Amygdala subnuclei volumes, functional connectivity, and social–emotional outcomes in children born very preterm
Bibliographic record
Abstract
Abstract Children born very preterm can demonstrate social-cognitive impairments, which may result from limbic system dysfunction. Altered development of the subnuclei of the amygdala, stress-sensitive regions involved in emotional processing, may be key predictors of social-skill development. In a prospective cohort study, 7-year-old children born very preterm underwent neurodevelopmental testing and brain MRI. The Child Behavioral Checklist was used to assess social–emotional outcomes. Subnuclei volumes were extracted automatically from structural scans (n = 69) and functional connectivity (n = 66) was examined. General Linear Models were employed to examine the relationships between amygdala subnuclei volumes and functional connectivity values and social–emotional outcomes. Sex was a significant predictor of all social–emotional outcomes (P < 0.05), with boys having poorer social–emotional outcomes. Smaller right basal nuclei volumes (B = -0.043, P = 0.014), smaller right cortical volumes (B = -0.242, P = 0.02) and larger right central nuclei volumes (B = 0.85, P = 0.049) were associated with increased social problems. Decreased connectivity strength between thalamic and amygdala networks and smaller right basal volumes were significant predictors of greater social problems (both, P < 0.05), effects which were stronger in girls (P = 0.025). Dysregulated maturation of the amygdala subnuclei, along with altered connectivity strength in stress-sensitive regions, may reflect stress-induced dysfunction and can be predictive of social–emotional outcomes.
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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 | 0.002 |
| 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) | 0.001 | 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".