Intergenerational transmission of depression risk and the developing brain.
Bibliographic record
Abstract
Parental depression is a well-established risk factor for depression in offspring. This intergenerational transmission involves a diverse array of mechanisms, both familial and environmental, working at different levels to increase depression in offspring. To identify modifiable mechanisms for depression among this heterogeneity, recent work has turned to neurobiological measures as more proximal indicators of risk. Indeed, there is emerging evidence that one point of convergence for multiple proposed mechanisms of intergenerational transmission may be the effect they have on the developing brain. In this narrative review, we discuss research that has examined associations between familial and environmental influences and offspring brain function, focusing specifically on direct neural measures of cognitive control, motivation, and affective processing. We first survey evidence indicating that genes, gestational stress, parenting, and stress exposure are associated with alterations in these neural measures from infancy to young adulthood. We then present a preliminary conceptual model outlining the roles of altered neural indices of cognitive control, motivation, and affective processing in pathways from parental depression to offspring depression and discuss future research avenues addressing limitations of the existing research. Finally, we conclude by discussing the potential of this research to inform the development of targeted preventive interventions aimed at disrupting the intergenerational transmission of depression. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".