Abstract
Owing to the rapid and robust clinical effects, electroconvulsive therapy (ECT) represents an optimal model to develop and test treatment predictors for major depressive disorders (MDDs), whereas imaging markers can be informative in identifying MDD patients who will respond to a specific antidepressant treatment or not. Here we aim to predict post-ECT depressive rating changes and remission status using pre-ECT gray matter (GM) in 38 MDD patients and validate in two independent data sets. Six GM regions including the right hippocampus/parahippocampus, right orbitofrontal gyrus, right inferior temporal gyrus (ITG), left postcentral gyrus/precuneus, left supplementary motor area, and left lingual gyrus were identified as predictors of ECT response, achieving accuracy of 89, 90 and 86% for remission prediction in three independent, age-matched data sets, respectively. For MDD patients, GM density increases only in the left supplementary motor cortex and left postcentral gyrus/precuneus after ECT. These results suggest that treatment-predictive and treatment-responsive regions may be anatomically different but functionally related in the context of ECT response. To the best of our knowledge, this is the first attempt to quantitatively identify and validate the ECT treatment biomarkers using multi-site GM data. We address a major clinical challenge and provide potential opportunities for more effective and timely interventions for electroconvulsive treatment.
Similar content being viewed by others
Log in or create a free account to read this content
Gain free access to this article, as well as selected content from this journal and more on nature.com
or
References
American Psychiatric Association (2012). In: Consensus Report of the APA Work Group on Neuroimaging Markers of Psychiatric Disorders. Association AP (ed): Arlington, VA, USA.
Argyelan M, Lencz T, Kaliora S, Sarpal DK, Weissman N, Kingsley PB et al (2016). Subgenual cingulate cortical activity predicts the efficacy of electroconvulsive therapy. Transl Psychiatry 6: e789.
Bora E, Fornito A, Pantelis C, Yucel M (2012). Gray matter abnormalities in major depressive disorder: a meta-analysis of voxel based morphometry studies. J Affect Disord 138: 9–18.
Bunea F, She Y, Ombao H, Gongvatana A, Devlin K, Cohen R (2011). Penalized least squares regression methods and applications to neuroimaging. Neuroimage 55: 1519–1527.
Calhoun VD, Sui J (2016). Multimodal fusion of brain imaging data: a key to finding the missing link(s) in complex mental illness. Biol Psychiatry Cogn Neurosci Neuroimaging 1: 230–244.
Costafreda SG, Chu C, Ashburner J, Fu CH (2009). Prognostic and diagnostic potential of the structural neuroanatomy of depression. PLoS ONE 4: e6353.
Dombrovski AY, Mulsant BH, Haskett RF, Prudic J, Begley AE, Sackeim HA (2005). Predictors of remission after electroconvulsive therapy in unipolar major depression. J Clin Psychiatry 66: 1043–1049.
Drysdale AT, Grosenick L, Downar J, Dunlop K, Mansouri F, Meng Y et al (2017). Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat Med 23: 28–38.
First MB, Spitzer RL, Gibbon M, Williams JBW (2002) Structured Clinical Interview for DSM-IV-TR Axis I Disorders, Research Version, Non-patient Edition. New York State Psychiatric Institute, Biomedical Research: New York.
Gabrieli JD, Ghosh SS, Whitfield-Gabrieli S (2015). Prediction as a humanitarian and pragmatic contribution from human cognitive neuroscience. Neuron 85: 11–26.
Gupta CN, Calhoun VD, Rachakonda S, Chen J, Patel V, Liu J et al (2015). Patterns of gray matter abnormalities in schizophrenia based on an international mega-analysis. Schizophr Bull 41: 1133–1142.
Haq AU, Sitzmann AF, Goldman ML, Maixner DF, Mickey BJ (2015). Response of depression to electroconvulsive therapy: a meta-analysis of clinical predictors. J Clin Psychiatry 76: 1374–1384.
Heijnen WT, Birkenhager TK, Wierdsma AI, van den Broek WW (2010). Antidepressant pharmacotherapy failure and response to subsequent electroconvulsive therapy: a meta-analysis. J Clin Psychopharmacol 30: 616–619.
Joshi SH, Espinoza RT, Pirnia T, Shi J, Wang Y, Ayers B et al (2016). Structural plasticity of the hippocampus and amygdala induced by electroconvulsive therapy in major depression. Biol Psychiatry 79: 282–292.
Kellner CH, Knapp RG, Petrides G, Rummans TA, Husain MM, Rasmussen K et al (2006). Continuation electroconvulsive therapy vs pharmacotherapy for relapse prevention in major depression: a multisite study from the Consortium for Research in Electroconvulsive Therapy (CORE). Arch Gen Psychiatry 63: 1337–1344.
Malberg JE, Eisch AJ, Nestler EJ, Duman RS (2000). Chronic antidepressant treatment increases neurogenesis in adult rat hippocampus. J Neurosci 20: 9104–9110.
McGrath PJ, Stewart JW, Fava M, Trivedi MH, Wisniewski SR, Nierenberg AA et al (2006). Tranylcypromine versus venlafaxine plus mirtazapine following three failed antidepressant medication trials for depression: a STAR*D report. Am J Psychiatry 163: 1531–1541 quiz 1666.
Meng X, Jiang R, Lin D, Bustillo J, Jones T, Chen J et al (2017). Predicting individualized clinical measures by a generalized prediction framework and multimodal fusion of MRI data. Neuroimage 145 (Pt B): 218–229.
Mrazek DA, Hornberger JC, Altar CA, Degtiar I (2014). A review of the clinical, economic, and societal burden of treatment-resistant depression: 1996-2013. Psychiatr Serv 65: 977–987.
Nordenskjold A, von Knorring L, Engstrom I (2012). Predictors of the short-term responder rate of electroconvulsive therapy in depressive disorders—a population based study. BMC Psychiatry 12: 115.
Oudega ML, van Exel E, Stek ML, Wattjes MP, van der Flier WM, Comijs HC et al (2014). The structure of the geriatric depressed brain and response to electroconvulsive therapy. Psychiatry Res 222: 1–9.
Randolph C, Tierney MC, Mohr E, Chase TN (1998). The Repeatable Battery for the Assessment of Neuropsychological Status (RBANS): preliminary clinical validity. J Clin Exp Neuropsychol 20: 310–319.
Redlich R, Opel N, Grotegerd D, Dohm K, Zaremba D, Burger C et al (2016). Prediction of individual response to electroconvulsive therapy via machine learning on structural magnetic resonance imaging data. JAMA Psychiatry 73: 557–564.
Sackeim HA, Dillingham EM, Prudic J, Cooper T, McCall WV, Rosenquist P et al (2009). Effect of concomitant pharmacotherapy on electroconvulsive therapy outcomes: short-term efficacy and adverse effects. Arch Gen Psychiatry 66: 729–737.
Shah SH, Arnett D, Houser SR, Ginsburg GS, MacRae C, Mital S et al (2016). Opportunities for the cardiovascular community in the precision medicine initiative. Circulation 133: 226–231.
Stokes ME, Visweswaran S (2012). Application of a spatially-weighted relief algorithm for ranking genetic predictors of disease. BioData Mining 5: 20.
Sui J, Huster R, Yu Q, Segall JM, Calhoun VD (2014). Function-structure associations of the brain: evidence from multimodal connectivity and covariance studies. Neuroimage 102 (Pt 1): 11–23.
Ten Doesschate F, van Eijndhoven P, Tendolkar I, van Wingen GA, van Waarde JA (2014). Pre-treatment amygdala volume predicts electroconvulsive therapy response. Front Psychiatry 5: 169.
Tripoliti EE, Fotiadis DI, Argyropoulou M, Manis G (2010). A six stage approach for the diagnosis of the Alzheimer’s disease based on fMRI data. J Biomed Inform 43: 307–320.
van Waarde JA, Scholte HS, van Oudheusden LJ, Verwey B, Denys D, van Wingen GA (2015). A functional MRI marker may predict the outcome of electroconvulsive therapy in severe and treatment-resistant depression. Mol Psychiatry 20: 609–614.
Wachinger C, Reuter M, Alzheimer’s Disease Neuroimaging I Australian Imaging B Lifestyle Flagship Study of Ageing (2016). Domain adaptation for Alzheimer’s disease diagnostics. Neuroimage 139: 470–479.
Wade BS, Joshi SH, Njau S, Leaver AM, Vasavada M, Woods RP et al (2016). Effect of electroconvulsive therapy on striatal morphometry in major depressive disorder. Neuropsychopharmacology 41: 2481–2491.
Weiner RD, Coffey CE, Fochtmann LJ, Greenberg RM, Isenberg KE, Kellner CH et al (2001) The Practice of Electroconvulsive Therapy: Recommendations for Treatment, Training, and Privileging, Second edn. American Psychiatric Association: Washington, DC.
Whelan R, Garavan H (2014). When optimism hurts: inflated predictions in psychiatric neuroimaging. Biol Psychiatry 75: 746–748.
WHO World Health Organization, Depression (2017). http://www.who.int/mental_health/management/depression/en/.
Wilkinson ST, Sanacora G, Bloch MH (2017). Hippocampal volume changes following electroconvulsive therapy: a systematic review and meta-analysis. Biol Psychiatry Clin Neurosci Neuroimaging 327–335.
Acknowledgements
This work was supported in part by the National High Tech Program (863, number 2015AA020513) and China National Natural Science Foundation (number 81471367), the Strategic Priority Research Program of the Chinese Academy of Sciences (grant number XDB02060005), Natural Science Foundation of Shanxi Province (grant number 2016021077), and National Institute of Health (1R01EB005846, 1R01MH094524, and P20GM103472).
Author information
Authors and Affiliations
Corresponding author
Additional information
Supplementary Information accompanies the paper on the Neuropsychopharmacology website
Supplementary information
Rights and permissions
About this article
Cite this article
Jiang, R., Abbott, C., Jiang, T. et al. SMRI Biomarkers Predict Electroconvulsive Treatment Outcomes: Accuracy with Independent Data Sets. Neuropsychopharmacol. 43, 1078–1087 (2018). https://doi.org/10.1038/npp.2017.165
Received:
Revised:
Accepted:
Published:
Issue date:
DOI: https://doi.org/10.1038/npp.2017.165
This article is cited by
-
Predicting treatment response in individuals with major depressive disorder using structural MRI-based similarity features
BMC Psychiatry (2025)
-
MiR-134-5p/BDNF/TrkB/CREB signaling pathway involved in the depression-like behaviors in mice following exposure to benzo[a]pyrene
Metabolic Brain Disease (2025)
-
Predictors of Response to Electroconvulsive Therapy in Major Depressive Disorder: A Review of Recent Research Findings
Current Behavioral Neuroscience Reports (2024)
-
Molecular mechanisms underlying structural plasticity of electroconvulsive therapy in major depressive disorder
Brain Imaging and Behavior (2024)
-
Functional connectivity changes between amygdala and prefrontal cortex after ECT are associated with improvement in distinct depressive symptoms
European Archives of Psychiatry and Clinical Neuroscience (2023)
