Neuroanatomical Correlates of Musicianship as Revealed by Cortical Thickness and Voxel-Based Morphometry
Bibliographic record
Abstract
We used a multimethod approach to investigate the neuroanatomical correlates of musicianship and absolute pitch (AP). Cortical thickness measures, interregional correlations applied to these thicknesses, and voxel-based morphometry (VBM) were applied to the same magnetic resonance imaging data set of 71 musicians (27 with AP) and 64 nonmusicians. Cortical thickness was greater in musicians with peaks in superior temporal and dorsolateral frontal regions. Correlations between 2 seed points, centered on peaks of thickness difference within the right frontal cortex, and all other points across the cortex showed greater specificity of significant correlations among musicians, with fewer and more discrete areas correlating with the frontal seeds, including the superior temporal cortex. VBM of gray matter (GM)-classified voxels yielded a strongly right-lateralized focus of greater GM concentration in musicians centered on the posterolateral aspect of Heschl's gyrus. Together, these results are consistent with functional evidence emphasizing the importance of a frontotemporal network of areas heavily relied upon in the performance of musical tasks. Among musicians, contrasts of AP possessors and nonpossessors showed significantly thinner cortex among possessors in a number of areas, including the posterior dorsal frontal cortices that have been previously implicated in the performance of AP tasks.
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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 | 0.002 |
| 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 | 0.001 |
| 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".