A1 Journal article (refereed)
Shift-Invariant Canonical Polyadic Decomposition of Complex-Valued Multi-Subject fMRI Data with a Phase Sparsity Constraint (2020)


Kuang, Li-Dan; Lin, Qiu-Hua; Gong, Xiao-Feng; Cong, Fengyu; Wang, Yu-Ping; Calhoun, Vince D. (2020). Shift-Invariant Canonical Polyadic Decomposition of Complex-Valued Multi-Subject fMRI Data with a Phase Sparsity Constraint. IEEE Transactions on Medical Imaging, 39 (4), 844-853. DOI: 10.1109/TMI.2019.2936046


JYU authors or editors


Publication details

All authors or editors: Kuang, Li-Dan; Lin, Qiu-Hua; Gong, Xiao-Feng; Cong, Fengyu; Wang, Yu-Ping; Calhoun, Vince D.

Journal or series: IEEE Transactions on Medical Imaging

ISSN: 0278-0062

eISSN: 1558-254X

Publication year: 2020

Volume: 39

Issue number: 4

Pages range: 844-853

Publisher: IEEE

Publication country: United States

Publication language: English

DOI: http://doi.org/10.1109/TMI.2019.2936046

Open Access: Open access publication published in a hybrid channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/67246


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.


Keywords: functional magnetic resonance imaging; signal analysis; signal processing

Free keywords: canonical polyadic decomposition (CPD); complex-valued fMRI data; source phase sparsity; shift-invariant; spatiotemporal constraints


Contributing organizations


Ministry reporting: Yes

Reporting Year: 2020

Preliminary JUFO rating: 2


Last updated on 2020-18-08 at 13:28