Evidence map›Paper›PMID 36752156›Full record

ArticlePsychological medicine2023

A computational neuroimaging study of reinforcement learning and goal-directed exploration in schizophrenia spectrum disorders.

A J Culbreth, E K Schwartz, M J Frank, E C Brown, Z Xu, S Chen, J M Gold, J A Waltz

Open access · greenAbstract read
In one paragraph

Article in Psychological medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.1field-weighted citation impact, top 25% of its field
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

2 citing papers in PubMed, 5 citations in OpenAlex.

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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

8 authors at 4 institutions in 1 country.

A J CulbrethDepartment of Psychiatry, Maryland Psychiatric Research Center (MPRC), University of Maryland School of Medicine, Baltimore, MD, USA.
E K SchwartzSignant Health, San Diego, CA, USA.
M J FrankDepartment of Cognitive, Linguistic and Psychological Sciences, Brown University, Providence, RI, USA.
E C BrownSchool of Health and Care Management, Arden University, Berlin, Germany.
Z XuApplied LifeSciences & Systems, Morrisville, NC, USA.
S ChenDepartment of Psychiatry, Maryland Psychiatric Research Center (MPRC), University of Maryland School of Medicine, Baltimore, MD, USA.
J M GoldDepartment of Psychiatry, Maryland Psychiatric Research Center (MPRC), University of Maryland School of Medicine, Baltimore, MD, USA.
J A WaltzDepartment of Psychiatry, Maryland Psychiatric Research Center (MPRC), University of Maryland School of Medicine, Baltimore, MD, USA.ORCID 0000-0002-9236-3719
University of Maryland, Baltimore · USApplied LifeSciences & Systems (United States) · USBrown University · USGuardant (United States) · US

Funding

Reward System Function as a Potential Mediator of the Impact of Stress on Psychopathology: A Computational Neuroimaging StudyR01MH115031 · NIMH · UNIVERSITY OF MARYLAND BALTIMORE · PI WALTZ, JAMES A · 2018 to 2022
$2.6M
NIMH NIH HHS R01 MH115031
6 · The paper itself

Abstract

backgroundPrior evidence indicates that negative symptom severity and cognitive deficits, in people with schizophrenia (PSZ), relate to measures of reward-seeking and loss-avoidance behavior (implicating the ventral striatum/VS), as well as uncertainty-driven exploration (reliant on rostrolateral prefrontal cortex/rlPFC). While neural correlates of reward-seeking and loss-avoidance have been examined in PSZ, neural correlates of uncertainty-driven exploration have not. Understanding neural correlates of uncertainty-driven exploration is an important next step that could reveal insights to how this mechanism of cognitive and negative symptoms manifest at a neural level.

methodsWe acquired fMRI data from 29 PSZ and 36 controls performing the Temporal Utility Integration decision-making task. Computational analyses estimated parameters corresponding to learning rates for both positive and negative reward prediction errors (RPEs) and the degree to which participates relied on representations of relative uncertainty. Trial-wise estimates of expected value, certainty, and RPEs were generated to model fMRI data.

resultsBehaviorally, PSZ demonstrated reduced reward-seeking behavior compared to controls, and negative symptoms were positively correlated with loss-avoidance behavior. This finding of a bias toward loss avoidance learning in PSZ is consistent with previous work. Surprisingly, neither behavioral measures of exploration nor neural correlates of uncertainty in the rlPFC differed significantly between groups. However, we showed that trial-wise estimates of relative uncertainty in the rlPFC distinguished participants who engaged in exploratory behavior from those who did not. rlPFC activation was positively associated with intellectual function.

conclusionsThese results further elucidate the nature of reinforcement learning and decision-making in PSZ and healthy volunteers.

Indexed as

Magnetic Resonance ImagingReinforcement, PsychologyRewardSchizophreniaAdultAvoidance LearningDecision MakingFemaleGoalsHumansMaleMiddle AgedNeuroimagingPrefrontal CortexSchizophrenic PsychologyUncertaintyDopaminemotivationpsychosisreward prediction errorrostrolateral prefrontal cortexventral striatum

Identifiers

PMID36752156
PMCPMC12433706
OpenAlexW4319461153

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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.