State-dependent connectivity in auditory-reward networks predicts peak pleasure experiences to music.
PLoS Biol
; 22(8): e3002732, 2024 Aug.
Article
em En
| MEDLINE
| ID: mdl-39133721
ABSTRACT
Music can evoke pleasurable and rewarding experiences. Past studies that examined task-related brain activity revealed individual differences in musical reward sensitivity traits and linked them to interactions between the auditory and reward systems. However, state-dependent fluctuations in spontaneous neural activity in relation to music-driven rewarding experiences have not been studied. Here, we used functional MRI to examine whether the coupling of auditory-reward networks during a silent period immediately before music listening can predict the degree of musical rewarding experience of human participants (N = 49). We used machine learning models and showed that the functional connectivity between auditory and reward networks, but not others, could robustly predict subjective, physiological, and neurobiological aspects of the strong musical reward of chills. Specifically, the right auditory cortex-striatum/orbitofrontal connections predicted the reported duration of chills and the activation level of nucleus accumbens and insula, whereas the auditory-amygdala connection was associated with psychophysiological arousal. Furthermore, the predictive model derived from the first sample of individuals was generalized in an independent dataset using different music samples. The generalization was successful only for state-like, pre-listening functional connectivity but not for stable, intrinsic functional connectivity. The current study reveals the critical role of sensory-reward connectivity in pre-task brain state in modulating subsequent rewarding experience.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Recompensa
/
Percepção Auditiva
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Imageamento por Ressonância Magnética
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Prazer
/
Música
Limite:
Adult
/
Female
/
Humans
/
Male
Idioma:
En
Revista:
PLoS Biol
Assunto da revista:
BIOLOGIA
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Japão
País de publicação:
Estados Unidos