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Connectome gradient dysfunction in major depression and its association with gene expression profiles and treatment outcomes

Molecular Psychiatry volume 27, pages 1384–1393 (2022)Cite this article

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Abstract

Patients with major depressive disorder (MDD) exhibit concurrent deficits in both sensory and higher-order cognitive processing. Connectome studies have suggested a principal primary-to-transmodal gradient in functional brain networks, supporting the spectrum from sensation to cognition. However, whether this gradient structure is disrupted in patients with MDD and how this disruption associates with gene expression profiles and treatment outcome remain unknown. Using a large cohort of resting-state fMRI data from 2227 participants (1148 MDD patients and 1079 healthy controls) recruited at nine sites, we investigated MDD-related alterations in the principal connectome gradient. We further used Neurosynth, postmortem gene expression, and an 8-week antidepressant treatment (20 MDD patients) data to assess the meta-analytic cognitive functions, transcriptional profiles, and treatment outcomes related to MDD gradient alterations, respectively. Relative to the controls, MDD patients exhibited global topographic alterations in the principal primary-to-transmodal gradient, including reduced explanation ratio, gradient range, and gradient variation (Cohen’s d = 0.16–0.21), and focal alterations mainly in the primary and transmodal systems (d = 0.18–0.25). These gradient alterations were significantly correlated with meta-analytic terms involving sensory processing and higher-order cognition. The transcriptional profiles explained 53.9% variance of the altered gradient pattern, with the most correlated genes enriched in transsynaptic signaling and calcium ion binding. The baseline gradient maps of patients significantly predicted symptomatic improvement after treatment. These results highlight the connectome gradient dysfunction in MDD and its linkage with gene expression profiles and clinical management, providing insight into the neurobiological underpinnings and potential biomarkers for treatment evaluation in this disorder.

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Fig. 1: Connectome gradient mapping in patients with MDD and controls.
Fig. 2: Statistical comparison of the gradient metrics.
Fig. 3: Association between MDD-related gradient alterations and gene expression.
Fig. 4: Clinical effects on the gradient topography and prediction of treatment outcomes in patients with MDD.

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Data availability

The core analysis code and resulting data are publicly available at github.com/mingruixia/MDD_ConnectomeGradient. For details of Materials and Methods, see Supplementary Material.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (82071998 and 81671767 to MX; 82021004, 81620108016, and 91432115 to YH; 81171286 and 91232714 to LL; 31771231 and 31271087 to JQ; 81571331 to FW; 81271499 and 81571311 to YT; 81920108019 and 81771344 to SQ; 81630031 to TS; 81621003 to QG; 81660237 to XX), the Beijing Nova Program (Z191100001119023 to MX), Fundamental Research Funds for the Central Universities (2020NTST29 to MX), the National Key R&D Program of China (2018YFA0701400 to YH), the Changjiang Scholar Professorship Award (T2015027 to YH), the National Science and Technologic Program of China (2015BAI13B02 to LL), the National Basic Research Program of China (2013CB835100 LL), the Natural Science Foundation of Chongqing (cstc2019jcyj-msxmX0520 to JQ), the National High Tech Development Plan (863) (2015AA020513 to FW), the Medical Science and Technology Research Project of Henan Province (201701011 to JC), and Shanghai Science and Technology Innovation Plan (17JC1404105 and 17JC1404101 to C-PL). The authors thank the Allen Institute for Brain Science for providing the gene expression data.

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Authors and Affiliations

  1. State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China

    Mingrui Xia, Jin Liu, Xiaoyi Sun, Qing Ma & Yong He

  2. Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China

    Mingrui Xia, Jin Liu, Xiaoyi Sun, Qing Ma & Yong He

  3. IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China

    Mingrui Xia, Jin Liu, Xiaoyi Sun, Qing Ma & Yong He

  4. Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, De Crespigny Park, London, UK

    Andrea Mechelli

  5. School of Systems Science, Beijing Normal University, Beijing, China

    Xiaoyi Sun

  6. Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China

    Qing Ma & Ching-Po Lin

  7. Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China

    Xiaoqin Wang, Dongtao Wei & Jiang Qiu

  8. Department of Psychology, Southwest University, Chongqing, China

    Xiaoqin Wang, Dongtao Wei & Jiang Qiu

  9. Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China

    Yuan Chen & Jingliang Cheng

  10. Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China

    Bangshan Liu & Lingjiang Li

  11. Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Key Laboratory of Psychiatry and Mental Health, Hunan Medical Center for Mental Health, Changsha, Hunan, China

    Bangshan Liu & Lingjiang Li

  12. Key Laboratory of Brain Functional Genomics (Ministry of Education), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China

    Chu-Chung Huang

  13. Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China

    Yanting Zheng & Shijun Qiu

  14. Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China

    Yankun Wu & Tianmei Si

  15. Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital, Sichuan University, Chengdu, China

    Taolin Chen & Qiyong Gong

  16. Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China

    Yuqi Cheng & Xiufeng Xu

  17. Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China

    Qiyong Gong

  18. Institute of Neuroscience, National Yang-Ming Chiao-Tung University, Taipei, Taiwan

    Ching-Po Lin

  19. Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China

    Yanqing Tang & Fei Wang

  20. Chongqing Key Laboratory of Neurobiology, Chongqing, China

    Peng Xie

  21. Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China

    Peng Xie

  22. Chinese Institute for Brain Research, Beijing, China

    Yong He

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DIDA-MDD Working Group

  • Yong He
  • , Lingjiang Li
  • , Jingliang Cheng
  • , Qiyong Gong
  • , Ching-Po Lin
  • , Jiang Qiu
  • , Shijun Qiu
  • , Tianmei Si
  • , Yanqing Tang
  • , Fei Wang
  • , Peng Xie
  • , Xiufeng Xu
  •  & Mingrui Xia

Contributions

MX and YH designed research; XW, DW, YChen, BL, C-CH, YZ, YW, TC, YCheng, XX, QG, TS, SQ, C-PL, JC, YT, FW, JQ, PX, and LL collected data; MX, XS, and QM performed data quality control; MX and JL analyzed data; and MX, JL, AM, and YH, wrote the paper.

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Correspondence to Yong He.

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Xia, M., Liu, J., Mechelli, A. et al. Connectome gradient dysfunction in major depression and its association with gene expression profiles and treatment outcomes. Mol Psychiatry 27, 1384–1393 (2022). https://doi.org/10.1038/s41380-022-01519-5

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