Long-Known Music Exposure Effects on Brain Imaging and Cognition in Early-Stage Cognitive Decline: A Pilot Study
Bibliographic record
Abstract
BACKGROUND: Repeated exposure to long-known music has been shown to have a beneficial effect on cognitive performance in patients with AD. However, the brain mechanisms underlying improvement in cognitive performance are not yet clear. OBJECTIVE: In this pilot study we propose to examine the effect of repeated long-known music exposure on imaging indices and corresponding changes in cognitive function in patients with early-stage cognitive decline. METHODS: Participants with early-stage cognitive decline were assigned to three weeks of daily long-known music listening, lasting one hour in duration. A cognitive battery was administered, and brain activity was measured before and after intervention. Paired-measures tests evaluated the longitudinal changes in brain structure, function, and cognition associated with the intervention. RESULTS: Fourteen participants completed the music-based intervention, including 6 musicians and 8 non-musicians. Post-baseline there was a reduction in brain activity in key nodes of a music-related network, including the bilateral basal ganglia and right inferior frontal gyrus, and declines in fronto-temporal functional connectivity and radial diffusivity of dorsal white matter. Musician status also significantly modified longitudinal changes in functional and structural brain measures. There was also a significant improvement in the memory subdomain of the Montreal Cognitive Assessment. CONCLUSION: These preliminary results suggest that neuroplastic mechanisms may mediate improvements in cognitive functioning associated with exposure to long-known music listening and that these mechanisms may be different in musicians compared to non-musicians.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".