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dc.contributor.authorCong, Fengyu
dc.contributor.authorPuoliväli, Tuomas
dc.contributor.authorAlluri, Vinoo
dc.contributor.authorSipola, Tuomo
dc.contributor.authorBurunat, Iballa
dc.contributor.authorToiviainen, Petri
dc.contributor.authorNandi, Asoke
dc.contributor.authorBrattico, Elvira
dc.contributor.authorRistaniemi, Tapani
dc.date.accessioned2020-11-23T10:05:13Z
dc.date.available2020-11-23T10:05:13Z
dc.date.issued2014
dc.identifier.citationCong, F., Puoliväli, T., Alluri, V., Sipola, T., Burunat, I., Toiviainen, P., Nandi, A., Brattico, E., & Ristaniemi, T. (2014). Key issues in decomposing fMRI during naturalistic and continuous music experience with independent component analysis. <i>Journal of Neuroscience Methods</i>, <i>223</i>, 74-84. <a href="https://doi.org/10.1016/j.jneumeth.2013.11.025" target="_blank">https://doi.org/10.1016/j.jneumeth.2013.11.025</a>
dc.identifier.otherCONVID_23541101
dc.identifier.otherTUTKAID_60929
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/72727
dc.description.abstractBackground Independent component analysis (ICA) has been often used to decompose fMRI data mostly for the resting-state, block and event-related designs due to its outstanding advantage. For fMRI data during free-listening experiences, only a few exploratory studies applied ICA. New method For processing the fMRI data elicited by 512-s modern tango, a FFT based band-pass filter was used to further pre-process the fMRI data to remove sources of no interest and noise. Then, a fast model order selection method was applied to estimate the number of sources. Next, both individual ICA and group ICA were performed. Subsequently, ICA components whose temporal courses were significantly correlated with musical features were selected. Finally, for individual ICA, common components across majority of participants were found by diffusion map and spectral clustering. Results The extracted spatial maps (by the new ICA approach) common across most participants evidenced slightly right-lateralized activity within and surrounding the auditory cortices. Meanwhile, they were found associated with the musical features. Comparison with existing method(s) Compared with the conventional ICA approach, more participants were found to have the common spatial maps extracted by the new ICA approach. Conventional model order selection methods underestimated the true number of sources in the conventionally pre-processed fMRI data for the individual ICA. Conclusions Pre-processing the fMRI data by using a reasonable band-pass digital filter can greatly benefit the following model order selection and ICA with fMRI data by naturalistic paradigms. Diffusion map and spectral clustering are straightforward tools to find common ICA spatial maps.fi
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherElsevier
dc.relation.ispartofseriesJournal of Neuroscience Methods
dc.rightsCC BY-NC-ND 4.0
dc.subject.otherDiffusion map
dc.subject.otherFFT filter
dc.subject.otherfast model order selection
dc.subject.otherICA
dc.subject.otherreal-world experiences
dc.subject.otherfMRI
dc.titleKey issues in decomposing fMRI during naturalistic and continuous music experience with independent component analysis
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202011236714
dc.contributor.laitosMusiikin laitosfi
dc.contributor.laitosTietotekniikan laitosfi
dc.contributor.laitosDepartment of Musicen
dc.contributor.laitosDepartment of Mathematical Information Technologyen
dc.contributor.oppiaineMusiikkitiedefi
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineMusicologyen
dc.contributor.oppiaineMathematical Information Technologyen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.date.updated2020-11-23T07:15:08Z
dc.description.reviewstatuspeerReviewed
dc.format.pagerange74-84
dc.relation.issn0165-0270
dc.relation.numberinseries0
dc.relation.volume223
dc.type.versionacceptedVersion
dc.rights.copyright© 2014 Elsevier
dc.rights.accesslevelopenAccessfi
dc.format.contentfulltext
dc.rights.urlhttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.relation.doi10.1016/j.jneumeth.2013.11.025


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