Trial reportBiological psychiatry. Cognitive neuroscience and neuroimaging2020
Machine Learning Identifies Large-Scale Reward-Related Activity Modulated by Dopaminergic Enhancement in Major Depression.
Trial report in Biological psychiatry. Cognitive neuroscience and neuroimaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.
- Machine learning approaches in the therapeutic outcome prediction in major depressive disorder: a systematic review.Frontiers in psychiatry · 2025Pooled it
- Investigating the effect of increased dopamine signaling on cerebral blood flow in Major Depressive Disorder: a double-blind randomized placebo-controlled study.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Trial
- Sustained effects of repeated levodopa (L-DOPA) administration on reward circuitry, effort-based motivation, and anhedonia in depressed patients with higher inflammation.Brain, behavior, and immunity · 2025Trial
- Anticipatory pleasure as a key hedonic component in classifying major depressive disorder: multidimensional behavioral evidence from majority vote algorithm.BMC psychiatry · 2026Article
- Brain-based graph-theoretical predictive modeling to map the trajectory of anhedonia, impulsivity, and hypomania from the human functional connectome.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024Article
- Article
- Methodological and Quality Flaws in the Use of Artificial Intelligence in Mental Health Research: Systematic Review.JMIR mental health · 2023Review
- Personalized Diagnosis and Treatment for Neuroimaging in Depressive Disorders.Journal of personalized medicine · 2022Review
- Toward a Better Understanding of the Mechanisms and Pathophysiology of Anhedonia: Are We Ready for Translation?The American journal of psychiatry · 2022Review
- Edible Mushrooms as a Potential Component of Dietary Interventions for Major Depressive Disorder.Foods (Basel, Switzerland) · 2022Review
- Influences of dopaminergic system dysfunction on late-life depression.Molecular psychiatry · 2022Review
- Artificial intelligence for brain diseases: A systematic review.APL bioengineering · 2020Review
- The ups and downs of relating nondrug reward activation to substance use risk in adolescents.Current addiction reports · 2020Article
Corrections and comments
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Authors and funding
11 authors at 4 institutions in 2 countries.
Funding
Abstract
backgroundTheoretical models have emphasized systems-level abnormalities in major depressive disorder (MDD). For unbiased yet rigorous evaluations of pathophysiological mechanisms underlying MDD, it is critically important to develop data-driven approaches that harness whole-brain data to classify MDD and evaluate possible normalizing effects of targeted interventions. Here, using an experimental therapeutics approach coupled with machine learning, we investigated the effect of a pharmacological challenge aiming to enhance dopaminergic signaling on whole-brain response to reward-related stimuli in MDD.
methodsUsing a double-blind, placebo-controlled design, we analyzed functional magnetic resonance imaging data from 31 unmedicated MDD participants receiving a single dose of 50 mg amisulpride (MDD
resultsHighly predictive classification models emerged that distinguished MDD
conclusionsResults indicate that in MDD, enhanced dopaminergic signaling restores abnormal activation and connectivity in a widespread network of regions. These findings provide new insights into the pathophysiology of MDD and pharmacological mechanism of antidepressants at the system level in addressing reward processing deficits among depressed individuals.
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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.