Constrained coupled CPD of complex-valued multi-slice multi-subject fMRI data

IF 3.4 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Li-Dan Kuang, Hao Zhu, Lei Long, Ting Tang, Yan Gui, Jin Zhang
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引用次数: 0

Abstract

Considering that the four-way complex-valued multi-subject fMRI tensor inevitably contains the large unwanted brain-out voxels, this paper innovatively combines multi-subject brain-in fMRI data with same slices as three-way multi-slice multi-subject fMRI tensors and thus brain-out voxels are discarded. Additionally, adjacent-slice fMRI tensors can be further merged, and multi-slice multi-subject fMRI tensors of N groups are formed, and are jointly decomposed by a novel spatiotemporally constrained coupled canonical polyadic decomposition (CCPD) by sharing temporal and subject modes but allowing slice-group differences. The spatial phase sparsity and orthonormality constraints are added on rank-R least-squares fit of N-slice-group shared spatial maps (SMs) to reduce noise effect, cross-talk among components and inter-subject spatial variability which naturally occur in complex-valued fMRI data. To alleviate CCPD model and allow inter-subject temporal variability, the alternating shift-invariant rank-1 least-squares optimization is performed to update shared time courses (TCs), subject-specific time delays and intensities. Results of simulated and experimental fMRI analyses demonstrate that the proposed methods with different N groups outperformed the competing methods by 6.21 %∼23.61 % in terms of shared task-related SMs and TCs. The proposed methods with N=45 and N=3 respectively obtain the best performance in the presence of strong noise levels and slightly strong noise levels cases.
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来源期刊
Signal Processing
Signal Processing 工程技术-工程:电子与电气
CiteScore
9.20
自引率
9.10%
发文量
309
审稿时长
41 days
期刊介绍: Signal Processing incorporates all aspects of the theory and practice of signal processing. It features original research work, tutorial and review articles, and accounts of practical developments. It is intended for a rapid dissemination of knowledge and experience to engineers and scientists working in the research, development or practical application of signal processing. Subject areas covered by the journal include: Signal Theory; Stochastic Processes; Detection and Estimation; Spectral Analysis; Filtering; Signal Processing Systems; Software Developments; Image Processing; Pattern Recognition; Optical Signal Processing; Digital Signal Processing; Multi-dimensional Signal Processing; Communication Signal Processing; Biomedical Signal Processing; Geophysical and Astrophysical Signal Processing; Earth Resources Signal Processing; Acoustic and Vibration Signal Processing; Data Processing; Remote Sensing; Signal Processing Technology; Radar Signal Processing; Sonar Signal Processing; Industrial Applications; New Applications.
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