How Does Social Interaction Impact Grey Matter Volume in Healthy Older Women?
Bibliographic record
Abstract
Abstract Background The global population of adults over the age of 65 is expected to surpass 2 billion by 2050. Alongside this rise in the aging population, the incidence of age‐related cognitive decline and dementia will continue to grow. Importantly, women are at an elevated risk of cognitive decline compared to men. Therefore, it is imperative to identify modifiable risk and protective factors for aging women. The Lancet Commission highlighted low social contact as a modifiable risk factor for dementia and the Scaffolding Theory of Aging and Cognition‐Revised suggests potential neural structural benefits of such sources of enrichment. Structural neuroimaging allows for the examination of grey matter volume directly, in vivo. It was hypothesized that women with greater social interaction would demonstrate greater grey matter volume. Method The current study used magnetic resonance imaging to examine the relationship between grey matter volume and social interaction in healthy older women. Participants were all biological females over the age 60 and obtained from the Women’s Healthy Ageing Project. The current study included 151 healthy older women (MAge = 70.30 ± 2.88) who had 3T structural magnetic resonance imaging data and had completed the social composite score on the Short Form‐36 (SF‐36). The correlation between the social composite score and grey matter volume was examined using voxel‐based morphometry. Result The results did not reveal significant correlations between grey matter volume and scores on the SF‐36, although there were sub‐threshold (p<0.2, corrected) positive correlations in the posterior areas of the brain. A post‐hoc region of interest analysis also revealed a significant correlation between SF‐36 and grey matter volume in the cerebellum (p<0.05, corrected). Conclusion Age‐related cognitive decline continues to represent a significant and ongoing issue for our global population, particularly for women who are at an increased risk. It is crucial to continue to investigate the unique variables, such as social interaction, to improve the quality of life for aging women.
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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 | 0.000 | 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) | 0.002 | 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".