dc.contributor.author | Kuang, Li-Dan | |
dc.contributor.author | Lin, Qiu-Hua | |
dc.contributor.author | Gong, Xiao-Feng | |
dc.contributor.author | Cong, Fengyu | |
dc.contributor.author | Wang, Yu-Ping | |
dc.contributor.author | Calhoun, Vince D. | |
dc.date.accessioned | 2020-01-14T07:56:28Z | |
dc.date.available | 2020-01-14T07:56:28Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Kuang, L.-D., Lin, Q.-H., Gong, X.-F., Cong, F., Wang, Y.-P., & Calhoun, V. D. (2020). Shift-Invariant Canonical Polyadic Decomposition of Complex-Valued Multi-Subject fMRI Data with a Phase Sparsity Constraint. <i>IEEE Transactions on Medical Imaging</i>, <i>39</i>(4), 844-853. <a href="https://doi.org/10.1109/TMI.2019.2936046" target="_blank">https://doi.org/10.1109/TMI.2019.2936046</a> | |
dc.identifier.other | CONVID_32494170 | |
dc.identifier.uri | https://jyx.jyu.fi/handle/123456789/67246 | |
dc.description.abstract | Canonical polyadic decomposition (CPD) of multi-subject complex-valued fMRI data can be used to provide spatially and temporally shared components among groups with both magnitude and phase information. However, the CPD model is not well formulated due to the large subject variability in the spatial and temporal modalities, as well as the high noise level in complex-valued fMRI data. Considering that the shift-invariant CPD can model temporal variability across subjects, we propose to further impose a phase sparsity constraint on the shared spatial maps to denoise the complex-valued components and to model the inter-subject spatial variability as well. More precisely, subject-specific time delays are first estimated for the complex-valued shared time courses in the framework of real-valued shift-invariant CPD. Source phase sparsity is then imposed on the complex-valued shared spatial maps. A smoothed $\ell _{\mathbf {{0}}}$ norm is specifically used to reduce voxels with large phase values after phase de-ambiguity based on the small phase characteristic of BOLD-related voxels. The results from both the simulated and experimental fMRI data demonstrate improvements of the proposed method over three complex-valued algorithms, namely, tensor-based spatial ICA, shift-invariant CPD and CPD without spatiotemporal constraints. When comparing with a real-valued algorithm combining shift-invariant CPD and ICA, the proposed method detects 178.7% more contiguous task-related activations. | en |
dc.format.mimetype | application/pdf | |
dc.language.iso | eng | |
dc.publisher | IEEE | |
dc.relation.ispartofseries | IEEE Transactions on Medical Imaging | |
dc.rights | CC BY 4.0 | |
dc.subject.other | canonical polyadic decomposition (CPD) | |
dc.subject.other | complex-valued fMRI data | |
dc.subject.other | source phase sparsity | |
dc.subject.other | shift-invariant | |
dc.subject.other | spatiotemporal constraints | |
dc.title | Shift-Invariant Canonical Polyadic Decomposition of Complex-Valued Multi-Subject fMRI Data with a Phase Sparsity Constraint | |
dc.type | research article | |
dc.identifier.urn | URN:NBN:fi:jyu-202001141199 | |
dc.contributor.laitos | Informaatioteknologian tiedekunta | fi |
dc.contributor.laitos | Faculty of Information Technology | en |
dc.contributor.oppiaine | Tietotekniikka | fi |
dc.contributor.oppiaine | Mathematical Information Technology | en |
dc.type.uri | http://purl.org/eprint/type/JournalArticle | |
dc.type.coar | http://purl.org/coar/resource_type/c_2df8fbb1 | |
dc.description.reviewstatus | peerReviewed | |
dc.format.pagerange | 844-853 | |
dc.relation.issn | 0278-0062 | |
dc.relation.numberinseries | 4 | |
dc.relation.volume | 39 | |
dc.type.version | acceptedVersion | |
dc.rights.copyright | © 2020 The Authors | |
dc.rights.accesslevel | openAccess | fi |
dc.type.publication | article | |
dc.subject.yso | toiminnallinen magneettikuvaus | |
dc.subject.yso | signaalinkäsittely | |
dc.subject.yso | signaalianalyysi | |
dc.format.content | fulltext | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p24211 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p12266 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p26805 | |
dc.rights.url | http://rightsstatements.org/page/InC/1.0/?language=en | |
dc.rights.url | https://creativecommons.org/licenses/by/4.0/ | |
dc.relation.doi | 10.1109/TMI.2019.2936046 | |
jyx.fundinginformation | This 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 DUT14RC(3)037, and in part by the Supercomputing Center of Dalian University of Technology. | |
dc.type.okm | A1 | |