Maternal Depression, Child Temperament, and Early-Life Stress Predict Never-Depressed Preadolescents’ Functional Connectivity During a Negative-Mood Induction
Bibliographic record
Abstract
Understanding the development of depression can inform etiology and prevention/intervention. Maternal depression and maladaptive patterns of temperament (e.g., low positive emotionality [PE] or high negative emotionality, especially sadness) are known to predict depression. Although it is unclear how these risks cause depression, altered functional connectivity (FC) during negative-emotion processing may play an important role. We investigated whether maternal depression and age-3 emotionality predicted FC during negative mood reactivity in never-depressed preadolescents and whether these relationships were augmented by early-life stress. Maternal depression predicted decreased medial prefrontal cortex (mPFC)–amygdala and mPFC–insula FC but increased mPFC–posterior cingulate cortex (PCC) FC. PE predicted increased dorsolateral prefrontal cortex–amygdala FC, whereas sadness predicted increased PCC-based FC in insula, orbitofrontal cortex, and anterior cingulate cortex (ACC). Sadness was more strongly associated with PCC–insula and PCC–ACC FC as early stress increased. Findings indicate that early depression risks may be mediated by FC underlying negative-emotion processing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".