Adverse Childhood Experiences and Amygdalar Reduction: High-Resolution Segmentation Reveals Associations With Subnuclei and Psychiatric Outcomes
Bibliographic record
Abstract
The aim of the present study was 2-fold: (1) to utilize improved amygdala segmentation and exploratory factor analysis to characterize the latent volumetric structure among amygdala nuclei and (2) to assess the effect of adverse childhood experiences (ACEs) on amygdalar morphometry and current psychiatric symptoms. To investigate these aims, structural (T1) MRI and self-report data were obtained from 119 emerging adults. Regression analysis showed that higher ACE scores were related to reduced volume of the right, but not the left, amygdalar segments. Further, exploratory factor analysis yielded a two-factor structure, basolateral and central-medial nuclei of the right amygdala. Stractual equation modeling analyses revealed that higher ACE scores were significantly related to a reduced volume of the right basolateral and central-medial segments. Furthermore, reduction in the right basolateral amygdala was associated with increased anxiety, depressive symptoms, and alcohol use. This association supports an indirect effect between early adversity and psychiatric problems via reduced right basolateral amygdalar volume. The high-resolution segmentation results reveal a latent structure among amygdalar nuclei, which is consistent with prior work conducted in nonhuman mammals. These findings extend previous reports linking early adversity, right amygdala volume, and psychopathology.
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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".