ReviewPsychopharmacology2019
Computational psychopharmacology: a translational and pragmatic approach.
Review in Psychopharmacology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis 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.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.
- "The wrong tools for the right job": a critical meta-analysis of traditional tests to assess behavioural impacts of maternal separation.Psychopharmacology · 2023Pooled it
- Impaired Learning From Negative Feedback in Stimulant Use Disorder: Dopaminergic Modulation.The international journal of neuropsychopharmacology · 2021Trial
- Comparable roles for serotonin in rats and humans for computations underlying flexible decision-making.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024Article
- Modulation of ventromedial orbitofrontal cortical glutamatergic activity affects the explore-exploit balance and influences value-based decision-making.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Editorial: Computational accounts of reinforcement learning and decision making in psychiatric disorders.Frontiers in psychiatry · 2022Article
- Prefrontal cortex and depression.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2022Review
- Article
- Editorial: bridging the gap with computational and translational psychopharmacology.Psychopharmacology · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
rationalePsychopharmacology needs novel quantitative measures and theoretical approaches based on computational modelling that can be used to help translate behavioural findings from experimental animals to humans, including patients with neuropsychiatric disorders.
objectivesThis brief review exemplifies this approach when applied to recent published studies of the effects of manipulating central dopaminergic and serotoninergic systems in rodents and marmoset monkeys, and possible comparisons with healthy human volunteers receiving systemic agents or patients with depression and schizophrenia.
methodsBehavioural effects of central depletions of dopamine or serotonin in monkeys in probabilistic learning paradigms are characterised further by computational modelling methods and related to rodent and human data.
resultsSeveral examples are provided of the power of computational modelling to derive new measures and reappraise conventional explanations of regional neurotransmitter depletion and other drug effects, whilst enhancing construct validation in patient groups. Specifically, effects are shown on such parameters as 'stimulus stickiness' and 'side stickiness', which occur over and above effects on standard parameters of reinforcement learning, reminiscent of some early innovations in data analysis in psychopharmacology.
conclusionsComputational modelling provides a useful methodology for further detailed analysis of behavioural mechanisms that are affected by pharmacological manipulations across species and will aid the translation of experimental findings to understand the therapeutic effects of medications in neuropsychiatric disorders, as well as facilitating future drug discovery.
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What OpenQuestion holds
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.