Shift-Invariant Canonical Polyadic Decomposition of Complex-Valued Multi-Subject fMRI Data With a Phase Sparsity Constraint

• Published in: IEEE transactions on medical imaging Vol. 39; no. 4; pp. 844 - 853
• Main Authors: Kuang, Li-Dan, Lin, Qiu-Hua, Gong, Xiao-Feng, Cong, Fengyu, Wang, Yu-Ping, Calhoun, Vince D.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.04.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Brain - diagnostic imaging; Canonical polyadic decomposition (CPD); complex-valued fMRI data; Computer simulation; Data models; Decomposition; Delay effects; Frequency-domain analysis; Functional magnetic resonance imaging; Humans; Image Processing, Computer-Assisted - methods; Invariants; Load modeling; Magnetic Resonance Imaging - methods; Noise levels; Noise reduction; shift-invariant; Signal Processing, Computer-Assisted; source phase sparsity; Sparsity; spatiotemporal constraints; Spatiotemporal phenomena; Tensors; Timing

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...