Examining the influence of musical sophistication, cognitive performance, and social skills on the Brain Age Gap Estimate (BrainAGE)
Bibliographic record
Abstract
Brain age, an estimate of biological brain aging derived from neuroimaging, has been linked to cognitive and related factors. Metrics such as the Brain Age Gap Estimate (BrainAGE), depicting the discrepancy between predicted and chronological age, are commonly used to determine the influence of variables on brain aging. This study explored how cognitive ability, musical sophistication, and social skills contribute to BrainAGE in a sample of 81 healthy participants who underwent high-resolution magnetic resonance imaging and completed cognitive, musical, and social assessments. Following statistical analyses to fit the model, structural equation modelling was used to examine the influence of cognitive ability, assessed using the Delis-Kaplan Executive Function System, California Verbal Learning Test, and Wechsler Adult Intelligence Scale; musical sophistication, measured by the Goldsmiths Musical Sophistication Index; and social skills, evaluated using the Social Skills Inventory, on BrainAGE. Our findings demonstrated no significant influence of cognitive ability, musical expertise, or social skills on BrainAGE. These findings highlight the complexity of cognitive and social influences on brain age and underscore the need for further research into their interactive effects on neurobiological aging.
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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 | 0.002 | 0.008 |
| 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 | 0.001 |
| 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".