Low-Rank Tucker-2 Model for Multi-Subject fMRI Data Decomposition with Spatial Sparsity Constraint
Han, 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. IEEE Transactions on Medical Imaging, 41(3), 667-679. https://doi.org/10.1109/TMI.2021.3122226
Julkaistu sarjassaIEEE Transactions on Medical Imaging
Han, Yue |
OppiaineTietotekniikkaOhjelmisto- ja tietoliikennetekniikkaSecure Communications Engineering and Signal ProcessingMathematical Information TechnologySoftware and Communications EngineeringSecure Communications Engineering and Signal Processing
© 2021 the Authors
Tucker 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 (0
JulkaisijaInstitute of Electrical and Electronics Engineers (IEEE)
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Lisätietoja rahoituksestaThis 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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