Dataset Information

Decoding Musical Training from Dynamic Processing of Musical Features in the Brain.

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.

SUBMITTER: Saari P

PROVIDER: S-EPMC5768727 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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Decoding Musical Training from Dynamic Processing of Musical Features in the Brain.

Saari Pasi P Burunat Iballa I Brattico Elvira E Toiviainen Petri P

Scientific reports 20180115 1

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 music ...[more]

PMID: 29335643

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Project description:Cell signaling dynamics and transcriptional regulatory activities are variable within specific cell types responding to an identical stimulus. In addition to studying the network interactions, there is much interest in utilizing single cell scale data to elucidate the non-random aspects of the variability involved in cellular decision making. Previous studies have considered the information transfer between the signaling and transcriptional domains based on an instantaneous relationship between the molecular activities. These studies predict a limited binary on/off encoding mechanism which underestimates the complexity of biological information processing, and hence the utility of single cell resolution data. Here we pursue a novel strategy that reformulates the information transfer problem as involving dynamic features of signaling rather than molecular abundances. We pursue a computational approach to test if and how the transcriptional regulatory activity patterns can be informative of the temporal history of signaling. Our analysis reveals (1) the dynamic features of signaling that significantly alter transcriptional regulatory patterns (encoding), and (2) the temporal history of signaling that can be inferred from single cell scale snapshots of transcriptional activity (decoding). Immediate early gene expression patterns were informative of signaling peak retention kinetics, whereas transcription factor activity patterns were informative of activation and deactivation kinetics of signaling. Moreover, the information processing aspects varied across the network, with each component encoding a selective subset of the dynamic signaling features. We developed novel sensitivity and information transfer maps to unravel the dynamic multiplexing of signaling features at each of these network components. Unsupervised clustering of the maps revealed two groups that aligned with network motifs distinguished by transcriptional feedforward vs feedback interactions. Our new computational methodology impacts the single cell scale experiments by identifying downstream snapshot measures required for inferring specific dynamical features of upstream signals involved in the regulation of cellular responses.

Dataset Information

Decoding Musical Training from Dynamic Processing of Musical Features in the Brain.

Publications

Decoding Musical Training from Dynamic Processing of Musical Features in the Brain.

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