Early adversity and combat exposure interact to influence anterior cingulate cortex volume in combat veterans
Bibliographic record
Abstract
OBJECTIVE: Childhood and combat trauma have been observed to interact to influence amygdala volume in a sample of U.S. military veterans with and without PTSD. This interaction was assessed in a second, functionally-related fear system component, the pregenual and dorsal anterior cingulate cortex, using the same sample and modeling approach. METHOD: Anterior cingulate cortical tissues (gray + white matter) were manually-delineated in 1.5 T MR images in 87 U.S. military veterans of the Vietnam and Persian Gulf wars. Hierarchical multiple regression modeling was used to assess associations between anterior cingulate volume and the following predictors, trauma prior to age 13, combat exposure, the interaction of early trauma and combat exposure, and PTSD diagnosis. RESULTS: As previously observed in the amygdala, unique variance in anterior cingulate cortical volume was associated with both the diagnosis of PTSD and with the interaction of childhood and combat trauma. The pattern of the latter interaction indicated that veterans with childhood trauma exhibited a significant inverse linear relationship between combat trauma and anterior cingulate volume while those without childhood trauma did not. Such associations were not observed in hippocampal or total cerebral tissue volumes. CONCLUSIONS: In the dorsal anterior cingulate cortex, as in the amygdala, early trauma may confer excess sensitivity to later combat trauma.
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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 | 0.000 | 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) | 0.001 | 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".