Childhood Maltreatment and Deviations from Normative Brain Structure: Results from 3,711 Individuals from the ENIGMA MDD and ENIGMA PTSD
Bibliographic record
Abstract
Childhood maltreatment (CM), encompassing abuse and neglect, affects over two-thirds of the general population and increases risk for stress-related psychopathology, including major depressive disorder (MDD) and posttraumatic stress disorder (PTSD). The extent to which neuroanatomical alterations in MDD and PTSD are attributable to CM, however, is uncertain. Here, we analyzed CM and 3D structural brain MRI data from 3,711 participants in the ENIGMA MDD and PTSD Working Groups (25 sites; 33.3+/-13.0 years; 59.9% female). Normative modeling estimated deviation z-scores for 14 subcortical volumes (SV), 68 cortical thickness (CT), and 68 surface area (SA) measures, capturing differences from population norms. Transdiagnostic associations between CM and brain deviation scores were evaluated within each sex and age cohort. In young adults (ages 18-35), abuse was associated with larger volumes in thalamus and pallidum, thinner isthmus cingulate and middle frontal regions, and thicker medial orbitofrontal cortex; there were no significant effects in pediatric (≤18 years) participants. The strongest effects were observed in young female adults (|β|=.07-.22, q<.05): greater abuse and neglect were correlated with smaller hippocampus and putamen volumes, thinner entorhinal cortex, and smaller SA in fusiform/inferior parietal regions, and with larger SA in orbitofrontal and occipital cortices. In males, abuse had widespread effects on CT and SA (|β|=.1-.18, q<.05); effects for neglect were minimal. Our findings of age- and sex-specific instantiations of CM on brain morphometry highlight the importance of developmental context in understanding how adverse experiences shape neurobiological vulnerability to MDD and PTSD.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".