Sensitive periods for the link between childhood maltreatment and brain aging during adulthood
Bibliographic record
Abstract
Background: Childhood maltreatment (CM) is associated with the early onset of psychopathology and accelerated biological aging. However, outcomes vary widely among individuals with CM history. This variability may, in part, be explained by differences in age of exposure to CM. In this study, we examined whether CM was associated with accelerated brain aging, depending on the timing of exposure (i.e., 'sensitive periods'). Method: A machine learning algorithm of brain age trained prior to the current study in 3377 healthy individuals was employed in a CM dataset of 150 adult postnatal women, 92 of whom provided MRI data. CM history was retrospectively assessed from birth to age 18 years. Brain-predicted age was calculated from T1-weighted MRI scans. Brain age gap (BAG) was quantified as the disparity between brain-predicted age, relative to chronological age. Sensitive periods were identified using random forest regression with conditional inference trees. Results: CM severity was associated with greater BAG (β = 0.34, p < 0.001). The most robust type/time risk factors for greater BAG were parental verbal abuse between ages 7 and 15 years, parental physical abuse between ages 4 and 6 years, witnessing sibling violence between ages 4 and 15 years, and sexual abuse between ages 4-6. Parental verbal abuse (7-15 years) and parental physical abuse (4-6) were the variables that were the most important predictors above and beyond duration, multiplicity (number of exposures), and cumulative maltreatment severity. Conclusion: These findings suggest a link between CM and accelerated brain aging, with certain developmental periods appearing more sensitive to these effects. To the best of our knowledge, this study is the first to identify sensitive periods for the link between CM and brain aging in adults, and the first to examine the link between CM and brain aging in postnatal women. Together, these results suggest that CM's association with brain development is complex and warrants nuanced approaches to investigating the possible mechanisms underlying its effects.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".