Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Severity of current depression and remission status are associated with structural connectome alterations in major depressive disorder


Major depressive disorder (MDD) is associated to affected brain wiring. Little is known whether these changes are stable over time and hence might represent a biological predisposition, or whether these are state markers of current disease severity and recovery after a depressive episode. Human white matter network (“connectome”) analysis via network science is a suitable tool to investigate the association between affected brain connectivity and MDD. This study examines structural connectome topology in 464 MDD patients (mean age: 36.6 years) and 432 healthy controls (35.6 years). MDD patients were stratified categorially by current disease status (acute vs. partial remission vs. full remission) based on DSM-IV criteria. Current symptom severity was assessed continuously via the Hamilton Depression Rating Scale (HAMD). Connectome matrices were created via a combination of T1-weighted magnetic resonance imaging (MRI) and tractography methods based on diffusion-weighted imaging. Global tract-based metrics were not found to show significant differences between disease status groups, suggesting conserved global brain connectivity in MDD. In contrast, reduced global fractional anisotropy (FA) was observed specifically in acute depressed patients compared to fully remitted patients and healthy controls. Within the MDD patients, FA in a subnetwork including frontal, temporal, insular, and parietal nodes was negatively associated with HAMD, an effect remaining when correcting for lifetime disease severity. Therefore, our findings provide new evidence of MDD to be associated with structural, yet dynamic, state-dependent connectome alterations, which covary with current disease severity and remission status after a depressive episode.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Get just this article for as long as you need it


Prices may be subject to local taxes which are calculated during checkout

Fig. 1
Fig. 2


  1. Friston K, Brown HR, Siemerkus J, Stephan K. The dysconnection hypothesis. Schizophr Res. 2016.

  2. Andreasen NC. Schizophrenia: The fundamental questions. Brain Res Rev. 2000

  3. Li BJ, Friston K, Mody M, Wang HN, Lu HB, Hu DW. A brain network model for depression: From symptom understanding to disease intervention. CNS Neurosci Ther. 2018.

  4. Soares JM, Marques P, Alves V, Sousa N. A hitchhiker’s guide to diffusion tensor imaging. Front Neurosci. 2013;7:1–14.

    Article  Google Scholar 

  5. Van Den Heuvel MP, Sporns O. Network hubs in the human brain. Trends Cogn Sci. 2013;17:683–96.

    Article  Google Scholar 

  6. De Reus MA, Van Den Heuvel MP. NeuroImage the parcellation-based connectome: limitations and extensions. Neuroimage. 2013;80:397–404.

    Article  Google Scholar 

  7. van den Heuvel MP, Mandl RC, Stam CJ, Kahn RS, Hulshoff Pol HE. Aberrant frontal and temporal complex network structure in schizophrenia: a graph theoretical analysis. J Neurosci. 2010;30:15915–26.

    Article  Google Scholar 

  8. Collin G, Van Den Heuvel MP, Abramovic L, Vreeker A, De Reus MA, Van Haren NEM, et al. Brain network analysis reveals affected connectome structure in bipolar I disorder. Hum Braim Mapp. 2016; 134:122–34.

  9. Korgaonkar MS, Fornito A, Williams LM, Grieve SM. Abnormal structural networks characterize major depressive disorder: a connectome analysis. Biol Psychiatry. 2014;76:567–74.

    Article  Google Scholar 

  10. Liu H, Zhao K, Shi J, Chen Y, Yao Z, Lu Q. Topological properties of brain structural networks represent early predictive characteristics for the occurrence of bipolar disorder in patients with major depressive disorder: a 7-year prospective longitudinal study. Front Psychiatry. 2018.

  11. Zheng K, Wang H, Li J, Yan B, Liu J, Xi Y, et al. Structural networks analysis for depression combined with graph theory and the properties of fiber tracts via diffusion tensor imaging. Neurosci Lett. 2019.

  12. Sacchet MD, Prasad G, Foland-Ross LC, Thompson PM, Gotlib IH. Support vector machine classification of major depressive disorder using diffusion-weighted neuroimaging and graph theory. Front Psychiatry. 2015; 6.

  13. Tymofiyeva O, Connolly CG, Ho TC, Sacchet MD, Henje Blom E, LeWinn KZ, et al. DTI-based connectome analysis of adolescents with major depressive disorder reveals hypoconnectivity of the right caudate. J Affect Disord. 2017.

  14. Korgaonkar MS, Grieve SM, Koslow SH, Gabrieli JDE, Gordon E, Williams LM. Loss of white matter integrity in major depressive disorder: evidence using tract-based spatial statistical analysis of diffusion tensor imaging. Hum Brain Mapp. 2011;32:2161–71.

    Article  Google Scholar 

  15. Myung W, Han CE, Fava M, Mischoulon D, Papakostas GI, Heo JY, et al. Reduced frontal-subcortical white matter connectivity in association with suicidal ideation in major depressive disorder. Transl Psychiatry. 2016.

  16. Wise T, Radua J, Nortje G, Cleare AJ, Young AH, Arnone D. Voxel-based meta-Analytical evidence of structural disconnectivity in major depression and bipolar disorder. Biol Psychiatry. 2016;79:293–302.

    Article  Google Scholar 

  17. Murphy ML, Frodl T. Meta-analysis of diffusion tensor imaging studies shows altered fractional anisotropy occurring in distinct brain areas in association with depression. Biol Mood Anxiety Disord. 2011;1:3.

    PubMed  PubMed Central  Google Scholar 

  18. Repple J, Zaremba D, Meinert S, Dannlowski U. Time heals all wounds? A 2-year longitudinal diffusion tensor imaging study in major depressive disorder. J Psychiatry Neurosci. 2019;44:1–7.

  19. Kircher T, Wöhr M, Nenadic I, Schwarting R, Schratt G, Alferink J, et al. Neurobiology of the major psychoses: a translational perspective on brain structure and function—the FOR2107 consortium. Eur Arch Psychiatry Clin Neurosci. 2018.

  20. Vogelbacher C, Möbius TWD, Sommer J, Schuster V, Dannlowski U, Kircher T, et al. The Marburg-Münster Affective Disorders Cohort Study (MACS): a quality assurance protocol for MR neuroimaging data. Neuroimage. 2018;172:450–60.

    Article  Google Scholar 

  21. Wittchen H-U, Wunderlich U, Gruschwitz S, Zaudig M. Strukturiertes Klinisches Interview fuer DSM-VI (SKID). Goettingen: Hogrefe; 1997.

  22. Hagmann P, Cammoun L, Gigandet X, Meuli R, Honey CJ, Van Wedeen J, et al. Mapping the structural core of human cerebral cortex. PLoS Biol. 2008.

  23. Cammoun L, Gigandet X, Meskaldji D, Thiran JP, Sporns O, Do KQ, et al. Mapping the human connectome at multiple scales with diffusion spectrum MRI. J Neurosci Methods. 2012.

  24. Mori S, Van Zijl PCM. Fiber tracking: principles and strategies—a technical review. NMR Biomed. 2002.

  25. Sarwar T, Ramamohanarao K, Zalesky A. Mapping connectomes with diffusion MRI: deterministic or probabilistic tractography? Magn Reson Med. 2019.

  26. de Reus MA, van den Heuvel MP. Estimating false positives and negatives in brain networks. Neuroimage. 2013.

  27. Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage. 2010;52:1059–69.

    Article  Google Scholar 

  28. van den Heuvel MP, Sporns O. Rich-Club Organization of the Human Connectome. J Neurosci. 2011;31:15775–86.

    Article  Google Scholar 

  29. Hamilton M. A rating scale for depression. J Neurol Neurosurg Psychiatry. 1960;23:56–62.

    Article  CAS  Google Scholar 

  30. Repple J, Meinert S, Grotegerd D, Kugel H, Redlich R, Dohm K, et al. A voxel-based diffusion tensor imaging study in unipolar and bipolar depression. Bipolar Disord. 2017;19:23–31.

    Article  Google Scholar 

  31. Redlich R, Almeida JJR, Grotegerd D, Opel N, Kugel H, Heindel W, et al. Brain morphometric biomarkers distinguishing unipolar and bipolar depression. JAMA Psychiatry. 2014;71:1222.

    Article  Google Scholar 

  32. Opel N, Redlich R, Dohm K, Zaremba D, Goltermann J, Repple J, et al. Mediation of the influence of childhood maltreatment on depression relapse by cortical structure: a 2-year longitudinal observational study. Lancet Psychiatry. 2019.

    Article  PubMed  Google Scholar 

  33. Zalesky A, Fornito A, Bullmore ET. Network-based statistic: Identifying differences in brain networks. Neuroimage. 2010;53:1197–207.

    Article  Google Scholar 

  34. Han KM, De Berardis D, Fornaro M, Kim YK. Differentiating between bipolar and unipolar depression in functional and structural MRI studies. Prog Neuro-Psychopharmacol Biol Psychiatry. 2019;91:20–7.

    Article  Google Scholar 

  35. Hamilton JP, Etkin A, Furman DJ, Lemus MG, Johnson RF, Gotlib IH. Functional neuroimaging of major depressive disorder: a meta-analysis and new integration of baseline activation and neural response data. Am J Psychiatry. 2012.

  36. Helm K, Viol K, Weiger TM, Tass PA, Grefkes C, Del Monte D, et al. Neuronal connectivity in major depressive disorder: a systematic review. Neuropsychiatr Dis Treat. 2018;14:2715–37.

    Article  Google Scholar 

  37. Bracht T, Jones DK, Müller TJ, Wiest R, Walther S. Limbic white matter microstructure plasticity reflects recovery from depression. J Affect Disord. 2015;170:143–9.

    Article  Google Scholar 

  38. Doolin K, Andrews S, Carballedo A, McCarthy H, O’Hanlon E, Tozzi L, et al. Longitudinal diffusion weighted imaging of limbic regions in patients with major depressive disorder after 6 years and partial to full remission. Psychiatry Res - Neuroimaging. 2019;287:75–86.

    Article  Google Scholar 

  39. Benedetti F, Poletti S, Hoogenboezem TA, Mazza E, Ambrée O, de Wit H, et al. Inflammatory cytokines influence measures of white matter integrity in bipolar disorder. J Affect Disord. 2016;202:1–9.

    Article  CAS  Google Scholar 

  40. Miller AH, Raison CL. The role of inflammation in depression: from evolutionary imperative to modern treatment target. Nat Rev Immunol. 2016;16:22–34.

    Article  CAS  Google Scholar 

  41. Lamers F, Milaneschi Y, Smit JH, Schoevers RA, Wittenberg G, Penninx BWJH. Longitudinal association between depression and inflammatory markers: results from the Netherlands study of depression and anxiety. Biol Psychiatry. 2019;85:829–37.

    Article  Google Scholar 

  42. Hemanth Kumar BS, Mishra SK, Trivedi R, Singh S, Rana P, Khushu S. Demyelinating evidences in CMS rat model of depression: A DTI study at 7T. Neuroscience. 2014;275:12–21.

    Article  CAS  Google Scholar 

  43. Cui L-B, Wei Y, Xi Y-B, Griffa A, De Lange SC, Kahn RS, et al. Connectome-based patterns of first-episode medication-naïve patients with schizophrenia. Schizophr Bull. 2019.

  44. Collin G, van den Heuvel MP, Abramovic L, Vreeker A, de Reus MA, van Haren NEM, et al. Brain network analysis reveals affected connectome structure in bipolar I disorder. Hum Brain Mapp. 2016;37:122–34.

    Article  Google Scholar 

  45. Van Den Heuvel MP, Sporns O, Collin G, Scheewe T, Mandl RCW, Cahn W, et al. Abnormal rich club organization and functional brain dynamics in schizophrenia. JAMA Psychiatry. 2013;70:783–92.

    Article  Google Scholar 

  46. Crossley NA, Mechelli A, Scott J, Carletti F, Fox PT, Mcguire P, et al. The hubs of the human connectome are generally implicated in the anatomy of brain disorders. Brain. 2014;137:2382–95.

    Article  Google Scholar 

  47. Collin G, de Nijs J, Hulshoff Pol HE, Cahn W, van den Heuvel MP. Connectome organization is related to longitudinal changes in general functioning, symptoms and IQ in chronic schizophrenia. Schizophr Res. 2016;173:166–73.

    Article  CAS  Google Scholar 

  48. Jones DK, Knösche TR, Turner R. White matter integrity, fiber count, and other fallacies: The do’s and don’ts of diffusion MRI. Neuroimage. 2013;73:239–54.

    Article  Google Scholar 

  49. Repple J, Karliczek G, Meinert S, Förster K, Grotegerd D, Goltermann J, et al. Variation of HbA1c affects cognition and white matter microstructure in healthy, young adults. Mol Psychiatry. 2019. [Epub ahead of print].

  50. van Erp TGM, Hibar DP, Rasmussen JM, Glahn DC, Pearlson GD, Andreassen OA, et al. Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium. Mol Psychiatry. 2016;21:547–53.

    Article  Google Scholar 

  51. Allen M, Poggiali D, Whitaker K, Marshall TR, Kievit RA. Raincloud plots: A multi-platform tool for robust data visualization. Wellcome Open Res. 2019; 4.

  52. Xia M, Wang J, He Y. BrainNet Viewer: a network visualization tool for human brain connectomics. PLoS ONE. 2013; 8.

Download references


This work is part of the German multicenter consortium “Neurobiology of Affective Disorders. A translational perspective on brain structure and function“, funded by the German Research Foundation (Deutsche Forschungsgemeinschaft DFG; Forschungsgruppe/Research Unit FOR2107). Principal investigators (PIs) with respective areas of responsibility in the FOR2107 consortium are: Work Package WP1, FOR2107/MACS cohort and brain imaging: Tilo Kircher (speaker FOR2107; DFG grant numbers KI 588/14-1, KI 588/14-2), Udo Dannlowski (co-speaker FOR2107; DA 1151/5-1, DA 1151/5-2), Axel Krug (KR 3822/5-1, KR 3822/7-2), Igor Nenadic (NE 2254/1-2), Carsten Konrad (KO 4291/3-1). CP1, biobank: Petra Pfefferle (PF 784/1-1, PF 784/1-2), Harald Renz (RE 737/20-1, 737/20-2). CP2, administration. Tilo Kircher (KI 588/15-1, KI 588/17-1), Udo Dannlowski (DA 1151/6-1), Carsten Konrad (KO 4291/4-1). Martijn van den Heuvel was supported by an ALW open (ALWOP.179), and VIDI (452-16-015) grant from the Netherlands Organization for Scientific Research (NWO) and a Fellowship of MQ. Data access and responsibility: All take responsibility for the integrity of the respective study data and their components. All authors and coauthors had full access to all study data. Acknowledgements and members by Work Package (WP): WP1: Henrike Bröhl, Katharina Brosch, Bruno Dietsche, Rozbeh Elahi, Jennifer Engelen, Sabine Fischer, Jessica Heinen, Svenja Klingel, Felicitas Meier, Tina Meller, Torsten Sauder, Simon Schmitt, Frederike Stein, Annette Tittmar, Dilara Yüksel (Dept. of Psychiatry, Marburg University). Mechthild Wallnig, Rita Werner (Core-Facility Brainimaging, Marburg University). Carmen Schade-Brittinger, Maik Hahmann (Coordinating Center for Clinical Trials, Marburg). Michael Putzke (Psychiatric Hospital, Friedberg). Rolf Speier, Lutz Lenhard (Psychiatric Hospital, Haina). Birgit Köhnlein (Psychiatric Practice, Marburg). Peter Wulf, Jürgen Kleebach, Achim Becker (Psychiatric Hospital Hephata, Schwalmstadt-Treysa). Ruth Bär (Care facility Bischoff, Neunkirchen). Matthias Müller, Michael Franz, Siegfried Scharmann, Anja Haag, Kristina Spenner, Ulrich Ohlenschläger (Psychiatric Hospital Vitos, Marburg). Matthias Müller, Michael Franz, Bernd Kundermann (Psychiatric Hospital Vitos, Gießen). Christian Bürger, Katharina Dohm, Fanni Dzvonyar, Verena Enneking, Stella Fingas, Katharina Förster, Janik Goltermann, Dominik Grotegerd, Hannah Lemke, Susanne Meinert, Nils Opel, Ronny Redlich, Jonathan Repple, Kordula Vorspohl, Bettina Walden, Dario Zaremba (Dept. of Psychiatry, University of Münster). Harald Kugel, Jochen Bauer, Walter Heindel, Birgit Vahrenkamp (Dept. of Clinical Radiology, University of Münster). Gereon Heuft, Gudrun Schneider (Dept. of Psychosomatics and Psychotherapy, University of Münster). Thomas Reker (LWL-Hospital Münster). Gisela Bartling (IPP Münster). Ulrike Buhlmann (Dept. of Clinical Psychology, University of Münster). CP1: Julian Glandorf, Fabian Kormann, Arif Alkan, Fatana Wedi, Lea Henning, Alena Renker, Karina Schneider, Elisabeth Folwarczny, Dana Stenzel, Kai Wenk, Felix Picard, Alexandra Fischer, Sandra Blumenau, Beate Kleb, Doris Finholdt, Elisabeth Kinder, Tamara Wüst, Elvira Przypadlo, Corinna Brehm (Comprehensive Biomaterial Bank Marburg, Marburg University). All authors have approved the final article.


This work was funded by the German Research Foundation (DFG, grant FOR2107 DA 1151/5-1 and DA 1151/5-2 to UD; SFB-TRR58, Projects C09 and Z02 to UD), the Interdisciplinary Center for Clinical Research (IZKF) of the medical faculty of Münster (grant Dan3/012/17 to UD), IMF Münster RE111604 to RR und RE111722 to RR, IMF Münster RE 22 17 07 to Jonathan Repple and the Deanery of the Medical Faculty of the University of Münster. The FOR2107 cohort project (WP1) was approved by the Ethics Committees of the Medical Faculties, University of Marburg (AZ: 07/14), and University of Münster (AZ: 2014-422-b-S).

Author information

Authors and Affiliations


Corresponding author

Correspondence to Jonathan Repple.

Ethics declarations

Conflict of interest

TK received unrestricted educational grants from Servier, Janssen, Recordati, Aristo, Otsuka, neuraxpharm. MW is scientific advisor of Avisoft Bioacoustics. This cooperation has no relevance to the work that is covered in the manuscript. The other authors declare no conflict of interest.

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

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Repple, J., Mauritz, M., Meinert, S. et al. Severity of current depression and remission status are associated with structural connectome alterations in major depressive disorder. Mol Psychiatry 25, 1550–1558 (2020).

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links