The prevalence of the Val66Met polymorphism in musicians: Possible evidence for compensatory neuroplasticity from a pilot study
Bibliographic record
Abstract
The study compared the prevalence of the Val66Met Brain-derived Neurotrophic Factor single nucleotide polymorphism (rs6265) in a sample of musicians (N = 50) to an ethnically matched general population sample from the 1000 Human Genome Project (N = 424). Met-carriers of the polymorphism (Val/Met and Met/Met genotypes) are typically present in 25-30% of the general population and have associated deficits in motor learning and plasticity. Many studies have assessed the benefits of long-term music training for neuroplasticity and motor learning. This study takes a unique genetic approach investigating if the prevalence of the Val66Met BDNF polymorphism, which negatively affects motor learning, is significantly different in musicians from the general population. Our genotype and allele frequency analyses revealed that the distribution of the Val66Met polymorphism was not significantly different in musicians versus the general population (p = 0.6447 for genotype analysis and p = 0.8513 allele analysis). In the Musician sample (N = 50), the prevalence of the Val/Met genotype was 40% and the prevalence of the Met/Met genotype was 2%. In the 1000 Human Genome Project subset (N = 424), the prevalence of Val/Met was 33.25% and the Met/Met genotype prevalence was 4%. Therefore, musicians do exist with the Val66Met polymorphism and the characteristics of long-term music training may compensate for genetic predisposition to motor learning deficits. Since the polymorphism has significant implications for stroke rehabilitation, future studies may consider the implications of the polymorphism in music-based interventions such as Neurologic Music Therapy.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".