A negative association between brainstem pontine grey-matter volume, well-being and resilience in healthy twins
Bibliographic record
Abstract
Background: Associations between well-being, resilience to trauma and the volume of grey-matter regions involved in affective processing (e.g., threat/reward circuits) are largely unexplored, as are the roles of shared genetic and environmental factors derived from multivariate twin modelling. Methods: This study presents, to our knowledge, the first exploration of well-being and volumes of grey-matter regions involved in affective processing using a region-of-interest, voxel-based approach in 263 healthy adult twins (60% monozygotic pairs, 61% females, mean age 39.69 yr). To examine patterns for resilience (i.e., positive adaptation following adversity), we evaluated associations between the same brain regions and well-being in a trauma-exposed subgroup. Results: We found a correlated effect between increased well-being and reduced grey-matter volume of the pontine nuclei. This association was strongest for individuals with higher resilience to trauma. Multivariate twin modelling suggested that the common variance between the pons volume and well-being scores was due to environmental factors. Limitations: We used a cross-sectional sample; results need to be replicated longitudinally and in a larger sample. Conclusion: Associations with altered grey matter of the pontine nuclei suggest that basic sensory processes, such as arousal, startle, memory consolidation and/or emotional conditioning, may have a role in well-being and resilience.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 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".