ArticleScientific reports2018
Decoding Musical Training from Dynamic Processing of Musical Features in the Brain.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Expertise-Dependent Brain Network Organization During Music Perception.Human brain mapping · 2025Article
- Operatic voices engage the default mode network in professional opera singers.Scientific reports · 2024Article
- Breaking (musical) boundaries by investigating brain dynamics of event segmentation during real-life music-listening.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Increased functional connectivity in the right dorsal auditory stream after a full year of piano training in healthy older adults.Scientific reports · 2023Article
- Music can be reconstructed from human auditory cortex activity using nonlinear decoding models.PLoS biology · 2023Article
- Moderate associations betweenHeliyon · 2023Article
- Neocortical substrates of feelings evoked with music in the ACC, insula, and somatosensory cortex.Scientific reports · 2021Article
- Resting-State Functional Connectivity in Mathematical Expertise.Brain sciences · 2021Article
- Music-based biofeedback to reduce tibial shock in over-ground running: a proof-of-concept study.Scientific reports · 2021Article
- Neural Plasticity in a French Horn Player with Bilateral Amelia.Neural plasticity · 2021Article
- Development and Psychometric Validation of the Music Receptivity Scale.Frontiers in psychology · 2020Article
- On the Association Between Musical Training, Intelligence and Executive Functions in Adulthood.Frontiers in psychology · 2019Article
- Decoding everyday levels of musical training from subcortical white-matter architecture.Imaging neuroscience (Cambridge, Mass.)Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pattern recognition on neural activations from naturalistic music listening has been successful at predicting neural responses of listeners from musical features, and vice versa. Inter-subject differences in the decoding accuracies have arisen partly from musical training that has widely recognized structural and functional effects on the brain. We propose and evaluate a decoding approach aimed at predicting the musicianship class of an individual listener from dynamic neural processing of musical features. Whole brain functional magnetic resonance imaging (fMRI) data was acquired from musicians and nonmusicians during listening of three musical pieces from different genres. Six musical features, representing low-level (timbre) and high-level (rhythm and tonality) aspects of music perception, were computed from the acoustic signals, and classification into musicians and nonmusicians was performed on the musical feature and parcellated fMRI time series. Cross-validated classification accuracy reached 77% with nine regions, comprising frontal and temporal cortical regions, caudate nucleus, and cingulate gyrus. The processing of high-level musical features at right superior temporal gyrus was most influenced by listeners' musical training. The study demonstrates the feasibility to decode musicianship from how individual brains listen to music, attaining accuracy comparable to current results from automated clinical diagnosis of neurological and psychological disorders.
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