Depressogenic self-schemas are associated with smaller regional grey matter volume in never-depressed preadolescents
Bibliographic record
Abstract
Self-referential processing (i.e., self-schemas that guide processing of self-descriptive information) emerges early in youth, with deeper encoding of negative self-descriptors and/or shallower encoding of positive self-descriptors causally linked to depression. However, the relationship between depressogenic self-schemas and brain structure is unclear. We investigated associations between self-schemas and regional grey matter volume (GMV) in 84 never-depressed preadolescents oversampled for depression risk based on maternal depression history. Self-schemas were assessed using a Self-Referent Encoding Task (SRET) and regional GMV was indexed via voxel-based morphometry analysis of structural magnetic resonance imaging data. Youths' positive self-schemas were associated with greater regional GMV within the ventrolateral prefrontal cortex (vlPFC) and posterior cingulate cortex (PCC), while negative self-schemas were associated with smaller regional GMV within vlPFC and PCC, areas important to emotion regulation and self-referential processing. These associations remained significant after controlling for youths' concurrent depressive symptoms. Exploratory mediation analysis suggested that adolescents' depressogenic self-schemas may mediate associations between GMV and depressive symptoms. Our findings suggest that the observed GMV variations within vlPFC and PCC may serve as neurobiological markers of depressogenic self-schemas during preadolescence.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".