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dc.contributor.authorHu, Guoqiang
dc.contributor.authorZhang, Qing
dc.contributor.authorWaters, Abigail B.
dc.contributor.authorLi, Huanjie
dc.contributor.authorZhang, Chi
dc.contributor.authorWu, Jianlin
dc.contributor.authorCong, Fengyu
dc.contributor.authorNickerson, Lisa D.
dc.date.accessioned2019-07-29T10:24:10Z
dc.date.available2019-07-29T10:24:10Z
dc.date.issued2019
dc.identifier.citationHu, G., Zhang, Q., Waters, A. B., Li, H., Zhang, C., Wu, J., Cong, F., & Nickerson, L. D. (2019). Tensor clustering on outer-product of coefficient and component matrices of independent component analysis for reliable functional magnetic resonance imaging data decomposition. <i>Journal of Neuroscience Methods</i>, <i>325</i>, Article 108359. <a href="https://doi.org/10.1016/j.jneumeth.2019.108359" target="_blank">https://doi.org/10.1016/j.jneumeth.2019.108359</a>
dc.identifier.otherCONVID_32130395
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/65140
dc.description.abstractBackground. Stability of spatial components is frequently used as a post-hoc selection criteria for choosing the dimensionality of an independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data. Although the stability of the ICA temporal courses differs from that of spatial components, temporal stability has not been considered during dimensionality decisions. New method. The current study aims to (1) develop an algorithm to incorporate temporal course stability into dimensionality selection and (2) test the impact of temporal course on the stability of the ICA decomposition of fMRI data via tensor clustering. Resting state fMRI data were analyzed with two popular ICA algorithms, InfomaxICA and FastICA, using our new method and results were compared with model order selection based on spatial or temporal criteria alone. Results. Hierarchical clustering indicated that the stability of the ICA decomposition incorporating spatiotemporal tensor information performed similarly when compared to current best practice. However, we found that component spatiotemporal stability and convergence of the model varied significantly with model order. Considering both may lead to methodological improvements for determining ICA model order. Selected components were also significantly associated with relevant behavioral variables. Comparison with Existing Method: The Kullback–Leibler information criterion algorithm suggests the optimal model order for group ICA is 40, compared to the proposed method with an optimal model order of 20. Conclusion. The current study sheds new light on the importance of temporal course variability in ICA of fMRI data.en
dc.format.mimetypeapplication/pdf
dc.languageeng
dc.language.isoeng
dc.publisherElsevier BV
dc.relation.ispartofseriesJournal of Neuroscience Methods
dc.rightsCC BY 4.0
dc.subject.otherindependent component analysis (ICA)
dc.subject.otherfMRI
dc.subject.othertensor clustering
dc.subject.otherstability
dc.subject.othermodel order
dc.titleTensor clustering on outer-product of coefficient and component matrices of independent component analysis for reliable functional magnetic resonance imaging data decomposition
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-201907293701
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineMathematical Information Technologyen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.relation.issn0165-0270
dc.relation.volume325
dc.type.versionpublishedVersion
dc.rights.copyright© 2019 The Authors.
dc.rights.accesslevelopenAccessfi
dc.subject.ysosignaalianalyysi
dc.subject.ysotoiminnallinen magneettikuvaus
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p26805
jyx.subject.urihttp://www.yso.fi/onto/yso/p24211
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.1016/j.jneumeth.2019.108359
jyx.fundinginformationThis work was supported by National Natural Science Foundation of China (Grant No. 81471742 & Grant No. 81371526) and the Fundamental Research Funds for the Central Universities [DUT16JJ(G)03] in Dalian University of Technology inChina.This work was also supported by China Scholarship Council(CSC).LDN was supported by the National Institutes of Health (PI:LDN,DA037265, AA024565).
dc.type.okmA1


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