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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. | LitMetric

AI Article Synopsis

  • Research shows a connection between depression and issues with processing rewards in both humans and animals, hinting at potential biomarkers and treatment targets.
  • The study critically examined existing findings and theories, using established causality frameworks and modern prediction techniques.
  • Through a preregistered meta-analysis, the researchers pointed out various challenges and determined that while reward processing issues aren't strong enough for clinical prediction, they might still contribute causally to depression.

Article 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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10726343PMC
http://dx.doi.org/10.1016/j.biopsych.2020.06.012DOI Listing

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