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dc.contributor.authorZhao, Wei
dc.contributor.authorLi, Huanjie
dc.contributor.authorHu, Guoqiang
dc.contributor.authorHao, Yuxing
dc.contributor.authorZhang, Qing
dc.contributor.authorWu, Jianlin
dc.contributor.authorFrederick, Blaise B.
dc.contributor.authorCong, Fengyu
dc.date.accessioned2021-02-01T08:32:16Z
dc.date.available2021-02-01T08:32:16Z
dc.date.issued2021
dc.identifier.citationZhao, W., Li, H., Hu, G., Hao, Y., Zhang, Q., Wu, J., Frederick, B. B., & Cong, F. (2021). Consistency of Independent Component Analysis for FMRI. <i>Journal of Neuroscience Methods</i>, <i>351</i>, Article 109013. <a href="https://doi.org/10.1016/j.jneumeth.2020.109013" target="_blank">https://doi.org/10.1016/j.jneumeth.2020.109013</a>
dc.identifier.otherCONVID_47413569
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/73899
dc.description.abstractBackground Independent component analysis (ICA) has been widely used for blind source separation in the field of medical imaging. However, despite of previous substantial efforts, the stability of ICA components remains a critical issue which has not been adequately addressed, despite numerous previous efforts. Most critical is the inconsistency of some of the extracted components when ICA is run with different model orders (MOs). New Method In this study, a novel method of determining the consistency of component analysis (CoCA) is proposed to evaluate the consistency of extracted components with different model orders. In the method, “consistent components” (CCs) are defined as those which can be extracted repeatably over a range of model orders. Result The efficacy of the method was evaluated with simulation data and fMRI datasets. With our method, the simulation result showed a clear difference of consistency between ground truths and noise. Comparison with existing methods The information criteria were implemented to provide suggestions for the optimal model order, where some of the ICs were revealed inconsistent in our proposed method. Conclusions This method provided an objective protocol for choosing CCs of an ICA decomposition of a data matrix, independent of model order. This is especially useful with high model orders, where noise or other disturbances could possibly lead to an instability of the components.en
dc.format.mimetypeapplication/pdf
dc.languageeng
dc.language.isoeng
dc.publisherElsevier BV
dc.relation.ispartofseriesJournal of Neuroscience Methods
dc.rightsCC BY-NC-ND 4.0
dc.subject.otherconsistency
dc.subject.othermodel order
dc.subject.otherICA
dc.subject.otherfMRI
dc.titleConsistency of Independent Component Analysis for FMRI
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202102011357
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.volume351
dc.type.versionacceptedVersion
dc.rights.copyright© 2020 Elsevier B.V.
dc.rights.accesslevelopenAccessfi
dc.subject.ysosignaalinkäsittely
dc.subject.ysotoiminnallinen magneettikuvaus
dc.subject.ysosignaalianalyysi
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p12266
jyx.subject.urihttp://www.yso.fi/onto/yso/p24211
jyx.subject.urihttp://www.yso.fi/onto/yso/p26805
dc.rights.urlhttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.relation.doi10.1016/j.jneumeth.2020.109013
jyx.fundinginformationThis work was supported by National Natural Science Foundation of China (Grant Nos. 91748105 & 81601484), and National Foundation in China (Nos. JCKY 2019110B009 & 2020-JCJQ-JJ-252), and the scholarships from China Scholarship Council (No. 202006060130), and the Fundamental Research Funds for the Central Universities [DUT2019, DUT20LAB303] in Dalian University of Technology in China.
dc.type.okmA1


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