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

  • Dylan M. Nielson
  • , Hanna Keren
  • , Georgia O'Callaghan
  • , Sarah M. Jackson
  • , Ioanna Douka
  • , Pablo Vidal-Ribas
  • , Narun Pornpattananangkul
  • , Christopher C. Camp
  • , Lisa S. Gorham
  • , Christine Wei
  • , Stuart Kirwan
  • , Charles Y. Zheng
  • , Argyris Stringaris
  • National Institutes of Health
  • University of Otago

Research output: Contribution to journalReview articlepeer-review

71 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)134-143
Number of pages10
JournalBiological Psychiatry
Volume89
Issue number2
DOIs
StatePublished - 15 Jan 2021
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2020

Funding

This research was supported in part by the Intramural Research Program of the National Institute of Mental Health National Institutes of Health (NIH) (Grant No. ZIA-MH002957-01 [to AS]). This work used the computational resources of the NIH HPC (high-performance computing) Biowulf cluster ( http://hpc.nih.gov ). The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, and approval of the manuscript; or decision to submit the manuscript for publication. The views expressed in this article do not necessarily represent the views of the National Institutes of Health, the Department of Health and Human Services, or the United States Government. This research was supported in part by the Intramural Research Program of the National Institute of Mental Health National Institutes of Health (NIH) (Grant No. ZIA-MH002957-01 [to AS]). This work used the computational resources of the NIH HPC (high-performance computing) Biowulf cluster (http://hpc.nih.gov). The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, and approval of the manuscript; or decision to submit the manuscript for publication. The views expressed in this article do not necessarily represent the views of the National Institutes of Health, the Department of Health and Human Services, or the United States Government. The authors report no biomedical financial interests or potential conflicts of interest. All data collated for this study are available at https://osf.io/whvam/. All codes for analyses in this study are available at https://github.com/nimh-comppsych/great_expectations.

FundersFunder number
National Institute of Mental Health National Institutes of Health
National Institutes of Health
U.S. Department of Health and Human Services
National Institute of Mental HealthZIAMH002957

    Keywords

    • Depression
    • Development
    • Meta-analysis
    • Reliability
    • Reproducibility
    • Reward processing

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