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
Published inIEEE Transactions on Medical Imaging
Han, Yue |
DisciplineTietotekniikkaOhjelmisto- 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
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISSN Search the Publication Forum0278-0062
Publication in research information system
MetadataShow full item record
Additional information about fundingThis 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. ...
Showing items with similar title or keywords.
Extracting multi-mode ERP features using fifth-order nonnegative tensor decomposition Wang, Deqing; Zhu, Yongjie; Ristaniemi, Tapani; Cong, Fengyu (Elsevier BV, 2018)Background Preprocessed Event-related potential (ERP) data are usually organized in multi-way tensor, in which tensor decomposition serves as a powerful tool for data processing. Due to the limitation of computation burden ...
Shift-Invariant Canonical Polyadic Decomposition of Complex-Valued Multi-Subject fMRI Data with a Phase Sparsity Constraint Kuang, Li-Dan; Lin, Qiu-Hua; Gong, Xiao-Feng; Cong, Fengyu; Wang, Yu-Ping; Calhoun, Vince D. (IEEE, 2020)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 ...
Discovering dynamic task-modulated functional networks with specific spectral modes using MEG Zhu, Yongjie; Liu, Jia; Ye, Chaoxiong; Mathiak, Klaus; Astikainen, Piia; Ristaniemi, Tapani; Cong, Fengyu (Elsevier, 2020)Efficient neuronal communication between brain regions through oscillatory synchronization at certain frequencies is necessary for cognition. Such synchronized networks are transient and dynamic, established on the timescale ...
Tensor clustering on outer-product of coefficient and component matrices of independent component analysis for reliable functional magnetic resonance imaging data decomposition Hu, Guoqiang; Zhang, Qing; Waters, Abigail B.; Li, Huanjie; Zhang, Chi; Wu, Jianlin; Cong, Fengyu; Nickerson, Lisa D. (Elsevier BV, 2019)Background. Stability of spatial components is frequently used as a post-hoc selection criteria for choosing the dimensionality of an independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) ...
Multi-domain Features of the Non-phase-locked Component of Interest Extracted from ERP Data by Tensor Decomposition Zhang, Guanghui; Zhang, Chi; Cao, Shuo; Xia, Xue; Tan, Xin; Si, Lichengxi; Wang, Chenxin; Wang, Xiaochun; Zhou, Chenglin; Ristaniemi, Tapani; Cong, Fengyu (Springer, 2020)The waveform in the time domain, spectrum in the frequency domain, and topography in the space domain of component(s) of interest are the fundamental indices in neuroscience research. Despite the application of time–frequency ...