White Matter Microstructure Reflects Individual Differences in Music Reward Sensitivity
Bibliographic record
Abstract
People show considerable variability in the degree of pleasure they experience from music. These individual differences in music reward sensitivity are driven by variability in functional connectivity between the nucleus accumbens (NAcc), a key structure of the reward system, and the right superior temporal gyrus (STG). However, it is unknown whether a neuroanatomical basis exists for this variability. We used diffusion tensor imaging and probabilistic tractography to study the relationship between music reward sensitivity and white matter microstructure connecting these two regions via the orbitofrontal cortex (OFC) in 38 healthy human participants (24 females and 14 males). We found that right axial diffusivity (AD) in the STG–OFC connectivity inversely correlated with music reward sensitivity. Additionally, right mean diffusivity and left AD in the NAcc-OFC tract also showed an inverse correlation. Further, AD in this tract also correlated with previously acquired BOLD activity during music listening, but not for a control monetary reward task in the NAcc. Finally, we used mediation analysis to show that AD in the NAcc–OFC tract explains the influence of NAcc activation during a music task on music reward sensitivity. Overall, our results provide further support for the idea that the exchange of information among perceptual, integrative, and reward systems is important for musical pleasure, and that individual differences in the structure of the relevant anatomical connectivity influences the degree to which people are able to derive such pleasure. SIGNIFICANCE STATEMENT Music is one of the most important sources of pleasure for many people, but at the same time there are important individual differences in the sensitivity to musical reward. Previous studies have revealed the critical involvement of the functional connectivity between perceptual and subcortical brain areas in the enjoyment of music. However, it is unknown whether individual differences in music sensitivity might arise from variability in the structural connectivity among these areas. Here we show that structural connectivity between supratemporal and orbitofrontal cortices, and between orbitofrontal and nucleus accumbens, predict individual differences in sensibility to music reward. These results provide evidence for the critical involvement of the interaction between the subcortical reward system and higher-order cortical areas in music-induced pleasure.
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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) | 0.001 | 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".