Skip to main content
SpringerLink
Log in

Altered brain activity in unipolar depression unveiled using connectomics

  • Analysis
  • Published:

From Nature Mental Health

View current issue Submit your manuscript

Abstract

Over 20 years of neuroimaging experiments into aberrant task-based brain activity in unipolar depression have failed to reliably delineate a convergent set of anatomical regions. Here we examined whether study-derived coordinates might delineate a dysfunctional brain network in unipolar depression rather than isolated neuroanatomical foci, utilizing data from 57 studies with 99 individual neuroimaging task-based experiments, testing either emotional or cognitive processing (n = 1,058). We further assessed clinical relevance by computing optimal network-based personalized targets in 26 individuals who previously received transcranial magnetic stimulation for unipolar depression. Although coordinates were neuroanatomically heterogeneous, they localized to highly robust distributed brain networks. Importantly, these networks closely recapitulated clinically meaningful and independently derived models of depression circuitry, quantified by spatial correlation (P < 0.00002). Therapeutic outcome of transcranial magnetic stimulation was dependent on how effectively this circuit was targeted (P = 0.018). These findings indicate that neuroimaging findings in depression, which previously appeared irreconcilable, localize to highly robust and clinically meaningful distributed brain networks.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1: Overview.
Fig. 2: Brain networks representing emotional and cognitive processing abnormalities in UD.
Fig. 3: Relation across different brain network maps.
Fig. 4: Convergence and comparison to previous network models of depression.
Fig. 5: The relation between TMS circuit-targeting and clinical response.
Fig. 6: Efficacy of circuit-specific targeting predicts TMS clinical success.

Similar content being viewed by others

Data availability

The meta-analysis derived coordinate data have been published previously1. HCP datasets are available for download to anyone agreeing to the open access data use terms (https://db.humanconnectome.org/). The clinical data remain subject to privacy and ethical restrictions. The depression cohort dataset was collected separately from the present study (ACTRN12610001071011) and remains subject to privacy and ethical restrictions.

Code availability

Preprocessing software and code is available at https://fmriprep.org/en/stable/. Custom analysis scripts were developed and implemented in MATLAB R2017a, and are available on reasonable request to the corresponding author (R.F.H.C.), although not for commercial applications.

References

  1. Müller, V. I. et al. Altered brain activity in unipolar depression revisited: meta-analyses of neuroimaging studies. JAMA Psychiatry 74, 47–55 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Barch, D. M. & Pagliaccio, D. Consistency, replication, and meta-analyses of altered brain activity in unipolar depression. JAMA Psychiatry 74, 56–57 (2017).

    Article  PubMed  Google Scholar 

  3. Ioannidis, J. P., Fanelli, D., Dunne, D. D. & Goodman, S. N. Meta-research: evaluation and improvement of research methods and practices. PLoS Biol. 13, e1002264 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Darby, R. R., Joutsa, J. & Fox, M. D. Network localization of heterogeneous neuroimaging findings. Brain 142, 70–79 (2019).

    Article  PubMed  Google Scholar 

  5. Button, K. S. et al. Power failure: why small sample size undermines the reliability of neuroscience. Nat. Rev. Neurosci. 14, 365–376 (2013).

    Article  PubMed  Google Scholar 

  6. Poldrack, R. A. et al. Scanning the horizon: towards transparent and reproducible neuroimaging research. Nat. Rev. Neurosci. 18, 115–126 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Carp, J. On the plurality of (methodological) worlds: estimating the analytic flexibility of FMRI experiments. Front. Neurosci. 6, 149 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Marquand, A. F. et al. Conceptualizing mental disorders as deviations from normative functioning. Mol. Psychiatry 24, 1415–1424 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Molenberghs, P., Sale, M. V. & Mattingley, J. B. Is there a critical lesion site for unilateral spatial neglect? A meta-analysis using activation likelihood estimation. Front. Hum. Neurosci. 6, 78 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Fornito, A., Zalesky, A. & Breakspear, M. The connectomics of brain disorders. Nat. Rev. Neurosci. 16, 159–172 (2015).

    Article  PubMed  Google Scholar 

  11. Downar, J. & Daskalakis, Z. J. New targets for rTMS in depression: a review of convergent evidence. Brain Stimul. 6, 231–240 (2013).

    Article  PubMed  Google Scholar 

  12. Boes, A. D. et al. Network localization of neurological symptoms from focal brain lesions. Brain 138, 3061–3075 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Siegel, J. S. et al. Disruptions of network connectivity predict impairment in multiple behavioral domains after stroke. Proc. Natl Acad. Sci. USA 113, E4367–E4376 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Padmanabhan, J. L. et al. A human depression circuit derived from focal brain lesions. Biol. Psychiatry 86, 749–758 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Horn, A. The impact of modern-day neuroimaging on the field of deep brain stimulation. Curr. Opin. Neurol. 32, 511–520 (2019).

    Article  PubMed  Google Scholar 

  16. Siddiqi, S. H. et al. Brain stimulation and brain lesions converge on common causal circuits in neuropsychiatric disease. Nat. Hum. Behav. 5, 1707–1716 (2021).

  17. Baldermann, J. C. et al. Connectivity profile predictive of ffective deep brain stimulation in obsessive-compulsive disorder. Biol. Psychiatry 85, 735–743 (2019).

    Article  PubMed  Google Scholar 

  18. Eickhoff, S. B. et al. Behavior, sensitivity, and power of activation likelihood estimation characterized by massive empirical simulation. Neuroimage 137, 70–85 (2016).

    Article  PubMed  Google Scholar 

  19. Cash, R. F., Cocchi, L., Lv, J., Fitzgerald, P. B. & Zalesky, A. Functional magnetic resonance imaging–guided personalization of transcranial magnetic stimulation treatment for depression. JAMA Psychiatry 78, 337–339 (2021).

    Article  PubMed  Google Scholar 

  20. Fox, M. D., Buckner, R. L., White, M. P., Greicius, M. D. & Pascual-Leone, A. Efficacy of transcranial magnetic stimulation targets for depression is related to intrinsic functional connectivity with the subgenual cingulate. Biol. Psychiatry 72, 595–603 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Weigand, A. et al. Prospective validation that subgenual connectivity predicts antidepressant efficacy of transcranial magnetic stimulation sites. Biol. Psychiatry 84, 28–37 (2018).

    Article  PubMed  Google Scholar 

  22. Cash, R. F. et al. Personalized connectivity‐guided DLPFC‐TMS for depression: advancing computational feasibility, precision and reproducibility. Hum. Brain Mapping 42, 4155–4172 (2021).

    Article  Google Scholar 

  23. Cash, R. F. et al. Personalized brain stimulation of memory networks. Brain Stimul. 15, 1300–1304 (2022).

    Article  PubMed  Google Scholar 

  24. Fox, M. D., Liu, H. & Pascual-Leone, A. Identification of reproducible individualized targets for treatment of depression with TMS based on intrinsic connectivity. Neuroimage 66, 151–160 (2013).

    Article  PubMed  Google Scholar 

  25. Tik, M. et al. Acute TMS/fMRI response explains offline TMS network effects—an interleaved TMS-fMRI study. NeuroImage 267, 119833 (2023).

    Article  PubMed  Google Scholar 

  26. Kong, G., Wei, L., Wang, J., Zhu, C. & Tang, Y. The therapeutic potential of personalized connectivity-guided transcranial magnetic stimulation target over group-average target for depression. Brain Stimul. 15, 1063–1064 (2022).

  27. Stöhrmann, P. et al. Effects of bilateral sequential theta-burst stimulation on functional connectivity in treatment-resistant depression: first results. Preprint at medRxiv https://doi.org/10.1101/2022.02.16.22271078 (2022).

  28. Cash, R. F. H. et al. Subgenual functional connectivity predicts antidepressant treatment response to transcranial magnetic stimulation: independent validation and evaluation of personalization. Biol. Psychiatry 86, e5–e7 (2019).

    Article  PubMed  Google Scholar 

  29. Hamani, C. et al. The subcallosal cingulate gyrus in the context of major depression. Biol. Psychiatry 69, 301–308 (2011).

    Article  PubMed  Google Scholar 

  30. Groenewold, N. A., Opmeer, E. M., de Jonge, P., Aleman, A. & Costafreda, S. G. Emotional valence modulates brain functional abnormalities in depression: evidence from a meta-analysis of fMRI studies. Neurosci. Biobehav. Rev. 37, 152–163 (2013).

    Article  PubMed  Google Scholar 

  31. Heilbronner, S. R., Safadi, Z. & Haber, S. N. in Neuromodulation in Psychiatry (eds Hamami, C. et al.) Ch. 3 (Wiley, 2016).

  32. Etkin, A., Egner, T. & Kalisch, R. Emotional processing in anterior cingulate and medial prefrontal cortex. Trends Cogn. Sci. 15, 85–93 (2011).

    Article  PubMed  Google Scholar 

  33. Wang, C. et al. Disrupted functional connectivity patterns of the insula subregions in drug-free major depressive disorder. J. Affect. Disord. 234, 297–304 (2018).

    Article  PubMed  Google Scholar 

  34. Sheline, Y. I., Price, J. L., Yan, Z. & Mintun, M. A. Resting-state functional MRI in depression unmasks increased connectivity between networks via the dorsal nexus. Proc. Natl Acad. Sci. USA 107, 11020–11025 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Downar, J. Orbitofrontal cortex: a ‘non-rewarding’new treatment target in depression? Curr. Biol. 29, R59–R62 (2019).

    Article  PubMed  Google Scholar 

  36. Bakker, N. et al. rTMS of the dorsomedial prefrontal cortex for major depression: safety, tolerability, effectiveness, and outcome predictors for 10 Hz versus intermittent theta-burst stimulation. Brain Stimul. 8, 208–215 (2015).

    Article  PubMed  Google Scholar 

  37. Mayberg, H. S. Limbic–cortical dysregulation: a proposed model of depression. J. Neuropsychiatry Clin. Neurosci. 9, 471–481 (1997).

    Article  PubMed  Google Scholar 

  38. Papez, J. W. A proposed mechanism of emotion. Arch. NeurPsych. 38, 725–743 (1937).

    Article  Google Scholar 

  39. Binder, D. K. & Iskandar, B. J. Modern neurosurgery for psychiatric disorders. Neurosurgery 47, 9–23 (2000).

    PubMed  Google Scholar 

  40. Stockmeier, C. A. & Rajkowska, G. Cellular abnormalities in depression: evidence from postmortem brain tissue. Dialogues Clin. Neurosci. 6, 185 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Cash, R. F. H. et al. Using brain imaging to improve spatial targeting of TMS for depression. Biol. Psychiatry 90, 689–700 (2020).

  42. Goodkind, M. et al. Identification of a common neurobiological substrate for mental illness. JAMA Psychiatry 72, 305–315 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Conradi, H., Ormel, J. & De Jonge, P. Presence of individual (residual) symptoms during depressive episodes and periods of remission: a 3-year prospective study. Psychol. Med. 41, 1165–1174 (2011).

    Article  PubMed  Google Scholar 

  44. Figee, M. & Mayberg, H. The future of personalized brain stimulation. Nat. Med. 27, 196–197 (2021).

    Article  PubMed  Google Scholar 

  45. Li, B. J. et al. A brain network model for depression: from symptom understanding to disease intervention. CNS Neurosci. Ther. 24, 1004–1019 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Siddiqi, S. H. et al. Distinct symptom-specific treatment targets for circuit-based neuromodulation. Am. J. Psychiatry 177, 435–446 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Gray, J. P., Müller, V. I., Eickhoff, S. B. & Fox, P. T. Multimodal abnormalities of brain structure and function in major depressive disorder: a meta-analysis of neuroimaging studies. Am. J. Psychiatry 177, 422–434 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Wang, Q. et al. Normative vs. patient-specific brain connectivity in deep brain stimulation. Neuroimage 224, 117307 (2021).

    Article  PubMed  Google Scholar 

  49. Smith, S. M. et al. Correspondence of the brain’s functional architecture during activation and rest. Proc. Natl Acad. Sci. USA 106, 13040–13045 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Cole, M. W., Bassett, D. S., Power, J. D., Braver, T. S. & Petersen, S. E. Intrinsic and task-evoked network architectures of the human brain. Neuron 83, 238–251 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Seeley, W. W. et al. Dissociable intrinsic connectivity networks for salience processing and executive control. J. Neurosci. 27, 2349–2356 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Fox, M. D. & Raichle, M. E. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat. Rev. Neurosci. 8, 700–711 (2007).

    Article  PubMed  Google Scholar 

  53. Vincent, J. L. et al. Intrinsic functional architecture in the anaesthetized monkey brain. Nature 447, 83–86 (2007).

    Article  PubMed  Google Scholar 

  54. Glasser, M. F. et al. The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage 80, 105–124 (2013).

    Article  PubMed  Google Scholar 

  55. Smith, S. M. et al. Resting-state fMRI in the Human Connectome Project. Neuroimage 80, 144–168 (2013).

    Article  PubMed  Google Scholar 

  56. Tian, Y., Margulies, D. S., Breakspear, M. & Zalesky, A. Topographic organization of the human subcortex unveiled with functional connectivity gradients. Nat. Neurosci. 23, 1421–1432 (2020).

    Article  PubMed  Google Scholar 

  57. Birn, R. M. et al. The effect of scan length on the reliability of resting-state fMRI connectivity estimates. Neuroimage 83, 550–558 (2013).

    Article  PubMed  Google Scholar 

  58. Noble, S. et al. Influences on the test–retest reliability of functional connectivity MRI and its relationship with behavioral utility. Cerebral Cortex 27, 5415–5429 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Choe, A. S. et al. Reproducibility and temporal structure in weekly resting-state fMRI over a period of 3.5 years. PLoS ONE 10, e0140134 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Gordon, E. M. et al. Precision functional mapping of individual human brains. Neuron 95, 791–807.e7 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Cui, Z. et al. Individual variation in functional topography of association networks in youth. Neuron 106, 340–353.e8 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Coalson, T. S., Van Essen, D. C. & Glasser, M. F. The impact of traditional neuroimaging methods on the spatial localization of cortical areas. Proc. Natl Acad. Sci. USA 115, E6356–E6365 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Fox, M. D. et al. Resting-state networks link invasive and noninvasive brain stimulation across diverse psychiatric and neurological diseases. Proc. Natl Acad. Sci. USA 111, E4367–E4375 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Fox, M. D. Mapping symptoms to brain networks with the human connectome. N. Engl. J. Med. 379, 2237–2245 (2018).

    Article  PubMed  Google Scholar 

  65. Zalesky, A., Fornito, A. & Bullmore, E. T. Network-based statistic: identifying differences in brain networks. Neuroimage 53, 1197–1207 (2010).

    Article  PubMed  Google Scholar 

  66. Fitzgerald, P. B., Hoy, K. E., Anderson, R. J. & Daskalakis, Z. J. A study of the pattern of response to rTMS treatment in depression. Depress Anxiety 33, 746–753 (2016).

    Article  PubMed  Google Scholar 

  67. Abbott, L. F. & Nelson, S. B. Synaptic plasticity: taming the beast. Nat. Neurosci. 3, 1178–1183 (2000).

    Article  PubMed  Google Scholar 

  68. Fox, M. D., Halko, M. A., Eldaief, M. C. & Pascual-Leone, A. Measuring and manipulating brain connectivity with resting state functional connectivity magnetic resonance imaging (fcMRI) and transcranial magnetic stimulation (TMS). Neuroimage 62, 2232–2243 (2012).

    Article  PubMed  Google Scholar 

  69. Jenkinson, M., Beckmann, C. F., Behrens, T. E., Woolrich, M. W. & Smith, S. M. FSL. Neuroimage 62, 782–790 (2012).

    Article  PubMed  Google Scholar 

  70. Xia, M., Wang, J. & He, Y. BrainNet Viewer: a network visualization tool for human brain connectomics. PLoS ONE 8, e68910 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  71. Beam, W., Borckardt, J. J., Reeves, S. T. & George, M. S. An efficient and accurate new method for locating the F3 position for prefrontal TMS applications. Brain Stimul. 2, 50–54 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank S. Siddiqi for providing the convergent and lesion circuits. We thank all participants, nurses and staff involved in the collection of clinical data. We thank all who contributed to the original experiments from which the present coordinates are derived and those involved in the Human Connectome Project. We thank all the funding bodies below, and note that the funders had no role in the study design, data collection and analysis, decision to publish or preparation of the manuscript. R.F.H.C. was funded by the Australian Research Council (DE200101708) and Brain and Behavior Research Foundation. A.Z. was supported by the Australian National Health and Medical Research Council Senior Research Fellowship B (ID: 1136649). P.B.F. has received equipment for research from Cervel Neurotech, Medtronic, MagVenture A/S and Brainsway. S.B.E. acknowledges funding by the European Union’s Horizon 2020 Research and Innovation Program (grant agreements 945539 (HBP SGA3) and 826421 (VBC), the Deutsche Forschungsgemeinschaft (DFG), Project-ID 431549029 – SFB 1451) and the National Institute of Health (NIH, 2R01-MH074457).

Author information

Authors and Affiliations

  1. Melbourne Neuropsychiatry Centre, The University of Melbourne, Melbourne, Victoria, Australia

    Robin F. H. Cash & Andrew Zalesky

  2. Department of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia

    Robin F. H. Cash & Andrew Zalesky

  3. Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany

    Veronika I. Müller & Simon B. Eickhoff

  4. Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre, Jülich, Germany

    Veronika I. Müller & Simon B. Eickhoff

  5. School of Medicine and Psychology, The Australian National University, Canberra, Australian Capital Territory, Australia

    Paul B. Fitzgerald

Authors
  1. Robin F. H. Cash
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Veronika I. Müller
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Paul B. Fitzgerald
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Simon B. Eickhoff
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Andrew Zalesky
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conception and study design: R.F.H.C., A.Z., S.B.E. and V.I.M. Preprocessing and data analysis: R.F.H.C. and A.Z. Clinical data was contributed by P.B.F. Interpretation R.F.H.C., A.Z., S.B.E. and V.I.M. Paper writing: R.F.H.C. and A.Z. with input from all authors.

Corresponding author

Correspondence to Robin F. H. Cash.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Mental Health thanks Martin Tik, Ruiyang Ge, Deborah Klooster and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cash, R.F.H., Müller, V.I., Fitzgerald, P.B. et al. Altered brain activity in unipolar depression unveiled using connectomics. Nat. Mental Health 1, 174–185 (2023). https://doi.org/10.1038/s44220-023-00038-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s44220-023-00038-8

  • Springer Nature America, Inc.

This article is cited by

Search

Navigation