Aging of Amateur Singers and Non-singers: From Behavior to Resting-state Connectivity
Bibliographic record
Abstract
Healthy aging is associated with extensive changes in brain structure and physiology, with impacts on cognition and communication. The "mental exercise hypothesis" proposes that certain lifestyle factors such as singing-perhaps the most universal and accessible music-making activity-can affect cognitive functioning and reduce cognitive decline in aging, but the neuroplastic mechanisms involved remain unclear. To address this question, we examined the association between age and resting-state functional connectivity (RSFC) in 84 healthy singers and nonsingers in five networks (auditory, speech, language, default mode, and dorsal attention) and its relationship to auditory cognitive aging. Participants underwent cognitive testing and fMRI. Our results show that RSFC is not systematically lower with aging and that connectivity patterns vary between singers and nonsingers. Furthermore, our results show that RSFC of the precuneus in the default mode network was associated with auditory cognition. In these regions, lower RSFC was associated with better auditory cognitive performance for both singers and nonsingers. Our results show, for the first time, that basic brain physiology differs in singers and nonsingers and that some of these differences are associated with cognitive performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".