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dc.contributor.authorSaari, Pasi
dc.contributor.authorBurunat, Iballa
dc.contributor.authorBrattico, Elvira
dc.contributor.authorToiviainen, Petri
dc.date.accessioned2018-01-22T08:10:41Z
dc.date.available2018-01-22T08:10:41Z
dc.date.issued2018
dc.identifier.citationSaari, P., Burunat, I., Brattico, E., & Toiviainen, P. (2018). Decoding Musical Training from Dynamic Processing of Musical Features in the Brain. <i>Scientific Reports</i>, <i>8</i>, Article 708. <a href="https://doi.org/10.1038/s41598-018-19177-5" target="_blank">https://doi.org/10.1038/s41598-018-19177-5</a>
dc.identifier.otherCONVID_27858213
dc.identifier.otherTUTKAID_76578
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/56835
dc.description.abstractPattern 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.
dc.language.isoeng
dc.publisherNature Publishing Group
dc.relation.ispartofseriesScientific Reports
dc.rightsCC BY 4.0
dc.subject.otherlearning algorithms
dc.subject.otherneural decoding
dc.titleDecoding Musical Training from Dynamic Processing of Musical Features in the Brain
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-201801181259
dc.contributor.laitosMusiikin, taiteen ja kulttuurin tutkimuksen laitosfi
dc.contributor.laitosDepartment of Music, Art and Culture Studiesen
dc.contributor.oppiaineMusiikkitiedefi
dc.contributor.oppiaineMusicologyen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.date.updated2018-01-18T13:15:09Z
dc.type.coarjournal article
dc.description.reviewstatuspeerReviewed
dc.relation.issn2045-2322
dc.relation.numberinseries0
dc.relation.volume8
dc.type.versionpublishedVersion
dc.rights.copyright© The Author(s) 2018.
dc.rights.accesslevelopenAccessfi
dc.relation.grantnumber272250
dc.subject.ysomusiikintutkimus
dc.subject.ysokuunteleminen
dc.subject.ysooppiminen
dc.subject.ysoaivot
dc.subject.ysoalgoritmit
dc.subject.ysoharjoittelu
jyx.subject.urihttp://www.yso.fi/onto/yso/p21685
jyx.subject.urihttp://www.yso.fi/onto/yso/p9106
jyx.subject.urihttp://www.yso.fi/onto/yso/p2945
jyx.subject.urihttp://www.yso.fi/onto/yso/p7040
jyx.subject.urihttp://www.yso.fi/onto/yso/p14524
jyx.subject.urihttp://www.yso.fi/onto/yso/p26412
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.1038/s41598-018-19177-5
dc.relation.funderSuomen Akatemiafi
dc.relation.funderAcademy of Finlanden
jyx.fundingprogramAkatemiaprofessorin tehtävä, SAfi
jyx.fundingprogramResearch post as Academy Professor, AoFen
jyx.fundinginformationThis work was supported by the Academy of Finland (project numbers 272250 and 274037) and the Danish National Research Foundation (project DNRF117).


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