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dc.contributor.authorHan, Yue
dc.contributor.authorLin, Qiu-Hua
dc.contributor.authorKuang, Li-Dan
dc.contributor.authorGong, Xiao-Feng
dc.contributor.authorCong, Fengyu
dc.contributor.authorWang, Yu-Ping
dc.contributor.authorCalhoun, Vince D.
dc.date.accessioned2021-12-22T06:26:41Z
dc.date.available2021-12-22T06:26:41Z
dc.date.issued2022
dc.identifier.citationHan, Y., Lin, Q.-H., Kuang, L.-D., Gong, X.-F., Cong, F., Wang, Y.-P., & Calhoun, V. D. (2022). Low-Rank Tucker-2 Model for Multi-Subject fMRI Data Decomposition with Spatial Sparsity Constraint. <i>IEEE Transactions on Medical Imaging</i>, <i>41</i>(3), 667-679. <a href="https://doi.org/10.1109/TMI.2021.3122226" target="_blank">https://doi.org/10.1109/TMI.2021.3122226</a>
dc.identifier.otherCONVID_102952360
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/79124
dc.description.abstractTucker decomposition can provide an intuitive summary to understand brain function by decomposing multi-subject fMRI data into a core tensor and multiple factor matrices, and was mostly used to extract functional connectivity patterns across time/subjects using orthogonality constraints. However, these algorithms are unsuitable for extracting common spatial and temporal patterns across subjects due to distinct characteristics such as high-level noise. Motivated by a successful application of Tucker decomposition to image denoising and the intrinsic sparsity of spatial activations in fMRI, we propose a low-rank Tucker-2 model with spatial sparsity constraint to analyze multi-subject fMRI data. More precisely, we propose to impose a sparsity constraint on spatial maps by using an ℓp norm (0en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartofseriesIEEE Transactions on Medical Imaging
dc.rightsCC BY 4.0
dc.subject.otherbrain modeling
dc.subject.othercore tensor
dc.subject.otherdata models
dc.subject.otherfeature extraction
dc.subject.otherfunctional magnetic resonance imaging
dc.subject.otherlow-rank
dc.subject.othermatrix decomposition
dc.subject.othermulti-subject fMRI data
dc.subject.othersparse matrices
dc.subject.othersparsity constraint
dc.subject.othertensors
dc.subject.otherTucker decomposition
dc.titleLow-Rank Tucker-2 Model for Multi-Subject fMRI Data Decomposition with Spatial Sparsity Constraint
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202112226106
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineOhjelmisto- ja tietoliikennetekniikkafi
dc.contributor.oppiaineSecure Communications Engineering and Signal Processingfi
dc.contributor.oppiaineMathematical Information Technologyen
dc.contributor.oppiaineSoftware and Communications Engineeringen
dc.contributor.oppiaineSecure Communications Engineering and Signal Processingen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.description.reviewstatuspeerReviewed
dc.format.pagerange667-679
dc.relation.issn0278-0062
dc.relation.numberinseries3
dc.relation.volume41
dc.type.versionpublishedVersion
dc.rights.copyright© 2021 the Authors
dc.rights.accesslevelopenAccessfi
dc.subject.ysosignaalinkäsittely
dc.subject.ysotoiminnallinen magneettikuvaus
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p12266
jyx.subject.urihttp://www.yso.fi/onto/yso/p24211
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.1109/TMI.2021.3122226
jyx.fundinginformationThis work was supported in part by the National Natural Science Foundation of China under Grant 61871067, Grant 61379012, Grant 61901061, Grant 61671106, Grant 61331019, and Grant 81471742, in part by the NSF under Grant 1539067, Grant 0840895, Grant 1539067, and Grant 0715022, in part by the NIH Grant R01MH104680, Grant R01MH107354, Grant R01EB005846, and Grant 5P20GM103472, in part by the Fundamental Research Funds for the Central Universities, China, under Grant DUT20ZD220, and in part by the Supercomputing Center of Dalian University of Technology.


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