Trial reportBiological psychiatry2022
Patterns of Pretreatment Reward Task Brain Activation Predict Individual Antidepressant Response: Key Results From the EMBARC Randomized Clinical Trial.
Trial report in Biological psychiatry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Machine Learning for Comparative Antidepressant Selection in Major Depressive Disorder: Systematic Review.JMIR mental health · 2026Pooled it
- Machine learning in the prediction of treatment response for emotional disorders: A systematic review and meta-analysis.Clinical psychology review · 2025Pooled it
- Artificial intelligence in mental health care: a systematic review of diagnosis, monitoring, and intervention applications.Psychological medicine · 2025Pooled it
- On the value of radiomics in addition to clinical measures in emotional conflict fMRI for predicting sertraline response in major depressive disorder.Scientific reports · 2026Trial
- Neuropathophysiological changes associated with mirtazapine treatment response in major depressive disorder: insights into bottom-up dopaminergic pathways and prefrontal control networks.European archives of psychiatry and clinical neuroscience · 2026Article
- Development of a baseline battery to optimize depression treatment assignment in primary care: Balancing breadth and brevity.Journal of affective disorders · 2026Article
- Developing Clinically Interpretable Neuroimaging Biotypes in Psychiatry.Biological psychiatry · 2026Review
- Multimodal AI-based systems in major depressive disorder: a review of clinical and translational applications.Frontiers in digital health · 2026Review
- Predicting antidepressant response via local-global graph neural network and neuroimaging biomarkers.NPJ digital medicine · 2025Article
- Antidepressant Treatment Response Prediction With Early Assessment of Functional Near-Infrared Spectroscopy and Micro-RNA.IEEE journal of translational engineering in health and medicine · 2025Article
- Personalized brain circuit scores identify clinically distinct biotypes in depression and anxiety.Nature medicine · 2024Article
- Candidate biomarkers in psychiatric disorders: state of the field.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2023Article
- New and emerging approaches to treat psychiatric disorders.Nature medicine · 2023Review
- Drug Development for New Psychiatric Drug Therapies.Advances in neurobiology · 2023Article
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Authors and funding
13 authors.
Funding
Abstract
backgroundThe lack of biomarkers to inform antidepressant selection is a key challenge in personalized depression treatment. This work identifies candidate biomarkers by building deep learning predictors of individual treatment outcomes using reward processing measures from functional magnetic resonance imaging, clinical assessments, and demographics.
methodsParticipants in the EMBARC (Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care) study (n = 222) underwent reward processing task-based functional magnetic resonance imaging at baseline and were randomized to 8 weeks of sertraline (n = 106) or placebo (n = 116). Subsequently, sertraline nonresponders (n = 37) switched to 8 weeks of bupropion. The change in Hamilton Depression Rating Scale was measured after treatment. Reward processing, clinical measurements, and demographics were used to train treatment-specific deep learning models.
resultsThe predictive model for sertraline achieved R
conclusionsThese findings demonstrate the utility of reward processing measurements and deep learning to predict antidepressant outcomes and to form multimodal treatment biomarkers.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.