Skip to main content
SpringerLink
Log in

Transition and Dynamic Reconfiguration of Whole-Brain Network in Major Depressive Disorder

  • Published:
Molecular Neurobiology Aims and scope Submit manuscript

Abstract

Major depressive disorder (MDD) has been characterized by abnormal brain activity and interactions across the whole-brain functional networks. However, the underlying alteration of brain dynamics remains unclear. Here, we aim to investigate in detail the temporal dynamics of brain activity for MDD, and to characterize the spatiotemporal specificity of whole-brain networks and transitions across them. We developed a hidden Markov model (HMM) analysis for resting-state functional magnetic resonance imaging (fMRI) from two independent cohorts with MDD. In particular, one cohort included 127 MDD patients and 117 gender- and age-matched healthy controls, and the other included 44 MDD patients and 33 controls. We identified brain states characterized by the engagement of distinct functional networks that recurred over time and assessed the dynamical configuration of whole-brain networks and the patterns of activation of states that characterized the MDD groups. Furthermore, we analyzed the community structure of transitions across states to investigate the specificity and abnormality of transitions for MDD. Based on our identification of 12 HMM states, we found that the temporal reconfiguration of states in MDD was associated with the high-order cognition network (DMN), subcortical network (SUB), and sensory and motor networks (SMN). Further, we found that the specific module of transitions was closely related to MDD, which were characterized by two HMM states with opposite activations in DMN, SMN, and subcortical areas. Notably, our results provide novel insights into the dynamical circuit configuration of whole-brain networks for MDD and suggest that brain dynamics should remain a prime target for further MDD research.

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
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Kennedy SH (2008) Core symptoms of major depressive disorder: relevance to diagnosis and treatment. Dialogues Clin Neurosci 10:271–277

    PubMed  PubMed Central  Google Scholar 

  2. Ng CWM, How CH, Ng YP (2016) Major depression in primary care: Making the diagnosis. Singap Med J 57:591–597

    Google Scholar 

  3. Ferrari AJ, Charlson FJ, Norman RE, Patten SB, Freedman G, Murray CJL, Vos T, Whiteford HA (2013) Burden of depressive disorders by country, sex, age, and year: findings from the global burden of disease study 2010. PLoS Med 10:e1001547

    PubMed  PubMed Central  Google Scholar 

  4. Yan C-G, Chen X, Li L, Castellanos FX, Bai TJ, Bo QJ, Cao J, Chen GM et al (2019) Reduced default mode network functional connectivity in patients with recurrent major depressive disorder. Proc Natl Acad Sci U S A 116:9078–9083

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Bora E, Harrison BJ, Yücel M, Pantelis C (2013) Cognitive impairment in euthymic major depressive disorder: a meta-analysis. Psychol Med 43:2017–2026

    CAS  PubMed  Google Scholar 

  6. McIntyre RS, Cha DS, Soczynska JK, Woldeyohannes HO, Gallaugher LA, Kudlow P, Alsuwaidan M, Baskaran A (2013) Cognitive deficits and functional outcomes in major depressive disorder: determinants, substrates, and treatment interventions. Depress Anxiety 30:515–527

    PubMed  Google Scholar 

  7. Lam RW, Kennedy SH, Mclntyre RS, Khullar A (2014) Cognitive dysfunction in major depressive disorder: effects on psychosocial functioning and implications for treatment. Can J Psychiatr 59:649–654

    Google Scholar 

  8. Sheline YI, Price JL, Yan Z, Mintun MA (2010) Resting-state functional MRI in depression unmasks increased connectivity between networks via the dorsal nexus. Proc Natl Acad Sci U S A 107:11020–11025

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Zeng L-L, Shen H, Liu L, Wang L, Li B, Fang P, Zhou Z, Li Y et al (2012) Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis. Brain 135:1498–1507

    PubMed  Google Scholar 

  10. Helm K, Viol K, Weiger TM, Tass PA, Grefkes C, del Monte D, Schiepek G (2018) Neuronal connectivity in major depressive disorder: a systematic review. Neuropsychiatr Dis Treat 14:2715–2737

    PubMed  PubMed Central  Google Scholar 

  11. Hutchison RM, Womelsdorf T, Allen EA, Bandettini PA, Calhoun VD, Corbetta M, Della Penna S, Duyn JH et al (2013) Dynamic functional connectivity: promise, issues, and interpretations. Neuroimage 80:360–378

    PubMed  Google Scholar 

  12. Liao X et al (2015) Spontaneous functional network dynamics and associated structural substrates in the human brain. Front Hum Neurosci 9:478

    PubMed  PubMed Central  Google Scholar 

  13. Fong AHC, Yoo K, Rosenberg MD, Zhang S, Li CSR, Scheinost D, Constable RT, Chun MM (2019) Dynamic functional connectivity during task performance and rest predicts individual differences in attention across studies. Neuroimage 188:14–25

    PubMed  Google Scholar 

  14. Liao W, Chen H, Li J, Ji GJ, Wu GR, Long Z, Xu Q, Duan X et al (2019) Endless fluctuations: temporal dynamics of the amplitude of low frequency fluctuations: temporal dynamics of the amplitude of low frequency fluctuations. IEEE Trans Med Imaging 38:2523–2532

    PubMed  Google Scholar 

  15. Deco G, Jirsa VK, McIntosh AR (2011) Emerging concepts for the dynamical organization of resting-state activity in the brain. Nat Rev Neurosci 12:43–56

    CAS  PubMed  Google Scholar 

  16. Gu S, Betzel RF, Mattar MG, Cieslak M, Delio PR, Grafton ST, Pasqualetti F, Bassett DS (2017) Optimal trajectories of brain state transitions. Neuroimage 148:305–317

    PubMed  PubMed Central  Google Scholar 

  17. Lin P, Yang Y, Gao J, de Pisapia N, Ge S, Wang X, Zuo CS, Jonathan Levitt J et al (2017) Dynamic default mode network across different brain states. Sci Rep 7:46088

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Damaraju E, Allen EA, Belger A, Ford JM, McEwen S, Mathalon DH, Mueller BA, Pearlson GD et al (2014) Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia. Neuroimage Clin 5:298–308

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Du Y et al (2018) Dynamic functional connectivity impairments in early schizophrenia and clinical high-risk for psychosis. Neuroimage 180:632–645

    PubMed  Google Scholar 

  20. Du Y et al (2016) Interaction among subsystems within default mode network diminished in schizophrenia patients: A dynamic connectivity approach. Schizophr Res 170:55–65

    PubMed  Google Scholar 

  21. Yang S, Meng Y, Li J, Fan YS, du L, Chen H, Liao W (2019) Temporal dynamic changes of intrinsic brain activity in schizophrenia with cigarette smoking. Schizophr Res 210:66–72

    PubMed  Google Scholar 

  22. Sheline YI, Raichle ME (2013) Resting state functional connectivity in preclinical Alzheimer’s disease: a review. Biol Psychiatry 74:340–347

    PubMed  PubMed Central  Google Scholar 

  23. de Haan W, van der Flier WM, Koene T, Smits LL, Scheltens P, Stam CJ (2012) Disrupted modular brain dynamics reflect cognitive dysfunction in Alzheimer's disease. Neuroimage 59:3085–3093

    PubMed  Google Scholar 

  24. Li J, Duan X, Cui Q, Chen H, Liao W (2019) More than just statics: temporal dynamics of intrinsic brain activity predicts the suicidal ideation in depressed patients. Psychol Med 49:852–860

    PubMed  Google Scholar 

  25. Nguyen TT, Kovacevic S, Dev SI, Lu K, Liu TT, Eyler LT (2017) Dynamic functional connectivity in bipolar disorder is associated with executive function and processing speed: a preliminary study. Neuropsychology 31:73–83

    PubMed  Google Scholar 

  26. Preti MG, Bolton TA, van de Ville D (2017) The dynamic functional connectome: State-of-the-art and perspectives. Neuroimage 160:41–54

    PubMed  Google Scholar 

  27. Wendling F, Ansari-Asl K, Bartolomei F, Senhadji L (2009) From EEG signals to brain connectivity: a model-based evaluation of interdependence measures. J Neurosci Methods 183:9–18

    PubMed  Google Scholar 

  28. Allen EA, Damaraju E, Plis SM, Erhardt EB, Eichele T, Calhoun VD (2014) Tracking whole-brain connectivity dynamics in the resting state. Cereb Cortex 24:663–676

    PubMed  Google Scholar 

  29. Vidaurre D, Abeysuriya R, Becker R, Quinn AJ, Alfaro-Almagro F, Smith SM, Woolrich MW (2018) Discovering dynamic brain networks from big data in rest and task. Neuroimage 180:646–656

    PubMed  PubMed Central  Google Scholar 

  30. Mokhtari F, Akhlaghi MI, Simpson SL, Wu G, Laurienti PJ (2019) Sliding window correlation analysis: Modulating window shape for dynamic brain connectivity in resting state. Neuroimage 189:655–666

    PubMed  PubMed Central  Google Scholar 

  31. Demirtaş M, Tornador C, Falcón C, López-Solà M, Hernández-Ribas R, Pujol J, Menchón JM, Ritter P et al (2016) Dynamic functional connectivity reveals altered variability in functional connectivity among patients with major depressive disorder. Hum Brain Mapp 37:2918–2930

    PubMed  PubMed Central  Google Scholar 

  32. Zhi D, Calhoun VD, Lv L, Ma X, Ke Q, Fu Z, du Y, Yang Y et al (2018) Aberrant dynamic functional network connectivity and graph properties in major depressive disorder. Front Psychiatry 9:339

    PubMed  PubMed Central  Google Scholar 

  33. Kaiser RH, Whitfield-Gabrieli S, Dillon DG, Goer F, Beltzer M, Minkel J, Smoski M, Dichter G et al (2016) Dynamic resting-state functional connectivity in major depression. Neuropsychopharmacology 41:1822–1830

    CAS  PubMed  Google Scholar 

  34. Hindriks R, Adhikari MH, Murayama Y, Ganzetti M, Mantini D, Logothetis NK, Deco G (2016) Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI? Neuroimage 127:242–256

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Leonardi N, van de Ville D (2015) On spurious and real fluctuations of dynamic functional connectivity during rest. Neuroimage 104:430–436

    PubMed  Google Scholar 

  36. Zalesky A, Breakspear M (2015) Towards a statistical test for functional connectivity dynamics. Neuroimage 114:466–470

    PubMed  Google Scholar 

  37. Vidaurre D, Quinn AJ, Baker AP, Dupret D, Tejero-Cantero A, Woolrich MW (2016) Spectrally resolved fast transient brain states in electrophysiological data. Neuroimage 126:81–95

    PubMed  PubMed Central  Google Scholar 

  38. Vidaurre D, Smith SM, Woolrich MW (2017) Brain network dynamics are hierarchically organized in time. Proc Natl Acad Sci U S A 114:12827–12832

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Stevner ABA, Vidaurre D, Cabral J, Rapuano K, Nielsen SFV, Tagliazucchi E, Laufs H, Vuust P et al (2019) Discovery of key whole-brain transitions and dynamics during human wakefulness and non-REM sleep. Nat Commun 10:1035

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Quinn AJ, Vidaurre D, Abeysuriya R, Becker R, Nobre AC, Woolrich MW (2018) Task-evoked dynamic network analysis through hidden Markov modeling. Front Neurosci 12:603

    PubMed  PubMed Central  Google Scholar 

  41. Vidaurre D, Hunt LT, Quinn AJ, Hunt BAE, Brookes MJ, Nobre AC, Woolrich MW (2018) Spontaneous cortical activity transiently organises into frequency specific phase-coupling networks. Nat Commun 9:2987

    PubMed  PubMed Central  Google Scholar 

  42. Vidaurre D, Myers NE, Stokes M, Nobre AC, Woolrich MW (2019) Temporally unconstrained decoding reveals consistent but time-varying stages of stimulus processing. Cereb Cortex 29:863–874

    PubMed  Google Scholar 

  43. Cheng W, Rolls ET, Qiu J, Yang D, Ruan H, Wei D, Zhao L, Meng J et al (2018) Functional connectivity of the precuneus in unmedicated patients with depression. Biol Psychiatry Cogn Neurosci Neuroimaging 3:1040–1049

    PubMed  Google Scholar 

  44. Wei D et al (2020) The reductions in the subcallosal region cortical volume and surface area in major depressive disorder across the adult life span. Psychol Med 50(3):422–430

  45. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, Mazoyer B, Joliot M (2002) Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15:273–289

    CAS  PubMed  Google Scholar 

  46. Newman MEJ (2006) Modularity and community structure in networks. Proc Natl Acad Sci U S A 103:8577–8582

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Rubinov M, Sporns O (2010) Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52:1059–1069

    PubMed  Google Scholar 

  48. Liu CH, Li F, Li SF, Wang YJ, Tie CL, Wu HY, Zhou Z, Zhang D et al (2012) Abnormal baseline brain activity in bipolar depression: a resting state functional magnetic resonance imaging study. Psychiatry Res 203:175–179

    PubMed  Google Scholar 

  49. Avery J, Drevets WC, Moseman S, Bodurka J, Barcalow J, Simmons WK (2013) Major depressive disorder is associated with abnormal interoceptive activity and functional connectivity in the insula. Biol Psychiatry 76:258–266

    PubMed  PubMed Central  Google Scholar 

  50. Wu X, He H, Shi L, Xia Y, Zuang K, Feng Q, Zhang Y, Ren Z et al (2019) Personality traits are related with dynamic functional connectivity in major depression disorder: A resting-state analysis. J Affect Disord 245:1032–1042

    PubMed  Google Scholar 

  51. Supekar K, Cai W, Krishnadas R, Palaniyappan L, Menon V (2019) Dysregulated brain dynamics in a triple-network saliency model of schizophrenia and its relation to psychosis. Biol Psychiatry 85:60–69

    PubMed  Google Scholar 

  52. Li C, Xia L, Ma J, Li S, Liang S, Ma X, Wang T, Li M et al (2019) Dynamic functional abnormalities in generalized anxiety disorders and their increased network segregation of a hyperarousal brain state modulated by insomnia. J Affect Disord 246:338–345

    PubMed  Google Scholar 

  53. Schumacher J, Peraza LR, Firbank M, Thomas AJ, Kaiser M, Gallagher P, O'Brien JT, Blamire AM et al (2019) Dynamic functional connectivity changes in dementia with Lewy bodies and Alzheimer’s disease. Neuroimage Clin 22:101812

    PubMed  PubMed Central  Google Scholar 

  54. Fu Z, Caprihan A, Chen J, du Y, Adair JC, Sui J, Rosenberg GA, Calhoun VD (2019) Altered static and dynamic functional network connectivity in Alzheimer’s disease and subcortical ischemic vascular disease: shared and specific brain connectivity abnormalities. Hum Brain Mapp 40:3203–3221

    PubMed  PubMed Central  Google Scholar 

  55. Córdova-Palomera A, Kaufmann T, Persson K, Alnæs D, Doan NT, Moberget T, Lund MJ, Barca ML et al (2017) Disrupted global metastability and static and dynamic brain connectivity across individuals in the Alzheimer’s disease continuum. Sci Rep 7:40268

    PubMed  PubMed Central  Google Scholar 

  56. Kaiser RH, Andrews-Hanna JR, Wager TD, Pizzagalli DA (2015) Large-scale network dysfunction in major depressive disorder: a meta-analysis of resting-state functional connectivity. JAMA Psychiatry 72:603–611

    PubMed  PubMed Central  Google Scholar 

  57. Hamilton JP, Farmer M, Fogelman P, Gotlib IH (2015) Depressive rumination, the default-mode network, and the dark matter of clinical neuroscience. Biol Psychiatry 78:224–230

    PubMed  PubMed Central  Google Scholar 

  58. Moreno-Ortega M, Prudic J, Rowny S, Patel GH, Kangarlu A, Lee S, Grinband J, Palomo T et al (2019) Resting state functional connectivity predictors of treatment response to electroconvulsive therapy in depression. Sci Rep 9:5071

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Wang L et al (2019) Altered functional connectivity patterns of insular subregions in major depressive disorder after electroconvulsive therapy. Brain Imaging Behav. https://doi.org/10.1007/s11682-018-0013-z

  60. Wei Q, Bai T, Chen Y, Ji G, Hu X, Xie W, Xiong Z, Zhu D et al (2018) The changes of functional connectivity strength in electroconvulsive therapy for depression: a longitudinal study. Front Neurosci 12:661

    PubMed  PubMed Central  Google Scholar 

  61. Bai T, Wei Q, Zu M, Xie W, Wang J, Gong-Jun J, Yu F, Tian Y et al (2019) Functional plasticity of the dorsomedial prefrontal cortex in depression reorganized by electroconvulsive therapy: Validation in two independent samples. Hum Brain Mapp 40:465–473

    PubMed  Google Scholar 

  62. Ji G-J, Liao W, Chen F-F, Zhang L, Wang K (2017) Low-frequency blood oxygen level-dependent fluctuations in the brain white matter: More than just noise: more than just noise. Sci Bull 62:656–657

    CAS  Google Scholar 

  63. Gawryluk JR, Mazerolle EL, D'Arcy RCN (2014) Does functional MRI detect activation in white matter? A review of emerging evidence, issues, and future directions: a review of emerging evidence, issues, and future directions. Front Neurosci 8:239

    PubMed  PubMed Central  Google Scholar 

  64. Li J, Biswal BB, Wang P, Duan X, Cui Q, Chen H, Liao W (2019) Exploring the functional connectome in white matter. Hum Brain Mapp 40:4331–4344

    PubMed  PubMed Central  Google Scholar 

  65. Ding Z, Huang Y, Bailey SK, Gao Y, Cutting LE, Rogers BP, Newton AT, Gore JC (2018) Detection of synchronous brain activity in white matter tracts at rest and under functional loading. Proc Natl Acad Sci U S A 115:595–600

    CAS  PubMed  Google Scholar 

Download references

Funding

This work was supported in part by the National Natural Science Foundation of China (Grant Number:61976209, 61906188 and 81701785), in part by the International Collaboration Key Project of Chinese  Academy of Sciences (CAS) (Grant Number: 173211KYSB20190024), in part by the Fundamental Research Funds fotr the Central Universities (Grant Number: SWU118065), and in part by the Strategic Priority Research Program of CAS (Grant Number: XDB32040000).

Author information

Author notes

  1. Shengpei Wang, Hongwei Wen and Xiaopeng Hu contributed equally to this work and should be considered as co-first authors.

Authors and Affiliations

  1. Research Center for Brain-inspired Intelligence and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Shengpei Wang, Shuang Qiu & Huiguang He

  2. University of Chinese Academy of Sciences, Beijing, China

    Shengpei Wang & Huiguang He

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

    Hongwei Wen & Jiang Qiu

  4. School of Psychology, Southwest University, Chongqing, China

    Hongwei Wen & Jiang Qiu

  5. Department of Radiology, the First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China

    Xiaopeng Hu & Yinfeng Qian

  6. Institute of Neuroscience, Chongqing Medical University, Chongqing, China

    Peng Xie

  7. Chongqing Key Laboratory of Neurobiology, Chongqing, China

    Peng Xie

  8. Department of Neurology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, China

    Peng Xie

  9. Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Beijing, China

    Huiguang He

Authors
  1. Shengpei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hongwei Wen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaopeng Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Peng Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shuang Qiu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yinfeng Qian
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jiang Qiu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Huiguang He
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Jiang Qiu or Huiguang He.

Ethics declarations

Competing Interests

The authors declare that they have no competing interests.

Data and Code Availability

The datasets generated and/or analyzed as well as code used during the current study are available upon reasonable request.

Additional information

Publisher’s Note

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

Electronic supplementary material

ESM 1

(DOCX 4415 kb).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, S., Wen, H., Hu, X. et al. Transition and Dynamic Reconfiguration of Whole-Brain Network in Major Depressive Disorder. Mol Neurobiol 57, 4031–4044 (2020). https://doi.org/10.1007/s12035-020-01995-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12035-020-01995-2

Keywords

Search

Navigation