Children’s neural reactivity to maternal praise and criticism: Associations with early depressive symptoms and maternal depression
Bibliographic record
Abstract
Abstract Caregiving experiences are implicated in children’s depression risk; however, children’s neural reactivity to positive and negative feedback from mothers, a potential mediator of depression risk, is poorly understood. In a sample of 81 children (Mage = 11.12 years, SDage = 0.63), some of whom were recruited based on a maternal history of depression (n = 29), we used fMRI to characterize children’s neural responses to maternal praise and criticism. Maternal history of depression was unrelated to children’s brain activity during both the praise and criticism conditions; however, ROI analyses showed that children’s self-reported depressive symptoms were negatively associated with functional activity in the left anterior insula and right putamen while hearing maternal criticism. Whole-brain analyses showed that children’s depressive symptoms were positively associated with left inferior frontal gyrus activity while listening to maternal praise. These findings complement past work implicating these brain regions in the processing of emotionally salient stimuli, reward processing, and internal speech. Given associations between early depressive symptoms and later disorder, findings suggest that maladaptive neural processing of maternal feedback may contribute to children’s early emerging risk for depression.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".