Skip to main content
Log in

Aberrant temporal–spatial complexity of intrinsic fluctuations in major depression

  • Original Paper
  • Published:
European Archives of Psychiatry and Clinical Neuroscience Aims and scope Submit manuscript

Abstract

Accumulating evidence suggests that the brain is highly dynamic; thus, investigation of brain dynamics especially in brain connectivity would provide crucial information that stationary functional connectivity could miss. This study investigated temporal expressions of spatial modes within the default mode network (DMN), salience network (SN) and cognitive control network (CCN) using a reliable data-driven co-activation pattern (CAP) analysis in two independent data sets. We found enhanced CAP-to-CAP transitions of the SN in patients with MDD. Results suggested enhanced flexibility of this network in the patients. By contrast, we also found reduced spatial consistency and persistence of the DMN in the patients, indicating reduced variability and stability in individuals with MDD. In addition, the patients were characterized by prominent activation of mPFC. Moreover, further correlation analysis revealed that persistence and transitions of RCCN were associated with the severity of depression. Our findings suggest that functional connectivity in the patients may not be simply attenuated or potentiated, but just alternating faster or slower among more complex patterns. The aberrant temporal–spatial complexity of intrinsic fluctuations reflects functional diaschisis of resting-state networks as characteristic of patients with MDD.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Availability of data and code

The data that support the findings of this study as well as code used during the current study are available from the corresponding author upon reasonable request.

References

  1. Song Z, Zhang M, Huang P (2016) Aberrant emotion networks in early major depressive disorder patients: an eigenvector centrality mapping study. Transl Psychiatry 6:e819. https://doi.org/10.1038/tp.2016.81

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Belmaker RH, Agam G (2008) Major depressive disorder. N Engl J Med 358:55–68

    Article  CAS  PubMed  Google Scholar 

  3. Fava M, Kendler KS (2000) Major depressive disorder. Neuron 28:335–341. https://doi.org/10.1016/S0896-6273(00)00112-4

    Article  CAS  PubMed  Google Scholar 

  4. Otte C, Gold SM, Penninx BW et al (2016) Major depressive disorder. Nat Rev Dis Prim 2:16065. https://doi.org/10.1038/nrdp.2016.65

    Article  PubMed  Google Scholar 

  5. Mulders PC, van Eijndhoven PF, Schene AH et al (2015) Resting-state functional connectivity in major depressive disorder: a review. Neurosci Biobehav Rev 56:330–344. https://doi.org/10.1016/j.neubiorev.2015.07.014

    Article  PubMed  Google Scholar 

  6. Ye M, Yang T, Qing P et al (2015) Changes of functional brain networks in major depressive disorder: a graph theoretical analysis of resting-state fMRI. PLoS One 10:e0133775. https://doi.org/10.1371/journal.pone.0133775

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Dutta A, McKie S, Deakin JFW (2014) Resting state networks in major depressive disorder. Psychiatry Res 224:139–151. https://doi.org/10.1016/j.pscychresns.2014.10.003

    Article  PubMed  Google Scholar 

  8. Sheng J, Shen Y, Qin Y et al (2018) Spatiotemporal, metabolic, and therapeutic characterization of altered functional connectivity in major depressive disorder. Hum Brain Mapp 39:1957–1971. https://doi.org/10.1002/hbm.23976

    Article  PubMed  PubMed Central  Google Scholar 

  9. Wang Y, Yang S, Sun W et al (2016) Altered functional interaction hub between affective network and cognitive control network in patients with major depressive disorder. Behav Brain Res 298:301–309. https://doi.org/10.1016/j.bbr.2015.10.040

    Article  PubMed  Google Scholar 

  10. Liu C-H, Guo J, Lu S-L et al (2018) Increased salience network activity in patients with insomnia complaints in major depressive disorder. Front Psychiatry 9:93. https://doi.org/10.3389/fpsyt.2018.00093

    Article  PubMed  PubMed Central  Google Scholar 

  11. Li B, Liu L, Friston KJ et al (2013) A treatment-resistant default mode subnetwork in major depression. Biol Psychiatry 74:48–54. https://doi.org/10.1016/j.biopsych.2012.11.007

    Article  PubMed  Google Scholar 

  12. Ho TC, Connolly CG, Henje Blom E et al (2015) Emotion-dependent functional connectivity of the default mode network in adolescent depression. Biol Psychiatry 78:635–646. https://doi.org/10.1016/j.biopsych.2014.09.002

    Article  PubMed  Google Scholar 

  13. Bluhm R, Williamson P, Lanius R et al (2009) Resting state default-mode network connectivity in early depression using a seed region-of-interest analysis: decreased connectivity with caudate nucleus. Psychiatry Clin Neurosci 63:754–761. https://doi.org/10.1111/j.1440-1819.2009.02030.x

    Article  PubMed  Google Scholar 

  14. Yan CG, Chen X, Li L et al (2019) Reduced default mode network functional connectivity in patients with recurrent major depressive disorder. Proc Natl Acad Sci 116:201900390

    Article  Google Scholar 

  15. Allen EA, Damaraju E, Plis SM et al (2014) Tracking whole-brain connectivity dynamics in the resting state. Cereb Cortex 24:663–676. https://doi.org/10.1093/cercor/bhs352

    Article  PubMed  Google Scholar 

  16. Chang C, Glover GH (2010) Time-frequency dynamics of resting-state brain connectivity measured with fMRI. Neuroimage 50:81–98. https://doi.org/10.1016/j.neuroimage.2009.12.011

    Article  PubMed  Google Scholar 

  17. Smith SM, Miller KL, Moeller S et al (2012) Temporally-independent functional modes of spontaneous brain activity. Proc Natl Acad Sci U S A 109:3131–3136. https://doi.org/10.1073/pnas.1121329109

    Article  PubMed  PubMed Central  Google Scholar 

  18. Di X, Biswal BB (2020) Intersubject consistent dynamic connectivity during natural vision revealed by functional MRI. Neuroimage 216:116698. https://doi.org/10.1016/j.neuroimage.2020.116698

    Article  PubMed  Google Scholar 

  19. Kang J, Wang L, Yan C et al (2011) Characterizing dynamic functional connectivity in the resting brain using variable parameter regression and Kalman filtering approaches. Neuroimage 56:1222–1234. https://doi.org/10.1016/j.neuroimage.2011.03.033

    Article  PubMed  Google Scholar 

  20. Hutchison RM, Womelsdorf T, Gati JS et al (2013) Resting-state networks show dynamic functional connectivity in awake humans and anesthetized macaques. Hum Brain Mapp 34:2154–2177. https://doi.org/10.1002/hbm.22058

    Article  PubMed  Google Scholar 

  21. Fiorenzato E, Strafella AP, Kim J et al (2019) Dynamic functional connectivity changes associated with dementia in Parkinson’s disease. Brain 142:2860–2872. https://doi.org/10.1093/brain/awz192

    Article  PubMed  PubMed Central  Google Scholar 

  22. Watanabe T, Rees G (2017) Brain network dynamics in high-functioning individuals with autism. Nat Commun 8:16048. https://doi.org/10.1038/ncomms16048

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Wang J, Wang Y, Huang H et al (2020) Abnormal dynamic functional network connectivity in unmedicated bipolar and major depressive disorders based on the triple-network model. Psychol Med 50:465–474. https://doi.org/10.1017/S003329171900028X

    Article  PubMed  Google Scholar 

  24. Handwerker DA, Roopchansingh V, Gonzalez-Castillo J, Bandettini PA (2012) Periodic changes in fMRI connectivity. Neuroimage 63:1712–1719. https://doi.org/10.1016/j.neuroimage.2012.06.078

    Article  PubMed  Google Scholar 

  25. Jones DT, Vemuri P, Murphy MC et al (2012) Non-stationarity in the ‘resting brain’s’ modular architecture. PLoS One 7:e39731. https://doi.org/10.1371/journal.pone.0039731

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Demirtaş M, Tornador C, Falcón C et al (2016) Dynamic functional connectivity reveals altered variability in functional connectivity among patients with major depressive disorder. Hum Brain Mapp 37:2918–2930. https://doi.org/10.1002/hbm.23215

    Article  PubMed  PubMed Central  Google Scholar 

  27. Zhi D, Calhoun VD, Lv L et al (2018) Aberrant dynamic functional network connectivity and graph properties in major depressive disorder. Front Psychiatry 9:1–11. https://doi.org/10.3389/fpsyt.2018.00339

    Article  Google Scholar 

  28. Liu X, Duyn JH (2013) Time-varying functional network information extracted from brief instances of spontaneous brain activity. Proc Natl Acad Sci U S A 110:4392–4397. https://doi.org/10.1073/pnas.1216856110

    Article  PubMed  PubMed Central  Google Scholar 

  29. Marshall E, Nomi JS, Dirks B et al (2020) Coactivation pattern analysis reveals altered salience network dynamics in children with autism spectrum disorder. Netw Neurosci 4:1219–1234. https://doi.org/10.1162/netn_a_00163

    Article  PubMed  PubMed Central  Google Scholar 

  30. Chen JE, Chang C, Greicius MD, Glover GH (2015) Introducing co-activation pattern metrics to quantify spontaneous brain network dynamics. Neuroimage 111:476–488. https://doi.org/10.1016/j.neuroimage.2015.01.057

    Article  PubMed  Google Scholar 

  31. Xue W, Kang J, Bowman FD et al (2014) Identifying functional co-activation patterns in neuroimaging studies via poisson graphical models. Biometrics 70:812–822. https://doi.org/10.1111/biom.12216

    Article  PubMed  PubMed Central  Google Scholar 

  32. Messé A, Hütt M-T, Hilgetag CC (2018) Toward a theory of coactivation patterns in excitable neural networks. PLoS Comput Biol 14:e1006084. https://doi.org/10.1371/journal.pcbi.1006084

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Kaiser RH, Whitfield-Gabrieli S, Dillon DG et al (2016) Dynamic resting-state functional connectivity in major depression. Neuropsychopharmacology 41:1822–1830. https://doi.org/10.1038/npp.2015.352

    Article  CAS  PubMed  Google Scholar 

  34. Kaiser RH, Kang MS, Lew Y et al (2019) Abnormal frontoinsular-default network dynamics in adolescent depression and rumination: a preliminary resting-state co-activation pattern analysis. Neuropsychopharmacology 44:1604–1612. https://doi.org/10.1038/s41386-019-0399-3

    Article  PubMed  PubMed Central  Google Scholar 

  35. Smith SM, Fox PT, Miller KL et al (2009) Correspondence of the brain’s functional architecture during activation and rest. Proc Natl Acad Sci U S A 106:13040–13045. https://doi.org/10.1073/pnas.0905267106

    Article  PubMed  PubMed Central  Google Scholar 

  36. Liu X, Chang C, Duyn JH (2013) Decomposition of spontaneous brain activity into distinct fMRI co-activation patterns. Front Syst Neurosci 7:101. https://doi.org/10.3389/fnsys.2013.00101

    Article  PubMed  PubMed Central  Google Scholar 

  37. Xiang T, Gong S (2008) Spectral clustering with eigenvector selection. Pattern Recognit 41:1012–1029. https://doi.org/10.1016/j.patcog.2007.07.023

    Article  Google Scholar 

  38. Lukasik S, Kowalski PA, Charytanowicz M, Kulczycki, Vancouver, 24–29 July 2016, pp. 2724–2728 PBT-2016 IC on EC (2016) Clustering using flower pollination algorithm and Calinski–Harabasz index

  39. Pang Y, Chen H, Wang Y et al (2018) Transdiagnostic and diagnosis-specific dynamic functional connectivity anchored in the right anterior insula in major depressive disorder and bipolar depression. Prog Neuropsychopharmacol Biol Psychiatry 85:7–15. https://doi.org/10.1016/j.pnpbp.2018.03.020

    Article  PubMed  Google Scholar 

  40. Tian S, Chattun MR, Zhang S et al (2019) Dynamic community structure in major depressive disorder: a resting-state MEG study. Prog Neuropsychopharmacol Biol Psychiatry 92:39–47. https://doi.org/10.1016/j.pnpbp.2018.12.006

    Article  PubMed  Google Scholar 

  41. 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 Psychiat 72:603–611. https://doi.org/10.1001/jamapsychiatry.2015.0071

    Article  Google Scholar 

  42. Bolton TAW, Wotruba D, Buechler R et al (2020) Triple network model dynamically revisited: lower salience network state switching in pre-psychosis. Front Physiol 11:66. https://doi.org/10.3389/fphys.2020.00066

    Article  PubMed  PubMed Central  Google Scholar 

  43. Kupis L, Romero C, Dirks B et al (2020) Evoked and intrinsic brain network dynamics in children with autism spectrum disorder. NeuroImage Clin 28:102396. https://doi.org/10.1016/j.nicl.2020.102396

    Article  PubMed  PubMed Central  Google Scholar 

  44. Kaiser RH, Whitfield-Gabrieli S, Dillon DG et al (2016) Dynamic resting-state functional connectivity in major depression. Neuropsychopharmacol Off Publ Am Coll Neuropsychopharmacol 41:1822–1830. https://doi.org/10.1038/npp.2015.352

    Article  CAS  Google Scholar 

  45. Broyd SJ, Demanuele C, Debener S et al (2009) Default-mode brain dysfunction in mental disorders: a systematic review. Neurosci Biobehav Rev 33:279–296. https://doi.org/10.1016/j.neubiorev.2008.09.002

    Article  PubMed  Google Scholar 

  46. Zamoscik V, Huffziger S, Ebner-Priemer U et al (2014) Increased involvement of the Parahippocampal gyri in a sad mood predicts future depressive symptoms. Soc Cogn Affect Neurosci 9:2034–2040. https://doi.org/10.1093/scan/nsu006

    Article  PubMed  PubMed Central  Google Scholar 

  47. Nolen-hoeksema S, Wisco BE, Lyubomirsky S (2008) Rethinking rumination. Perspect Psychol Sci 3:400–424

    Article  PubMed  Google Scholar 

  48. Goodman ZT, Bainter SA, Kornfeld S et al (2021) Whole-brain functional dynamics track depressive symptom severity. Cereb Cortex 31:4867–4876. https://doi.org/10.1093/cercor/bhab047

    Article  PubMed  PubMed Central  Google Scholar 

  49. Ho TC, Wu J, Shin D et al (2013) Altered cerebral perfusion in executive, affective, and motor networks during adolescent depression. J Am Acad Child Adolesc Psychiatry 52:1076-1091.e2

    Article  PubMed  Google Scholar 

  50. Chen H, Liu K, Zhang B et al (2019) More optimal but less regulated dorsal and ventral visual networks in patients with major depressive disorder. J Psychiatr Res 110:172–178

    Article  PubMed  Google Scholar 

  51. Ding YD, Yang R, Yan CG et al (2021) Disrupted hemispheric connectivity specialization in patients with major depressive disorder: evidence from the REST-meta-MDD Project. J Affect Disord 284:217–228

    Article  PubMed  Google Scholar 

  52. Cohen AD, Chang C, Wang Y (2021) Using multiband multi-echo imaging to improve the robustness and repeatability of co-activation pattern analysis for dynamic functional connectivity. Neuroimage 243:118555. https://doi.org/10.1016/j.neuroimage.2021.118555

    Article  PubMed  Google Scholar 

Download references

Funding

This work was supported by the National Natural Science Foundation of China (62088102, U21A20485, 61976248, 91648208, 61976175, 81371478, 61503411).

Author information

Authors and Affiliations

Authors

Contributions

All authors contributed to the study conception and design. Data collection was performed by the LL, LL, JL and KZ. Clinical data were assessed by JL. The preprocessing analyses were performed by KZ and JQ. The first draft of the manuscript was written by KZ and BL. All authors commented on previous versions of the manuscript. KZ, BC and DH reviewed and revised the manuscript. All authors approved the final manuscript as submitted.

Corresponding authors

Correspondence to Badong Chen, Dewen Hu or Lingjiang Li.

Ethics declarations

Conflict of interest

All authors declare that they have no competing interests.

Ethics approval and consent to participants

This study was approved by the Ethics Committee in Zhumadian Psychiatric Hospital and Xi-jing Hospital. All subjects gave informed consent before participating in this study.

Research involving human participants and/or animals

Human and human samples were used in the current study.

Consent for publication

Last version of the manuscript was approved by the authors before the submission.

Supplementary Information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zheng, K., Li, B., Lu, H. et al. Aberrant temporal–spatial complexity of intrinsic fluctuations in major depression. Eur Arch Psychiatry Clin Neurosci 273, 169–181 (2023). https://doi.org/10.1007/s00406-022-01403-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00406-022-01403-x

Keywords

Navigation