A registered report of error-related negativity and reward positivity as biomarkers of depression: P-Curving the evidence.

Published
January 29, 2020
Journal
International journal of psychophysiology : official journal of the International Organization of Psychophysiology
PICOID
9e9c5c8c
DOI
Citations
33
Keywords
Depression, Error-related negativity (ERN), Performance monitoring, Reward positivity (RewP), p curve
Copyright
Copyright © 2020 Elsevier B.V. All rights reserved.
Patients/Population/Participants

-

Intervention

p-curve analysis

Comparison

ERN and RewP literatures

Outcome

evidential value for a relationship between each ERP and depression

Abstract

P
I
C
O

Performance-monitoring event-related brain potentials (ERPs), such as the error-related negativity (ERN) and reward positivity (RewP), are advocated as biomarkers of depression symptoms and risk. However, a recent meta-analysis indicated effect size heterogeneity in the ERN and RewP literatures. Hence, advocating these ERPs as biomarkers of depression might be premature or possibly misguided due to the selective reporting of significant analyses on the part of researchers (e.g., p-hacking or omission of non-significant findings). The present study quantified the degree of selective reporting and the evidential value for a true relationship between depression and ERN and RewP using a p-curve analysis. We predicted that the ERN and RewP literatures would fail to show evidential value for a relationship between each ERP and depression. Contrary to expectations, both literatures showed evidential value, albeit weak. The statistical power of the included ERN studies was between 20% and 25%, and the statistical power of the RewP was around 27%. Taken together, these findings provide support for a relationship between these ERPs and depression, which strengthens claims that these ERPs represent candidate biomarkers of depression symptoms and risk. In light of the evidence for these relationships being weak, some recommendations moving forward include conducting a priori power analyses, increasing sample sizes to improve statistical power, assessing the internal consistency of ERP scores, and carefully planning statistical approaches to maximize power.

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