The development of depressogenic self-schemas: Associations with children's regional grey matter volume in ventrolateral prefrontal cortex
Bibliographic record
Abstract
Abstract Cognitive theories of depression contend that biased cognitive information processing plays a causal role in the development of depression. Extensive research shows that deeper processing of negative and/or shallower processing of positive self-descriptors (i.e., negative and positive self-schemas) predicts current and future depression in adults and children. However, the neural correlates of the development of self-referent encoding are poorly understood. We examined children's self-referential processing using the self-referent encoding task (SRET) collected from 74 children at ages 6, 9, and 12; around age 10, these children also contributed structural magnetic resonance imaging data. From age 6 to age 12, both positive and negative self-referential processing showed mean-level growth, with positive self-schemas increasing relatively faster than negative ones. Further, voxel-based morphometry showed that slower growth in positive self-schemas was associated with lower regional gray matter volume (GMV) in ventrolateral prefrontal cortex (vlPFC). Our results suggest that smaller regional GMV within vlPFC, a critical region for regulatory control in affective processing and emotion development, may have implications for the development of depressogenic self-referential processing in mid-to-late childhood.
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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 | 0.001 | 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".