Caudothalamic dysfunction in drug-free suicidally depressed patients: an MEG study

Abstract

Major depressive disorder (MDD), characterized by low mood or anhedonia, is commonly associated with a greater suicidal susceptibility. There are numerous suicide-related findings pertaining to the dorsolateral prefrontal cortex (DLPFC), caudate nucleus and thalamus, which form a cortico-striato-thalamo-cortical (CSTC) circuit responsible for executive function and working memory. An aberrant CSTC circuitry is hypothesized to be implicated in depressed patients with a high suicidal risk. 27 MDD patients were assessed with the Nurses Global Assessment of Suicide Risk (NGASR), following which 14 patients were classified into a high suicide risk group (NGASR ≥ 12) and 13 patients were assigned to a low suicide risk group (NGASR < 6). All 27 patients were enrolled with 25 healthy controls for resting-state magnetoencephalography (MEG). Cross-frequency coupling (CFC) measured the phase of alpha-band (8–13 Hz) as it modulated to cortical gamma-band (30–48 Hz). There was a significantly lower alpha-to-gamma phase-amplitude coupling (PAC) between the right caudate and left thalamus in high-risk suicide group compared to both the low-risk suicide group and healthy controls. The presence of a weaker coupling between the right caudate and left thalamus is indicative of a caudothalamic abnormality in suicidally depressed patients. This implies that a disruption of CSTC loop could result in executive dysfunction and working memory impairment, leading to an increased suicidal risk in MDD patients. In the future, this preliminary study has the possibility of being replicated on a larger scale, and hence validates caudothalamic dysfunction as a reliable neuroimaging biomarker for suicide in depression.

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

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

References

  1. 1.

    Angst J et al (2013) Mortality of 403 patients with mood disorders 48 to 52 years after their psychiatric hospitalisation. Eur Arch Psychiatry Clin Neurosci 263(5):425–434

    PubMed  Google Scholar 

  2. 2.

    Aaltonen KI et al (2018) Decline in suicide mortality after psychiatric hospitalization for depression in Finland between 1991 and 2014. World Psychiatry 17(1):110–112

    PubMed  PubMed Central  Google Scholar 

  3. 3.

    World Health Organization (2017) Depression and other common mental disorders: global health estimates. http://apps.who.int/iris/bitstream/handle/10665/254610/WHOMSD?sequence=1

  4. 4.

    Dong M et al (2018) Prevalence of suicidal behaviors in patients with major depressive disorder in China: a comprehensive meta-analysis. J Affect Disord 225:32–39

    PubMed  Google Scholar 

  5. 5.

    Richard-Devantoy S, Berlim MT, Jollant F (2014) Suicidal behaviour and memory: a systematic review and meta-analysis. World J Biol Psychiatry 16(8):544–566

    PubMed  Google Scholar 

  6. 6.

    Mann JJ (2003) Neurobiology of suicidal behaviour. Nat Rev Neurosci 4:819

    CAS  PubMed  Google Scholar 

  7. 7.

    John Mann J et al (2016) Self-rated depression severity relative to clinician-rated depression severity: trait stability and potential role in familial transmission of suicidal behavior. Arch Suicide Res 20(3):412–425

    CAS  PubMed  Google Scholar 

  8. 8.

    Millner AJ, Lee MD, Hoyt K, Buckholtz JW, Auerbach RP, Nock MK (2018) Are suicide attempters more impulsive than suicide ideators? Gen Hosp Psychiatry. https://doi.org/10.1016/j.genhosppsych.2018.08.002

    Article  PubMed  Google Scholar 

  9. 9.

    Carballo JJ et al (2014) Aggressiveness across development and suicidal behavior in depressed patients. Arch Suicide Res 18(1):39–49

    PubMed  Google Scholar 

  10. 10.

    Olié E et al (2015) Processing of decision-making and social threat in patients with history of suicidal attempt: a neuroimaging replication study. Psychiatry Res Neuroimaging 234(3):369–377

    Google Scholar 

  11. 11.

    Keilp JG et al (2014) Neuropsychological deficits in past suicide attempters with varying levels of depression severity. Psychol Med 44(14):2965–2974

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Richard-Devantoy S, Berlim M, Jollant F (2014) A meta-analysis of neuropsychological markers of vulnerability to suicidal behavior in mood disorders. Psychol Med 44(8):1663–1673

    CAS  PubMed  Google Scholar 

  13. 13.

    O’Connor RC, Nock MK (2014) The psychology of suicidal behaviour. Lancet Psychiatry 1(1):73–85

    PubMed  Google Scholar 

  14. 14.

    Bani-Fatemi A et al (2018) Structural and functional alterations of the suicidal brain: an updated review of neuroimaging studies. Psychiatry Res Neuroimaging 278:77–91

    PubMed  Google Scholar 

  15. 15.

    Lippard ETC, Johnston JA, Blumberg HP (2014) Neurobiological risk factors for suicide: insights from brain imaging. Am J Prev Med 47(3):S152–S162

    Google Scholar 

  16. 16.

    Sturm VE, Haase CM, Levenson RW, Emotional dysfunction in psychopathology and neuropathology: neural and genetic pathways, in genomics, circuits, and pathways in clinical neuropsychiatry. 2016, Elsevier.Amsterdam p. 345–364

    Google Scholar 

  17. 17.

    Wagner G et al (2012) Prefrontal cortical thickness in depressed patients with high-risk for suicidal behavior. J Psychiatr Res 46(11):1449–1455

    PubMed  Google Scholar 

  18. 18.

    Pu S et al (2015) Suicidal ideation is associated with reduced prefrontal activation during a verbal fluency task in patients with major depressive disorder. J Affect Disord 181:9–17

    PubMed  Google Scholar 

  19. 19.

    Sublette ME et al (2013) Regional brain glucose uptake distinguishes suicide attempters from non-attempters in major depression. Arch Suicide Res 17(4):434–447

    PubMed  Google Scholar 

  20. 20.

    Austin M et al (2002) Localized decrease in serotonin transporter-immunoreactive axons in the prefrontal cortex of depressed subjects committing suicide. Neuroscience 114(3):807–815

    CAS  PubMed  Google Scholar 

  21. 21.

    Zhao J et al (2016) Prefrontal changes in the glutamate-glutamine cycle and neuronal/glial glutamate transporters in depression with and without suicide. J Psychiatr Res 82:8–15

    CAS  PubMed  Google Scholar 

  22. 22.

    Hungund B et al (2004) Upregulation of CB1 receptors and agonist-stimulated [35S] GTPγS binding in the prefrontal cortex of depressed suicide victims. Mol Psychiatry 9(2):184–190

    CAS  PubMed  Google Scholar 

  23. 23.

    Gray A et al (2015) Sex differences in glutamate receptor gene expression in major depression and suicide. Mol Psychiatry 20(9):1057–1068

    CAS  Google Scholar 

  24. 24.

    Pandey GN et al (2014) Toll-like receptors in the depressed and suicide brain. J Psychiatr Res 53:62–68

    PubMed  PubMed Central  Google Scholar 

  25. 25.

    Cox Lippard ET, Johnston JAY, Blumberg HP (2014) Neurobiological risk factors for suicide: insights from brain imaging. Am J Prev Med 47(3 Suppl 2):S152–S162

    PubMed  PubMed Central  Google Scholar 

  26. 26.

    Vang FJ et al (2010) Size of basal ganglia in suicide attempters, and its association with temperament and serotonin transporter density. Psychiatry Res 183(2):177–179

    PubMed  Google Scholar 

  27. 27.

    Wagner G et al (2011) Structural brain alterations in patients with major depressive disorder and high risk for suicide: evidence for a distinct neurobiological entity? Neuroimage 54(2):1607–1614

    PubMed  Google Scholar 

  28. 28.

    Pan L et al (2013) Preserved hippocampal function during learning in the context of risk in adolescent suicide attempt. Psychiatry Res Neuroimaging 211(2):112–118

    Google Scholar 

  29. 29.

    Giakoumatos CI et al., Are structural brain abnormalities associated with suicidal behavior in patients with psychotic disorders? J Psychiatr Res 2013. 47(10): p. 1389–1395

    PubMed  PubMed Central  Google Scholar 

  30. 30.

    Torres-Platas SG et al (2016) Glial fibrillary acidic protein is differentially expressed across cortical and subcortical regions in healthy brains and downregulated in the thalamus and caudate nucleus of depressed suicides. Mol Psychiatry 21(4):509–515

    CAS  PubMed  Google Scholar 

  31. 31.

    Willeumier K, Taylor DV, Amen DG (2011) Decreased cerebral blood flow in the limbic and prefrontal cortex using SPECT imaging in a cohort of completed suicides. Transl Psychiatry 1:e28

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Jollant F et al (2018) Neuroimaging-informed phenotypes of suicidal behavior: a family history of suicide and the use of a violent suicidal means. Transl Psychiatry 8(1):120

    PubMed  PubMed Central  Google Scholar 

  33. 33.

    Leckman JF, Riddle MA (2000) Tourette’s syndrome: when habit-forming systems form habits of their own? Neuron 28(2):349–354

    CAS  PubMed  Google Scholar 

  34. 34.

    Tau GZ, Peterson BS (2009) Normal development of brain circuits. Neuropsychopharmacology 35:147

    PubMed Central  Google Scholar 

  35. 35.

    Fernández de la Cruz, L et al (2016) Suicide in obsessive–compulsive disorder: a population-based study of 36 788 Swedish patients. Mol Psychiatry 22:1626

    Google Scholar 

  36. 36.

    Posner J, Park C, Wang Z (2014) Connecting the dots: a review of resting connectivity MRI studies in attention-deficit/hyperactivity disorder. Neuropsychol Rev 24(1):3–15

    PubMed  PubMed Central  Google Scholar 

  37. 37.

    Polyanska L, Critchley HD, Rae CL, Centrality of prefrontal and motor preparation cortices to Tourette syndrome revealed by meta-analysis of task-based neuroimaging studies. NeuroImage Clin (2017) 16: p. 257–267

    PubMed  PubMed Central  Google Scholar 

  38. 38.

    Bredemeier K, Miller IW (2015) Executive function and suicidality: a systematic qualitative review. Clin Psychol Rev 40:170–183

    PubMed  PubMed Central  Google Scholar 

  39. 39.

    Keilp JG et al (2012) Neuropsychological function and suicidal behavior: attention control, memory and executive dysfunction in suicide attempt. Psychol Med 43(03):539–551

    PubMed  PubMed Central  Google Scholar 

  40. 40.

    Zheng C, Zhang T (2013) Alteration of phase-phase coupling between theta and gamma rhythms in a depression-model of rats. Cogn Neurodyn 7(2):167–172

    PubMed  Google Scholar 

  41. 41.

    Canolty RT et al (2006) High gamma power is phase-locked to theta oscillations in human neocortex. Science 313(5793):1626–1628

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Hyafil A et al (2015) Neural cross-frequency coupling: connecting architectures, mechanisms, and functions. Trends Neurosci 38(11):725–740

    CAS  PubMed  Google Scholar 

  43. 43.

    Jiang H et al (2015) Measuring directionality between neuronal oscillations of different frequencies. Neuroimage 118:359–367

    PubMed  Google Scholar 

  44. 44.

    Bonnefond M, Jensen O (2015) Gamma activity coupled to alpha phase as a mechanism for top-down controlled gating. PloS One 10(6):e0128667

    PubMed  PubMed Central  Google Scholar 

  45. 45.

    Osipova D, Hermes D, Jensen O (2008) Gamma power is phase-locked to posterior alpha activity. PloS One 3(12):e3990

    PubMed  PubMed Central  Google Scholar 

  46. 46.

    Proudfoot M et al (2014) Magnetoencephalography Pract Neurol 14(5):336–343

    PubMed  PubMed Central  Google Scholar 

  47. 47.

    Fernandez A et al (2018) Complexity analysis of spontaneous brain activity in mood disorders: a magnetoencephalography study of bipolar disorder and major depression. Compr Psychiatry 84:112–117

    PubMed  Google Scholar 

  48. 48.

    Alamian G et al (2017) Alterations of intrinsic brain connectivity patterns in depression and bipolar disorders: a critical assessment of magnetoencephalography-based evidence. Front Psychiatry 8:41

    PubMed  PubMed Central  Google Scholar 

  49. 49.

    Baillet S (2017) Magnetoencephalography for brain electrophysiology and imaging. Nature Neurosci 20(3):327

    CAS  PubMed  Google Scholar 

  50. 50.

    Lema YY et al (2018) Trait and state biomarkers for psychiatric disorders: Importance of infrastructure to bridge the gap between basic and clinical research and industry. Psychiatry Clin Neurosci 72(7):482–489

    PubMed  Google Scholar 

  51. 51.

    Hamilton M (1960) A rating scale for depression. J Neurol Neurosurg Psychiatry 23(1):56–62

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Lecrubier Y et al (1997) The mini international neuropsychiatric interview (MINI). A short diagnostic structured interview: reliability and validity according to the CIDI. Eur Psychiatry 12(5):224–231

    Google Scholar 

  53. 53.

    Edition F, Association AP, Diagnostic and statistical manual of mental disorders (1994) Washington, American Psychological Association

    Google Scholar 

  54. 54.

    Sartorius N et al (1993) Progress toward achieving a common language in psychiatry: results from the field trial of the clinical guidelines accompanying the WHO classification of mental and behavioral disorders in ICD-10. Arch Gen Psychiatry 50(2):115–124

    CAS  PubMed  Google Scholar 

  55. 55.

    Cutcliffe JR, Barker P (2004) The nurses’ global assessment of suicide risk (NGASR): developing a tool for clinical practice. J Psychiatr Ment Health Nurs 11(4):393–400

    CAS  PubMed  Google Scholar 

  56. 56.

    World Medical A (2013) World medical association declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA 310(20):2191–2194

    Google Scholar 

  57. 57.

    Van Veen BD et al (1997) Localization of brain electrical activity via linearly constrained minimum variance spatial filtering. IEEE Trans Biomed Eng 44(9):867–880

    PubMed  Google Scholar 

  58. 58.

    Wei W, Wang X-J (2016) Inhibitory control in the cortico-basal ganglia-thalamocortical circuit: complex modulation and its interplay with working memory and decision-making. Neuron 92(5):1093

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Cummings JL (1993) Frontal-subcortical circuits and human behavior. Arch Neurol 50(8):873–880

    CAS  PubMed  Google Scholar 

  60. 60.

    Hanlon C, Dowdle L, Jones J (2016) Chapter six-biomarkers for success: using neuroimaging to predict relapse and develop brain stimulation treatments for cocaine-dependent individuals. Int Rev Neurobiol 129:125–156

    CAS  PubMed  PubMed Central  Google Scholar 

  61. 61.

    Jia Z et al (2014) Impaired frontothalamic circuitry in suicidal patients with depression revealed by diffusion tensor imaging at 3.0 T. J Psychiatry Neurosci 39(3):170

    PubMed  PubMed Central  Google Scholar 

  62. 62.

    Myung W et al (2016) Reduced frontal-subcortical white matter connectivity in association with suicidal ideation in major depressive disorder. Transl Psychiatry 6(6):e835

    CAS  PubMed  PubMed Central  Google Scholar 

  63. 63.

    Marchand WR et al (2012) Striatal and cortical midline circuits in major depression: implications for suicide and symptom expression. Prog Neuropsychopharmacol Biol Psychiatry 36(2):290–299

    PubMed  Google Scholar 

  64. 64.

    Baillet S (2017) Magnetoencephalography for brain electrophysiology and imaging. Nat Neurosci 20(3):327–339

    CAS  PubMed  Google Scholar 

  65. 65.

    Gross J et al (2013) Good practice for conducting and reporting MEG research. Neuroimage 65:349–363

    PubMed  PubMed Central  Google Scholar 

  66. 66.

    Lin F-H et al (2008) Linear constraint minimum variance beamformer functional magnetic resonance inverse imaging. Neuroimage 43(2):297–311

    PubMed  PubMed Central  Google Scholar 

  67. 67.

    Nugent AC et al (2015) Group differences in MEG-ICA derived resting state networks: application to major depressive disorder. Neuroimage 118:1–12

    PubMed  PubMed Central  Google Scholar 

  68. 68.

    Hillebrand A et al (2016) Detecting epileptiform activity from deeper brain regions in spatially filtered MEG data. Clin Neurophysiol 127(8):2766–2769

    CAS  PubMed  Google Scholar 

  69. 69.

    Just MA et al (2017) Machine learning of neural representations of suicide and emotion concepts identifies suicidal youth. Nat Hum Behav 1(12):911

    PubMed  PubMed Central  Google Scholar 

  70. 70.

    Yahata N, Kasai K, Kawato M (2017) Computational neuroscience approach to biomarkers and treatments for mental disorders. Psychiatry Clin Neurosci 71(4):215–237

    PubMed  Google Scholar 

Download references

Acknowledgements

We would like to express our sincere gratitude to the Department of Psychiatry and the Department of Radiology at Nanjing Brain Hospital for helping patients during the neuroimaging procedures. We are also thankful to our healthy controls, patients and patient’s family for their generous support, cooperation and participation.

Funding

This work was supported by the National Natural Science Foundation of China (81571639, 61372032, and 81871066); Jiangsu Provincial Medical Innovation Team of the Project of Invigorating Health Care through Science, Technology and Education (CXTDC2016004) and Jiangsu Provincial Key Research and Development Program (BE2018609).

Author information

Affiliations

Authors

Corresponding authors

Correspondence to Qing Lu or Zhijian Yao.

Ethics declarations

Conflict of interest

The authors have no conflict of interest to declare.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 53 KB)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Chattun, M.R., Zhang, S., Chen, Y. et al. Caudothalamic dysfunction in drug-free suicidally depressed patients: an MEG study. Eur Arch Psychiatry Clin Neurosci 270, 217–227 (2020). https://doi.org/10.1007/s00406-018-0968-1

Download citation

Keywords

  • Cortico-striato-thalamo-cortical (CSTC) circuit
  • Cross-frequency coupling (CFC)
  • Magnetoencephalography (MEG)
  • Major depressive disorder (MDD)
  • Nurses global assessment of suicide risk (NGASR)