SMRI Biomarkers Predict Electroconvulsive Treatment Outcomes: Accuracy with Independent Data Sets

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.

[1]  R. Whelan,et al.  When Optimism Hurts: Inflated Predictions in Psychiatric Neuroimaging , 2014, Biological Psychiatry.

[2]  Satrajit S. Ghosh,et al.  Prediction as a Humanitarian and Pragmatic Contribution from Human Cognitive Neuroscience , 2015, Neuron.

[3]  H. Sackeim,et al.  Predictors of remission after electroconvulsive therapy in unipolar major depression. , 2005, The Journal of clinical psychiatry.

[4]  Andrew T. Drysdale,et al.  Resting-state connectivity biomarkers define neurophysiological subtypes of depression , 2016, Nature Medicine.

[5]  T. Birkenhäger,et al.  Antidepressant Pharmacotherapy Failure and Response to Subsequent Electroconvulsive Therapy: A Meta-Analysis , 2010, Journal of clinical psychopharmacology.

[6]  V. Arolt,et al.  Prediction of Individual Response to Electroconvulsive Therapy via Machine Learning on Structural Magnetic Resonance Imaging Data. , 2016, JAMA psychiatry.

[7]  T. Lencz,et al.  Subgenual cingulate cortical activity predicts the efficacy of electroconvulsive therapy , 2016, Translational Psychiatry.

[8]  B. Mickey,et al.  Response of depression to electroconvulsive therapy: a meta-analysis of clinical predictors. , 2015, The Journal of clinical psychiatry.

[9]  Shyam Visweswaran,et al.  Application of a spatially-weighted Relief algorithm for ranking genetic predictors of disease , 2012, BioData Mining.

[10]  V. Calhoun,et al.  Multimodal fusion of brain imaging data: A key to finding the missing link(s) in complex mental illness. , 2016, Biological psychiatry. Cognitive neuroscience and neuroimaging.

[11]  Shantanu H. Joshi,et al.  Structural Plasticity of the Hippocampus and Amygdala Induced by Electroconvulsive Therapy in Major Depression , 2016, Biological Psychiatry.

[12]  M. Fink.  The Practice of Electroconvulsive Therapy: Recommendations for Treatment, Training, and Privileging, second edition , 2002 .

[13]  C. Altar,et al.  A review of the clinical, economic, and societal burden of treatment-resistant depression: 1996-2013. , 2014, Psychiatric services.

[14]  H. Scholte,et al.  A functional MRI marker may predict the outcome of electroconvulsive therapy in severe and treatment-resistant depression , 2014, Molecular Psychiatry.

[15]  Joseph Loscalzo,et al.  Opportunities for the Cardiovascular Community in the Precision Medicine Initiative , 2016, Circulation.

[16]  M. Bloch,et al.  Hippocampal volume changes following electroconvulsive therapy: a systematic review and meta-analysis. , 2017, Biological psychiatry. Cognitive neuroscience and neuroimaging.

[17]  E. Bora,et al.  Gray matter abnormalities in Major Depressive Disorder: a meta-analysis of voxel based morphometry studies. , 2012, Journal of affective disorders.

[18]  Hernando Ombao,et al.  Penalized least squares regression methods and applications to neuroimaging , 2011, NeuroImage.

[19]  Jiayu Chen,et al.  Patterns of Gray Matter Abnormalities in Schizophrenia Based on an International Mega-analysis. , 2015, Schizophrenia bulletin.

[20]  Eric J. Nestler,et al.  Chronic Antidepressant Treatment Increases Neurogenesis in Adult Rat Hippocampus , 2000, The Journal of Neuroscience.

[21]  Shantanu H. Joshi,et al.  Methylphenidate modifies the motion of the circadian clock Lamotrigine in mood disorders and cocaine dependence Cortical glutamate in postpartum depression Effect of Electroconvulsive Therapy on Striatal Morphometry in Major Depressive Disorder , 2016 .

[22]  Richard Abrams,et al.  The Practice of Electroconvulsive Therapy: Recommendations for Treatment, Training, and Privileging (2nd ed.). , 1992 .

[23]  Indira Tendolkar,et al.  Pre-Treatment Amygdala Volume Predicts Electroconvulsive Therapy Response , 2014, Front. Psychiatry.

[24]  Vince D. Calhoun,et al.  Predicting individualized clinical measures by a generalized prediction framework and multimodal fusion of MRI data , 2017, NeuroImage.

[25]  M. Fava,et al.  Tranylcypromine versus venlafaxine plus mirtazapine following three failed antidepressant medication trials for depression: a STAR*D report. , 2006, The American journal of psychiatry.

[26]  Dimitrios I. Fotiadis,et al.  A six stage approach for the diagnosis of the Alzheimer's disease based on fMRI data , 2010, J. Biomed. Informatics.

[27]  C. Randolph,et al.  The Repeatable Battery for the Assessment of Neuropsychological Status (RBANS): preliminary clinical validity. , 1998, Journal of clinical and experimental neuropsychology.

[28]  M. Biggs,et al.  Continuation electroconvulsive therapy vs pharmacotherapy for relapse prevention in major depression: a multisite study from the Consortium for Research in Electroconvulsive Therapy (CORE). , 2006, Archives of general psychiatry.

[29]  Vince D. Calhoun,et al.  Function–structure associations of the brain: Evidence from multimodal connectivity and covariance studies , 2014, NeuroImage.

[30]  L. von Knorring,et al.  Predictors of the short-term responder rate of Electroconvulsive therapy in depressive disorders - a population based study , 2012, BMC Psychiatry.

[31]  Elaine M. Dillingham,et al.  Effect of concomitant pharmacotherapy on electroconvulsive therapy outcomes: short-term efficacy and adverse effects. , 2009, Archives of general psychiatry.

[32]  Christian Wachinger,et al.  Domain adaptation for Alzheimer's disease diagnostics , 2016, NeuroImage.

[33]  J. Ashburner,et al.  Prognostic and Diagnostic Potential of the Structural Neuroanatomy of Depression , 2009, PloS one.

[34]  P. Scheltens,et al.  The structure of the geriatric depressed brain and response to electroconvulsive therapy , 2014, Psychiatry Research: Neuroimaging.