Human amygdala engagement moderated by early life stress exposure is a biobehavioral target for predicting recovery on antidepressants

Abstract

Amygdala circuitry and early life stress (ELS) are both strongly and independently implicated in the neurobiology of depression. Importantly, animal models have revealed that the contribution of ELS to the development and maintenance of depression is likely a consequence of structural and physiological changes in amygdala circuitry in response to stress hormones. Despite these mechanistic foundations, amygdala engagement and ELS have not been investigated as biobehavioral targets for predicting functional remission in translational human studies of depression. Addressing this question, we integrated human neuroimaging and measurement of ELS within a controlled trial of antidepressant outcomes. Here we demonstrate that the interaction between amygdala activation engaged by emotional stimuli and ELS predicts functional remission on antidepressants with a greater than 80% cross-validated accuracy. Our model suggests that in depressed people with high ELS, the likelihood of remission is highest with greater amygdala reactivity to socially rewarding stimuli, whereas for those with low-ELS exposure, remission is associated with lower amygdala reactivity to both rewarding and threat-related stimuli. This full model predicted functional remission over and above the contribution of demographics, symptom severity, ELS, and amygdala reactivity alone. These findings identify a human target for elucidating the mechanisms of antidepressant functional remission and offer a target for developing novel therapeutics. The results also offer a proof-of-concept for using neuroimaging as a target for guiding neuroscience-informed intervention decisions at the level of the individual person.

Trial registration: ClinicalTrials.gov NCT00693849.

Keywords: amygdala; antidepressant remission; early life stress; human brain imaging; predictive biomarkers.

Conflict of interest statement

L.M.W. received research funding from Brain Resource Pty Ltd. as an investigator at International Study to Predict Optimized Treatment in Depression sites. A.F.S. has speaking fees from Merck, GSK, and Roche; consultant fees from BrainCells, CeNeRx, CNS Response, Eli Lilly, Forest Labs, Genetech, Gilead, GSK, Jazz, Lundbeck, Merck, Neuronetics, Novadel, Novartis, Pathway Diagnostics, Pfizer, PharmaNeuroBoost, Quintiles, Sanofi-Aventis, Sunovion, Synosia, Takeda, Xytis, Wyeth; equity in Amnestix, BrainCells, CeNeRx, Corcept (co-founder), Delpor, Forest, Merck, Neurocrine, Novadel, Pfizer, PharaNeuroBoost, Somaxon, Synosis, and Titan; and patents on glucocorticoid antagonists and antidepressant response. T.S. has received research funding, from 2015 to the present, from the National Institute of Mental Health, Sunovion Pharmaceuticals, Inc., Elan Pharma International Limited, VA Cooperative Studies Program, Pathway Genomics, and Stanley Medical Research Institute. She has received consultant speaker fees from A/S Lundbeck, Sunovion, and Merck & Co; continuing medical education honoraria from Medscape Education, Global Medical Education, and CMEology; and royalties from Jones and Bartlett and UptoDate. C.B.N. has consultant fees from Xhale, Takeda, Mitsubishi Tanabe Pharma Development America, Taisho Pharmaceutical Inc., Lundbeck, Prismic Pharmaceuticals, Bracket (Clintara), Total Pain Solutions (TPS), and Gerson Lehrman Group (GLG) Healthcare & Biomedical Council. He has equity in Xhale, Celgene, Seattle Genetics, Abbvie, Titan Pharmaceuticals, OPKO Health, Inc., and Bracket Intermediate Holding Corp; a Scientific Advisory Board/Board of Directors fee from Skyland Trail, Bracket (Clintara), and RiverMend Health LLC; and patents for Methods and devices for transdermal delivery of lithium (US 6,375,990 BI) and Method to estimate antidepressant drug therapy by ex vivo assay (US 7,148,027B2). L.M.W. has consult fees from Humana and Advisor Board Fees from Psyberguide.

Figures

Fig. 1.

ROC curves showing the relative performance of candidate regression models in predicting functional remission. ROC curves model performance in predicting functional remission for 70 participants using the following predictors: (A) demographic/clinical measures; (B) demographic/covariate measures and ELS; (C) demographic/covariate measures and the interaction between amygdala reactivity to happy faces and ELS; (D) demographic/covariate measures and the interaction between amygdala reactivity to fearful faces and ELS; and (E) demographic/clinical covariates, the interaction between ELS and amygdala reactivity to happy faces, as well as the interaction between ELS and amygdala reactivity to threatening faces.

Fig. 2.

Binary classification of remission. Predicted likelihood of remission (log odds) at varying levels of ELS and amygdala reactivity to emotional faces. The likelihood of remission was generated from the model containing both the interaction between ELS and amygdala reactivity to happy faces and the interaction between ELS and amygdala reactivity to fearful faces, using all 70 participants. The likelihood of remission was calculated at mean levels of covariates at low (mean minus 1 SD), mid (mean), and high (mean plus 1 SD) levels of reactivity to happy faces, as well as low and high levels of reactivity to fearful faces. Individuals with predicted probabilities above the cross-validated threshold for classification (dotted line) would be classified as likely to respond to escitalopram, sertraline, or venlafaxine, whereas those below would not. Error bars show 90% confidence intervals.