathilde Antoniades³, Dhivya Srinivasan³, Yong Fan³, Guray Erus³, Jose A Garcia³, Stephen R Arnott⁴, Taolin Chen^{5

6}, Ki Sueng Choi⁷, Cherise Chin Fatt⁸, Benicio N Frey^{9

10}, Vibe G Frokjaer^{11

12

13}, Melanie Ganz^{11

14}, Beata R Godlewska^{15

16}, Stefanie Hassel^{17

18}, Keith Ho¹⁹, Andrew M McIntosh²⁰, Kun Qin^{5

6

21}, Susan Rotzinger^{19

22}, Matthew D Sacchet²³, Jonathan Savitz²⁴, Haochang Shou^{3

25}, Ashish Singh³, Aleks Stolicyn²⁰, Irina Strigo²⁶, Stephen C Strother^{4

27}, Duygu Tosun²⁸, Dongtao Wei²⁹, Ian M Anderson³⁰, W Edward Craighead^{31

32}, J F William Deakin³⁰, Boadie W Dunlop³¹, Rebecca Elliott³⁰, Qiyong Gong^{5

6}, Ian H Gotlib³³, Catherine J Harmer¹⁵, Sidney H Kennedy^{19

22}, Gitte M Knudsen^{11

12}, Helen S Mayberg⁷, Martin P Paulus²⁴, Jiang Qiu²⁹, Madhukar H Trivedi⁸, Heather C Whalley^{20

34}, Chao-Gan Yan³⁵, Allan H Young^{36

37}, Christos Davatzikos³, Cynthia H Y Fu^{38

39

40}

Affiliations

¹ School of Childhood and Social Care, University of East London, London, UK. w.xiao@uel.ac.uk.
² School of Childhood and Social Care, University of East London, London, UK. r.woodham@uel.ac.uk.
³ Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
⁴ Rotman Research Institute, Baycrest Centre, Ontario, ON, Canada.
⁵ Department of Radiology, Huaxi MR Research Center (HMRRC), Institute of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, China.
⁶ Xiamen Key Laboratory of Psychoradiology and Neuromodulation, Department of Radiology, West China Xiamen Hospital of Sichuan University, Fujian, China.
⁷ Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
⁸ Department of Psychiatry, Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, Dallas, TX, USA.
⁹ Department of Psychiatry and Behavioural Neurosciences, McMaster University, Ontario, ON, Canada.
¹⁰ Mood Disorders Treatment and Research Centre and Women's Health Concerns Clinic, St Joseph's Healthcare Hamilton, Ontario, ON, Canada.
¹¹ Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark.
¹² Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
¹³ Department of Psychiatry, Psychiatric Centre Copenhagen, Copenhagen, Denmark.
¹⁴ Department of Computer Science, University of Copenhagen, Copenhagen, Denmark.
¹⁵ Department of Psychiatry, University of Oxford, Oxford, UK.
¹⁶ Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, UK.
¹⁷ Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, AB, Canada.
¹⁸ Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
¹⁹ Department of Psychiatry, University Health Network, Ontario, ON, Canada.
²⁰ Division of Psychiatry, Centre for Clinical Brain Sciences, Royal Infirmary Edinburgh, Edinburgh, UK.
²¹ Department of Radiology, Taihe Hospital, Hubei University of Medicine, Shiyan, China.
²² Centre for Depression and Suicide Studies, Unity Health Toronto, Ontario, ON, Canada.
²³ Meditation Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
²⁴ Laureate Institute for Brain Research, Tulsa, OK, USA.
²⁵ Penn Statistics in Imaging and Visualization Endeavor (PennSIVE) Center, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA.
²⁶ Department of Psychiatry, University of California San Francisco, San Francisco, CA, USA.
²⁷ Department of Medical Biophysics, University of Toronto, Ontario, ON, Canada.
²⁸ Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.
²⁹ School of Psychology, Southwest University, Chongqing, China.
³⁰ Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK.
³¹ Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA.
³² Department of Psychology, Emory University, Atlanta, GA, USA.
³³ Department of Psychology, Stanford University, Stanford, CA, USA.
³⁴ Generation Scotland, Centre for Medical Informatics, University of Edinburgh, Edinburgh, UK.
³⁵ Department of Psychological and Cognitive Sciences, Tsinghua University, Beijing, China.
³⁶ Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
³⁷ Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, UK.
³⁸ School of Childhood and Social Care, University of East London, London, UK. c.fu@uel.ac.uk.
³⁹ Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK. c.fu@uel.ac.uk.
⁴⁰ Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, UK. c.fu@uel.ac.uk.

^# Contributed equally.

Abstract

Background: Major depressive disorder (MDD) is a leading cause of disability worldwide, yet its diagnosis relies on clinical symptoms alone.

Methods: Using 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.

Results: Dimension 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).

Conclusions: D1 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.

Plain language summary

Major depressive disorder is a common and disabling condition, but people differ greatly in their symptoms and responses to treatment. We used brain scans and machine learning to identify two patterns of brain structure linked to depression. One pattern showed relatively preserved brain volume and was associated with better treatment response. The other showed widespread reductions in brain volume and was related to poorer memory and thinking skills, greater exposure to adverse life events, increased risk of self-harm, and metabolic and genetic changes. These findings were confirmed in a large general population sample as well as in people with current depressive symptoms. The results suggest that depression includes distinct brain-based subtypes, which may help explain differences in treatment response and guide the development of more personalised approaches.

Abstract

Plain language summary

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