Childhood trauma and amygdala nuclei volumes in youth at risk for mental illness
Bibliographic record
Abstract
BACKGROUND: Adults with significant childhood trauma and/or serious mental illness may exhibit persistent structural brain changes within limbic structures, including the amygdala. Little is known about the structure of the amygdala prior to the onset of SMI, despite the relatively high prevalence of trauma in at-risk youth. METHODS: Data were gathered from the Canadian Psychiatric Risk and Outcome study. A total of 182 youth with a mean age of 18.3 years completed T1-weighted MRI scans along with clinical assessments that included questionnaires on symptoms of depression and anxiety. Participants also completed the Childhood Trauma and Abuse Scale. We used a novel subfield-specific amygdala segmentation workflow as a part of FreeSurfer 6.0 to examine amygdala structure. RESULTS: Participants with higher trauma scores were more likely to have smaller amygdala volumes, particularly within the basal regions. Among various types of childhood trauma, sexual and physical abuse had the largest effects on amygdala subregions. Abuse-related differences in the right basal region mediated the severity of depression and anxiety symptoms, even though no participants met criteria for clinical diagnosis at the time of assessment. CONCLUSION: The experience of physical or sexual abuse may leave detectable structural alterations in key regions of the amygdala, potentially mediating the risk of psychopathology in trauma-exposed youth.
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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 | 0.000 |
| 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) | 0.003 | 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".