Scholarlink

Background <p>Major depressive disorder (MDD) is a leading cause of disability worldwide, yet its diagnosis relies on clinical symptoms alone.</p> Methods <p>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.</p> Results <p>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).</p> Conclusions <p>D1 and D2 represent distinct neurobiological mechanisms underlying MDD. The validation in a general population-based cohort and in a&#xa0;cohort sample with depressive symptoms delineates mechanisms underlying heterogeneity in MDD.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月15日

查看全文
  • 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

摘要

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.