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A mega-analysis of functional connectivity and network abnormalities in youth depression

Nature Mental Health (2024)Cite this article

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Abstract

Major depressive disorder (MDD) represents the leading cause of mental health disability for young people worldwide but remains poorly understood. Previous neuroimaging research has indicated alterations in the connectivity of brain circuitry in youth MDD; however, findings have been inconsistent. This may relate to limitations in sample size and sample and methodological heterogeneity. In an effort to delineate robust neurobiological markers of youth MDD, we conducted a data-driven, connectome-wide mega-analysis of resting-state functional connectivity in 810 young individuals across 7 independent cohorts with a cross-sectional and case-control design. Compared with healthy comparison individuals (n = 370), youth MDD (n = 440) was associated with significant alterations in connectivity of densely connected brain areas (hubs), anchored in the default mode and dorsal and ventral attention networks. Critically, functional connectivity within these networks was significantly associated with depression symptom severity (r = –0.46 for hypoconnected regions and r = 0.53 for hyperconnected regions; both P values < 0.001), indicating the clinical relevance of functional connectivity alterations. Further, machine-learning analyses demonstrated that individual diagnostic status (AUC = 73.1%) and clinical severity (r = 0.14, P = 0.008) could be predicted on the basis of functional connectivity alone in unseen data using leave-one-site-out cross-validation. Together, our work represents an important first step toward robust characterization of the neurobiological basis of youth depression. We demonstrate the clinical relevance of brain connectivity in youth depression and highlight a critical role of functional hub regions, especially those localized to the default mode and dorsal and ventral attention networks in youth MDD.

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Fig. 1: Mega-analysis design and workflows.
Fig. 2: Functional connectivity changes in youth MDD compared with HC individuals controlling for age and sex.
Fig. 3: Depression symptom severity-related functional connectivity and network changes controlling for age and sex.
Fig. 4: Predictive models of diagnostic status (depression or HC individual) and depression severity.

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Data availability

This study did not involve the use of publicly available datasets, but de-identified data from seven previously published datasets collected by six research groups across four countries. Data may be made available upon reasonable request at the discretion of each respective principal investigator. Data sharing will be subject to the policies and procedures of the institution where each dataset was collected. Principal investigators from sites that provided data used in this study include C.G.D. (Sites 1 and 2), I.H.G. (Site 3 TAD dataset), B.J.H. (Sites 1 and 2), T.C.H. (Site 3 TIGER dataset), J.Q. (Site 5), J.S. (Site 4) and T.T.Y. (Site 6). Please direct all data requests to N.Y.T. at ngayant@student.unimelb.edu.au.

Code availability

All the neuroimaging pre-processing and analyses conducted in this study involved the use of publicly available toolboxes and resources. This included the fMRIPrep version 23.0.1 (accessible at https://fmriprep.org/en/stable/installation.html), the combined Schaefer 400 cortical and Melbourne Subcortex Atlas (accessible at https://github.com/yetianmed/subcortex/tree/master/Group-Parcellation/3T/Cortex-Subcortex), NBS MATLAB toolbox version 1.2 (accessible at https://www.nitrc.org/projects/nbs/) and ComBat Harmonization package (https://github.com/Jfortin1/ComBatHarmonization). All cortical renderings were generated using the GUI-based toolbox BrainNet Viewer version 1.7 (https://www.nitrc.org/projects/bnv) via MATLAB. For predictive analyses, dimension reduction via PCA was performed using the pca function in MATLAB version R2021a. This was followed by classification and regression analyses performed using the fitclinear and fitrlinear MATLAB functions, respectively.

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Acknowledgments

This work was supported by the American Foundation for Suicide Prevention (SRG-1-141-18 to T.T.Y.), the Australian National Health and Medical Research Council (NHMRC; Postgraduate Scholarship grant no. 2022387 to N.Y.T., Early Career fellowship to A.R., Investigator Leadership grant no. 2017962 to L.S. and Emerging Leadership Investigator grant no. 2017527 to R.F.H.C.), the Australian Research Council Future Fellowship (A.Z.), the Australian Research Training Program Scholarship (S.G.), the Brain and Behavior Research Foundation (to T.T.Y. and grant no. 28972 to M.D.S.), the Dimension Giving Fund (M.D.S.), the training fellowship awarded to the Division of Child and Adolescent Psychiatry at Columbia University (grant no. T32 MH016434-42 to J.S.K.), the Graeme Clark Institute top-up scholarship (S.G.), the J. Jacobson Fund (T.T.Y.), the Mary Lugton Postdoc Fellowship (Y.E.T.), the National Center for Advancing Translational Sciences (T.T.Y.), the National Center for Complementary and Integrative Health (grant nos. R21AT009173, R61AT009864, R33AT009864 to T.T.Y.), the National Institutes of Health (RO1 MH129832 to L.S. and UCSF-CTSI UL1TR001872 to T.T.Y.), the National Institute of Mental Health (project no. R01MH125850 to M.D.S. and R01MH085734 to T.T.Y.), the Rebecca L. Cooper Foundation Fellowship (A.Z.), the Rubicon award from the Dutch NOW (grant no. 452020227 to L.K.M.H.), the University of Melbourne Dame Kate Campbell fellowship (L.S.), the UCSF Research Evaluation and Allocation Committee (T.T.Y.) and the UCSF Weill Institute for Neurosciences (T.T.Y.). Data from the MR-IMPACT study site were funded by the United Kingdom Medical Research Council (G0802226) and undertaken at the University of Cambridge. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. This research was also supported by The University of Melbourne’s Research Computing Services and the Petascale Campus Initiative.

Author information

Author notes

  1. These authors contributed equally: Robin F. H. Cash, Andrew Zalesky.

Authors and Affiliations

  1. Systems Lab, Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia

    Nga Yan Tse, Ye Ella Tian, Saampras Ganesan, Robin F. H. Cash & Andrew Zalesky

  2. Orygen, Melbourne, Victoria, Australia

    Aswin Ratheesh, Laura K. M. Han & Lianne Schmaal

  3. Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia

    Aswin Ratheesh, Laura K. M. Han & Lianne Schmaal

  4. Discipline of Psychiatry and Mental Health, University of New South Wales, Randwick, New South Wales, Australia

    Aswin Ratheesh

  5. Department of Biomedical Sciences, Florida State University, Tallahassee, FL, USA

    Colm G. Connolly

  6. Department of Psychiatry, The University of Melbourne, Parkville, Victoria, Australia

    Christopher G. Davey, Ben J. Harrison & Alec J. Jamieson

  7. Department of Biomedical Engineering, Faculty of Engineering and Information Technology, The University of Melbourne, Melbourne, Victoria, Australia

    Saampras Ganesan, Robin F. H. Cash & Andrew Zalesky

  8. Contemplative Studies Centre, Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, Victoria, Australia

    Saampras Ganesan

  9. Department of Psychology, Stanford University, Stanford, CA, USA

    Ian H. Gotlib

  10. Department of Psychology, University of California, Los Angeles, CA, USA

    Tiffany C. Ho

  11. Department of Psychiatry, Columbia University, New York, NY, USA

    Jaclyn S. Kirshenbaum

  12. Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute, New York, NY, USA

    Jaclyn S. Kirshenbaum

  13. School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China

    Yong Liu

  14. Mental Health Center and Laboratory of Psychiatry, West China Hospital of Sichuan University, Chengdu, China

    Xiaohong Ma & Xiao Yang

  15. Center for Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA, USA

    Amar Ojha

  16. Center for Neuroscience, University of Pittsburgh, Pittsburgh, PA, USA

    Amar Ojha

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

    Jiang Qiu & Dongtao Wei

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

    Jiang Qiu & Dongtao Wei

  19. Meditation Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

    Matthew D. Sacchet

  20. VA San Diego Healthcare System, San Diego, CA, USA

    Alan N. Simmons

  21. Department of Psychiatry, University of California San Diego, La Jolla, CA, USA

    Alan N. Simmons

  22. Department of Psychiatry, Herchel Smith Building for Brain and Mind Sciences, University of Cambridge, Cambridge, UK

    John Suckling

  23. Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK

    John Suckling

  24. Department of Psychiatry and Behavioral Sciences, Division of Child and Adolescent Psychiatry, Weill Institute for Neurosciences, University of California, San Francisco, CA, USA

    Tony T. Yang

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  1. Nga Yan Tse
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Contributions

N.Y.T., A.Z., R.F.H.C. and A.R. contributed to the conception and design of the study. N.Y.T., A.Z., R.F.H.C. and A.R. conducted the neuroimaging and statistical analyses with contributions from Y.E.T., C.G.C., C.G.D., I.H.G., B.J.H., T.C.H., A.J.J., J.S.K., Y.L., A.O., J.Q., M.D.S., A.N.S., J.S., D.W. and T.T.Y. contributed data. S.G., X.M. and X.Y. assisted with data collation. N.Y.T., A.Z., R.F.H.C., A.R., L.K.M.H. and L.S. contributed to the writing of the manuscript with input and valuable revision from all authors.

Corresponding author

Correspondence to Andrew Zalesky.

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Peer review

Peer review information

Nature Mental Health thanks Deanna Barch, Mingrui Xia and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Participant exclusion flowchart.

Flowchart outlining the number of participants excluded and the reason for exclusion following each stage of processing. HC = healthy control; MDD = major depressive disorder.

Supplementary information

Supplementary Information

Supplementary Figs. 1–12, Tables 1–4 and Sections 1–5.

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Tse, N.Y., Ratheesh, A., Tian, Y.E. et al. A mega-analysis of functional connectivity and network abnormalities in youth depression. Nat. Mental Health (2024). https://doi.org/10.1038/s44220-024-00309-y

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