Effects of music composition on structural and functional connectivity in the orbitofrontal cortex
Bibliographic record
Abstract
Aims: Composer is a classic case for investigating the plasticity effect of musical composition on the brain.As an essential brain region associated with prediction and decision, the orbitofrontal cortex exhibits the significant difference in many studies between composers and controls.Meanwhile, these studies also show that musical composition induces changes in cognitive networks, such as auditory and attentional functions.However, these structural and functional connectivity changes are often measured independently, making it crucial to exam the link between these two changes in order to further understand the neuroplasticity of musical composition.Methods: In this work, we recruited 18 composers and 20 controls under resting-state functional Magnetic Resonance Imaging (fMRI) scanning.First, based on the Tract-Based Spatial Statistics method, we found the differences in white matter skeleton between composers and the controls.Subsequently, we compared the differences in structural connectivity by probabilistic tracing from the orbitofrontal cortex.Finally, we examined the functional difference between groups.Results: We found that composers had higher anisotropy scores and mean diffusion rates in white matter regions such as the corpus callosum, anterior radiating corona, anterior and posterior branches of the internal capsule than the control group.Functional connectivity also provided evidence for the more robust relationship between the orbitofrontal cortex and other regions, such as attentional networks.
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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 | 0.001 |
| 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) | 0.003 | 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".