Original Article | Published:

A functional MRI marker may predict the outcome of electroconvulsive therapy in severe and treatment-resistant depression

Molecular Psychiatry volume 20, pages 609614 (2015) | Download Citation

Abstract

Electroconvulsive therapy (ECT) is effective even in treatment-resistant patients with major depression. Currently, there are no markers available that can assist in identifying those patients most likely to benefit from ECT. In the present study, we investigated whether resting-state network connectivity can predict treatment outcome for individual patients. We included forty-five patients with severe and treatment-resistant unipolar depression and collected functional magnetic resonance imaging scans before the course of ECT. We extracted resting-state networks and used multivariate pattern analysis to discover networks that predicted recovery from depression. Cross-validation revealed two resting-state networks with significant classification accuracy after correction for multiple comparisons. A network centered in the dorsomedial prefrontal cortex (including the dorsolateral prefrontal cortex, orbitofrontal cortex and posterior cingulate cortex) showed a sensitivity of 84% and specificity of 85%. Another network centered in the anterior cingulate cortex (including the dorsolateral prefrontal cortex, sensorimotor cortex, parahippocampal gyrus and midbrain) showed a sensitivity of 80% and a specificity of 75%. These preliminary results demonstrate that resting-state networks may predict treatment outcome for individual patients and suggest that resting-state networks have the potential to serve as prognostic neuroimaging biomarkers to guide personalized treatment decisions.

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References

  1. 1.

    . Electroconvulsive therapy for depression. N Engl J Med 2007; 357: 1939–1945.

  2. 2.

    , , , , , et al. The State of US Health, 1990–2010: burden of diseases, injuries, and risk factors. JAMA 2013; 310: 591–608.

  3. 3.

    , , , , , et al. Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: a STAR*D report. Am J Psychiatry 2006; 163: 1905–1917.

  4. 4.

    , , , , . Effectiveness of electroconvulsive therapy in community settings. Biol Psychiatry 2004; 55: 301–312.

  5. 5.

    . Electroconvulsive therapy in the spotlight. N Engl J Med 2011; 364: 1785–1787.

  6. 6.

    , , , , , . The cognitive effects of electroconvulsive therapy in community settings. Neuropsychopharmacology 2007; 32: 244–254.

  7. 7.

    , , , . Antidepressant pharmacotherapy failure and response to subsequent electroconvulsive therapy: a meta-analysis. J Clin Psychopharmacol 2010; 30: 616–619.

  8. 8.

    , , , , , et al. Declining use of electroconvulsive therapy in United States general hospitals. Biol Psychiatry 2013; 73: 119–126.

  9. 9.

    , , , , , et al. ECT remission rates in psychotic versus nonpsychotic depressed patients: a report from CORE. J ECT 2001; 17: 244–253.

  10. 10.

    , , , , . Predictors of response to ultrabrief right unilateral electroconvulsive therapy. J Affect Disord 2011; 130: 192–197.

  11. 11.

    , , , , . Appropriateness for electroconvulsive therapy (ECT) can be assessed on a three-item scale. Med Hypotheses 2012; 79: 204–206.

  12. 12.

    American Psychiatric Association. Consensus report of the APA Work Group on Neuroimaging Markers of Psychiatric Disorders. American Psychiatric Association: Arlington, VA, USA, 2012.

  13. 13.

    , , , . Disease state prediction from resting state functional connectivity. Magn Reson Med 2009; 62: 1619–1628.

  14. 14.

    , , , , , et al. Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis. Brain 2012; 135: 1498–1507.

  15. 15.

    , , , , , et al. Pattern classification of sad facial processing: toward the development of neurobiological markers in depression. Biol Psychiatry 2008; 63: 656–662.

  16. 16.

    , , , . Neural correlates of sad faces predict clinical remission to cognitive behavioural therapy in depression. NeuroReport 2009; 20: 637–641.

  17. 17.

    , , , , , . Patient, treatment, and anatomical predictors of outcome in electroconvulsive therapy: a prospective study. J ECT 2013; 29: 113–121.

  18. 18.

    , , , , , et al. MRI characteristics predicting seizure threshold in patients undergoing electroconvulsive therapy: a prospective study. Brain Stimul 2013; 6: 607–614.

  19. 19.

    , . A new depression scale designed to be sensitive to change. Br J Psychiatry 1979; 134: 382–389.

  20. 20.

    , , . A review of studies of the Hamilton depression rating scale in healthy controls: implications for the definition of remission in treatment studies of depression. J Nerv Ment Dis 2004; 192: 595–601.

  21. 21.

    , , , . Investigations into resting- state connectivity using independent component analysis. Philos Trans R Soc Lond Ser B 2005; 360: 1001–1013.

  22. 22.

    , . Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. NeuroImage 2009; 44: 83–98.

  23. 23.

    , , . Predictive neural biomarkers of clinical response in depression: a meta-analysis of functional and structural neuroimaging studies of pharmacological and psychological therapies. Neurobiol Dis 2013; 52: 75–83.

  24. 24.

    , , , . Resting-state functional MRI in depression unmasks increased connectivity between networks via the dorsal nexus. Proc Natl Acad Sci USA 2010; 107: 11020–11025.

  25. 25.

    , , , , , et al. Microstructural white matter abnormalities and remission of geriatric depression. Am J Psychiatry 2008; 165: 238–244.

  26. 26.

    , , , , , . Metabolic correlates of antidepressant and antipsychotic response in patients with psychotic depression undergoing electroconvulsive therapy. J ECT 2007; 23: 265–273.

  27. 27.

    , , , , , et al. Electroconvulsive therapy response in major depressive disorder: a pilot functional network connectivity resting state FMRI investigation. Front Psychiatry 2013; 4: 10.

  28. 28.

    , , , , , , . Electroconvulsive therapy reduces frontal cortical connectivity in severe depressive disorder. Proc Natl Acad Sci USA 2012; 109: 5464–5468.

  29. 29.

    , , , , , et al. Electroconvulsive therapy-induced brain plasticity determines therapeutic outcome in mood disorders. Proc Natl Acad Sci USA 2014; 111: 1156–1161.

  30. 30.

    , , , . Prognostic and diagnostic potential of the structural neuroanatomy of depression. PLoS One 2009; 4: e6353.

  31. 31.

    , , , , , et al. Prognostic prediction of therapeutic response in depression using high-field MR imaging. NeuroImage 2011; 55: 1497–1503.

  32. 32.

    . Resting-state functional connectivity in neuropsychiatric disorders. Curr Opin Neurol 2008; 21: 424–430.

  33. 33.

    . Electroconvulsive Therapy. 4th edn Oxford University Press: New York, NY, USA, 2002.

  34. 34.

    , , , , , et al. Decreased regional brain metabolism after ECT. Am J Psychiatry 2001; 158: 305–308.

  35. 35.

    , , , , , . ECT in treatment-resistant depression. Am J Psychiatry 2012; 169: 1238–1244.

  36. 36.

    . How does electroconvulsive therapy work? Theories on its mechanism. Can J Psychiatry 2011; 56: 13–18.

  37. 37.

    , . Antidepressant medications reduce subcortical–cortical resting-state functional connectivity in healthy volunteers. NeuroImage 2011; 57: 1317–1323.

  38. 38.

    , , , , , . Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization. J Neurophysiol 2010; 103: 297–321.

  39. 39.

    , , , , , et al. Toward discovery science of human brain function. Proc Natl Acad Sci USA 2010; 107: 4734–4739.

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Acknowledgements

We thank all the staff at the Department of Radiology in the Rijnstate Hospital, especially Bart AR Tonino, MD (radiologist), Marc van Driel (head of the MRI section) and Mrs Gonda Niehuis (quality manager) for their technical assistance, and all the staff of the Department of Psychiatry in the Rijnstate Hospital, especially Oscar Büno Heslinga (ECT nurse) for his excellent help in collecting the clinical data.

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Affiliations

  1. Department of Psychiatry, Rijnstate Hospital, Arnhem, The Netherlands

    • J A van Waarde
    • , L J B van Oudheusden
    •  & B Verwey
  2. Cognitive Neuroscience Group, University of Amsterdam, Amsterdam, The Netherlands

    • H S Scholte
  3. Department of Psychiatry, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands

    • D Denys
    •  & G A van Wingen

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Competing interests

The authors declare no conflicts of interest.

Corresponding authors

Correspondence to J A van Waarde or G A van Wingen.

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DOI

https://doi.org/10.1038/mp.2014.78