Evidence map›Paper›PMID 40498519›Full record

SynthesisJAMA psychiatry2025

Differential Associations of Dopamine and Serotonin With Reward and Punishment Processes in Humans: A Systematic Review and Meta-Analysis.

Anahit Mkrtchian, Zeguo Qiu, Yaniv Abir, Tore Erdmann, Quentin Dercon, Terezie Sedlinska, Michael Browning, Harry Costello, Quentin J M Huys

Abstract readSystematic ReviewMeta-Analysis
In one paragraph

Synthesis in JAMA psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Observational
  5. Article
  6. Review
  7. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Anahit MkrtchianApplied Computational Psychiatry Lab, Mental Health Neuroscience Department, Division of Psychiatry and Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Queen Square Institute of Neurology, University College London, London, United Kingdom.
Zeguo QiuApplied Computational Psychiatry Lab, Mental Health Neuroscience Department, Division of Psychiatry and Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Queen Square Institute of Neurology, University College London, London, United Kingdom.
Yaniv AbirApplied Computational Psychiatry Lab, Mental Health Neuroscience Department, Division of Psychiatry and Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Queen Square Institute of Neurology, University College London, London, United Kingdom.
Tore ErdmannApplied Computational Psychiatry Lab, Mental Health Neuroscience Department, Division of Psychiatry and Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Queen Square Institute of Neurology, University College London, London, United Kingdom.
Quentin DerconApplied Computational Psychiatry Lab, Mental Health Neuroscience Department, Division of Psychiatry and Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Queen Square Institute of Neurology, University College London, London, United Kingdom.
Terezie SedlinskaOkinawa Institute of Science and Technology, Okinawa, Japan.
Michael BrowningDepartment of Psychiatry, University of Oxford, Oxford, United Kingdom.
Harry CostelloDivision of Psychiatry, Mental Health Neuroscience Department, University College London, London, United Kingdom.
Quentin J M HuysApplied Computational Psychiatry Lab, Mental Health Neuroscience Department, Division of Psychiatry and Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Queen Square Institute of Neurology, University College London, London, United Kingdom.

Funding

Wellcome Trust 221826Wellcome Trust 226790
6 · The paper itself

Abstract

Importance: Mechanistic biomarkers for guiding treatment selection require selective sensitivity to specific pharmacological interventions. Reinforcement learning processes show potential, but there have been conflicting and sometimes inconsistent reports on how dopamine and serotonin-2 key targets in treating common mental illnesses-affect reinforcement learning in humans. Objective: To perform a meta-analysis of pharmacological manipulations of dopamine and serotonin and examine whether they show distinct associations with reinforcement learning components in humans. Data Sources: Ovid MEDLINE/PubMed, Embase, and PsycInfo databases were searched for studies published between January 1, 1946, and January 19, 2023 (repeated April 9, 2024, and October 15, 2024), investigating dopaminergic or serotonergic effects on reward and punishment processes in humans according to PRISMA guidelines. Study Selection: Studies reporting randomized, placebo-controlled, dopaminergic or serotonergic manipulations on a behavioral outcome from a reward or punishment processing task in healthy humans were included. Data Extraction and Synthesis: Standardized mean difference (SMD) scores were calculated for the comparison between each drug (dopamine or serotonin) and placebo on a behavioral reward or punishment outcome and quantified in random-effects models for overall reward or punishment processes and 4 main subcategories. Study quality (Cochrane Collaboration tool), moderators, heterogeneity, and publication bias were also assessed. Main Outcomes and Measures: Performance on reward or punishment processing tasks. Results: In total, 102 studies conducted among healthy volunteers were included (2291 participants receiving dopamine vs 2284 receiving placebo and 1491 receiving serotonin vs 1523 receiving placebo). Dopamine was associated with an increase in overall reward (SMD, 0.18; 95% CI, 0.09 to 0.28) but not punishment function (SMD, -0.06; 95% CI, -0.26 to 0.13). Serotonin was not meaningfully associated with overall punishment (SMD, 0.22; 95% CI, -0.04 to 0.49) or reward (SMD, 0.02; 95% CI, -0.33 to 0.36). Dopaminergic and serotonergic manipulations had distinct associations with subcomponents. Dopamine was associated with reward learning or sensitivity (SMD, 0.26; 95% CI, 0.11 to 0.40), reward discounting (SMD, -0.08; 95% CI, -0.14 to -0.01), and reward vigor (SMD, 0.32; 95% CI, 0.11 to 0.54). By contrast, serotonin was associated with punishment learning or sensitivity (SMD, 0.32; 95% CI, 0.05 to 0.59), reward discounting (SMD, -0.35; 95% CI, -0.67 to -0.02), and aversive pavlovian processes (within-participant studies only; SMD, 0.36; 95% CI, 0.20 to 0.53). Conclusions and Relevance: In this study, pharmacological manipulations of both dopamine and serotonin had measurable associations with reinforcement learning in humans. The selective associations with different components suggest that reinforcement learning tasks could form the basis of selective, mechanistically interpretable biomarkers to support treatment assignment.

Indexed as

DopamineDopamine AgentsPunishmentRewardSerotoninHumansReinforcement, PsychologyDopamineDopamine AgentsSerotonin

Identifiers

PMID40498519
PMCPMC12159863

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.