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dc.contributor.authorKuang, Li-Dan
dc.contributor.authorLin, Qiu-Hua
dc.contributor.authorGong, Xiao-Feng
dc.contributor.authorCong, Fengyu
dc.contributor.authorSui, Jing
dc.contributor.authorCalhoun, Vince D.
dc.date.accessioned2018-04-26T09:04:52Z
dc.date.available2020-01-01T22:35:44Z
dc.date.issued2018
dc.identifier.citationKuang, L.-D., Lin, Q.-H., Gong, X.-F., Cong, F., Sui, J., & Calhoun, V. D. (2018). Model order effects on ICA of resting-state complex-valued fMRI data : application to schizophrenia. <em>Journal of Neuroscience Methods</em>, 304, 24-38. <a href="https://doi.org/10.1016/j.jneumeth.2018.02.013">doi:10.1016/j.jneumeth.2018.02.013</a>
dc.identifier.otherTUTKAID_77381
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/57769
dc.description.abstractBackground Component splitting at higher model orders is a widely accepted finding for independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data. However, our recent study found that intact components occurred with subcomponents at higher model orders. New method This study investigated model order effects on ICA of resting-state complex-valued fMRI data from 82 subjects, which included 40 healthy controls (HCs) and 42 schizophrenia patients. In addition, we explored underlying causes for distinct component splitting between complex-valued data and magnitude-only data by examining model order effects on ICA of phase fMRI data. A best run selection method was proposed to combine subject averaging and a one-sample t-test. We selected the default mode network (DMN)-, visual-, and sensorimotor-related components from the best run of ICA at varying model orders from 10 to 140. Results Results show that component integration occurred in complex-valued and phase analyses, whereas component splitting emerged in magnitude-only analysis with increasing model order. Incorporation of phase data appears to play a complementary role in preserving integrity of brain networks. Comparison with existing method(s) When compared with magnitude-only analysis, the intact DMN component obtained in complex-valued analysis at higher model orders exhibited highly significant subject-level differences between HCs and patients with schizophrenia. We detected significantly higher activity and variation in anterior areas for HCs and in posterior areas for patients with schizophrenia. Conclusions These results demonstrate the potential of complex-valued fMRI data to contribute generally and specifically to brain network analysis in identification of schizophrenia-related changes.
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherElsevier BV
dc.relation.ispartofseriesJournal of Neuroscience Methods
dc.subject.otherindependent component analysis (ICA)
dc.subject.othercomplex-valued fMRI data
dc.subject.othermodel order
dc.subject.othercomponent splitting
dc.subject.otherphase data
dc.subject.otherschizophrenia
dc.titleModel order effects on ICA of resting-state complex-valued fMRI data : application to schizophrenia
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-201804242345
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTietotekniikka
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.date.updated2018-04-24T12:15:05Z
dc.description.reviewstatuspeerReviewed
dc.format.pagerange24-38
dc.relation.issn0165-0270
dc.relation.numberinseries0
dc.relation.volume304
dc.type.versionacceptedVersion
dc.rights.copyright© 2018 Elsevier B.V. This is a final draft version of an article whose final and definitive form has been published by Elsevier B.V. Published in this repository with the kind permission of the publisher.
dc.rights.accesslevelopenAccessfi
dc.format.contentfulltext
dc.relation.doi10.1016/j.jneumeth.2018.02.013


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