Multiple risk markers for increases in depression symptoms across two years: Evidence from the reward positivity and the error-related negativity
Bibliographic record
Abstract
BACKGROUND: Both a blunted Reward Positivity (RewP) and Error-Related Negativity (ERN) have been associated with depression. Associations between these neural markers and depression have been observed cross-sectionally, but evidence that they can prospectively predict the development of, or increases in, symptoms of depression is more limited. METHOD: In this study, we collected EEG data from 157 young adults at a baseline visit (T1), using the Doors and Flanker Tasks to elicit the RewP and the ERN respectively. Participants also reported on symptoms of depression at T1, and multiple times across two academic years (T2 - T8). RESULTS: Using a multilevel model with the RewP and the ERN as predictors, we found that the RewP predicted future symptoms of depression, while controlling for symptoms of depression at T1, such that a blunted RewP at baseline predicted higher depressive symptoms later. In our data, however, the ERN was not a significant predictor of increases in depression symptoms. CONCLUSIONS: These findings replicate previous work showing the RewP prospectively predicted increases in depression, and further suggest the specificity of this association. Results support the utility of the RewP as a neurophysiological marker that can help clarify the etiology of depression and inform treatment planning.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".