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.
Highlights
Participant-specific differences in the neural processing of music are a result of a multitude of demographical and background variables, musical expertise resulting from music instrument training has been identified as a major contributor
The brain regions selected by the decoder which individually best discriminated between musicians and nonmusicians were the bilateral anterior cingulate and paracingulate gyrus (ACG), the opercular part of the right inferior frontal gyrus, and the right superior temporal gyrus
We demonstrated the feasibility to decode musicianship class from how individual brains listen to real music, attaining a classification performance comparable to current state-of-the-art results in other classification tasks, including patients’ classifications
Summary
Participant-specific differences in the neural processing of music are a result of a multitude of demographical and background variables, musical expertise resulting from music instrument training has been identified as a major contributor. Past studies on the effect of musical training on the brain have utilized statistical analyses of functional or structural group differences between musicians and nonmusicians[16,17,18,19]. Utilizing temporal information in classification rather than directly using correlations between stimulus features and neuroimaging data enables one to dynamically take into account the temporally changing statistical uncertainty This has been found effective in past approaches on fMRI decoding[27,28]. Since we were conducting the analysis on healthy participants, we did not expect to reach as high accuracy levels than those obtained for neurological disorders having severe structural or functional effects on the brain, such as autism spectrum disease or Altzheimer’s disease. The decoding accuracy was expected to be driven mainly by high-level musical features, as neural processing of these features has been suggested to have high inter-participant variability[10] and are dependent upon rules learned in exposure to music[11]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
