Audiovisual structural connectivity in musicians and non-musicians: a cortical thickness and diffusion tensor imaging study
Bibliographic record
Abstract
Our sensory systems provide complementary information about the multimodal objects and events that are the target of perception in everyday life. Professional musicians' specialization in the auditory domain is reflected in the morphology of their brains, which has distinctive characteristics, particularly in areas related to auditory and audio-motor activity. Here, we combined diffusion tensor imaging (DTI) with a behavioral measure of visually induced gain in pitch discrimination, and we used measures of cortical thickness (CT) correlations to assess how auditory specialization and musical expertise are reflected in the structural architecture of white and grey matter relevant to audiovisual processing. Across all participants (n = 45), we found a correlation (p < 0.001) between reliance on visual cues in pitch discrimination and the fractional anisotropy (FA) in the left inferior fronto-occipital fasciculus (IFOF), a structure connecting visual and auditory brain areas. Group analyses also revealed greater cortical thickness correlation between visual and auditory areas in non-musicians (n = 28) compared to musicians (n = 17), possibly reflecting musicians' auditory specialization (FDR < 10%). Our results corroborate and expand current knowledge of functional specialization with a specific focus on audition, and highlight the fact that perception is essentially multimodal while uni-sensory processing is a specialized task.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".