Great Expectations: A Critical Review of and Suggestions for the Study of Reward Processing as a Cause and Predictor of Depression.

Depression Development Meta-analysis Reliability Reproducibility Reward processing

Journal

Biological psychiatry
ISSN: 1873-2402
Titre abrégé: Biol Psychiatry
Pays: United States
ID NLM: 0213264

Informations de publication

Date de publication:
15 01 2021
Historique:
received: 02 03 2020
revised: 20 05 2020
accepted: 10 06 2020
pubmed: 17 8 2020
medline: 24 4 2021
entrez: 17 8 2020
Statut: ppublish

Résumé

Both human and animal studies support the relationship between depression and reward processing abnormalities, giving rise to the expectation that neural signals of these processes may serve as biomarkers or mechanistic treatment targets. Given the great promise of this research line, we scrutinized those findings and the theoretical claims that underlie them. To achieve this, we applied the framework provided by classical work on causality as well as contemporary approaches to prediction. We identified a number of conceptual, practical, and analytical challenges to this line of research and used a preregistered meta-analysis to quantify the longitudinal associations between reward processing abnormalities and depression. We also investigated the impact of measurement error on reported data. We found that reward processing abnormalities do not reach levels that would be useful for clinical prediction, yet the available evidence does not preclude a possible causal role in depression.

Identifiants

pubmed: 32797941
pii: S0006-3223(20)31700-5
doi: 10.1016/j.biopsych.2020.06.012
pii:
doi:

Types de publication

Journal Article Meta-Analysis Research Support, N.I.H., Intramural Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

134-143

Subventions

Organisme : Intramural NIH HHS
ID : ZIA MH002957
Pays : United States

Informations de copyright

Published by Elsevier Inc.

Auteurs

Dylan M Nielson (DM)

Section on Clinical and Computational Psychiatry (CompΨ), National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland.

Hanna Keren (H)

Section on Clinical and Computational Psychiatry (CompΨ), National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland.

Georgia O'Callaghan (G)

Section on Clinical and Computational Psychiatry (CompΨ), National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland.

Sarah M Jackson (SM)

Section on Clinical and Computational Psychiatry (CompΨ), National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland.

Ioanna Douka (I)

Section on Clinical and Computational Psychiatry (CompΨ), National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland.

Pablo Vidal-Ribas (P)

Social and Behavioral Science Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland.

Narun Pornpattananangkul (N)

Department of Psychology, University of Otago, Dunedin, New Zealand.

Christopher C Camp (CC)

Section on Clinical and Computational Psychiatry (CompΨ), National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland.

Lisa S Gorham (LS)

Section on Clinical and Computational Psychiatry (CompΨ), National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland.

Christine Wei (C)

Section on Clinical and Computational Psychiatry (CompΨ), National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland.

Stuart Kirwan (S)

Section on Clinical and Computational Psychiatry (CompΨ), National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland.

Charles Y Zheng (CY)

Machine Learning Team, Functional Magnetic Resonance Imaging Facility, National Institutes of Health, Bethesda, Maryland.

Argyris Stringaris (A)

Section on Clinical and Computational Psychiatry (CompΨ), National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland. Electronic address: argyris.stringaris@nih.gov.

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