A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Shift-Invariant Canonical Polyadic Decomposition of Complex-Valued Multi-Subject fMRI Data with a Phase Sparsity Constraint (2020)

Kuang, L.-D., Lin, Q.-H., Gong, X.-F., Cong, F., Wang, Y.-P., & Calhoun, V. 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. https://doi.org/10.1109/TMI.2019.2936046

JYU-tekijät tai -toimittajat

Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Kuang, Li-Dan; Lin, Qiu-Hua; Gong, Xiao-Feng; Cong, Fengyu; Wang, Yu-Ping; Calhoun, Vince D.

Lehti tai sarja: IEEE Transactions on Medical Imaging

ISSN: 0278-0062

eISSN: 1558-254X

Julkaisuvuosi: 2020

Volyymi: 39

Lehden numero: 4

Artikkelin sivunumerot: 844-853

Kustantaja: IEEE

Julkaisumaa: Yhdysvallat (USA)

Julkaisun kieli: englanti

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

Julkaisun avoin saatavuus: Avoimesti saatavilla

Julkaisukanavan avoin saatavuus: Osittain avoin julkaisukanava

Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/67246

Tiivistelmä

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.

YSO-asiasanat: toiminnallinen magneettikuvaus; signaalianalyysi; signaalinkäsittely

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

Liittyvät organisaatiot

OKM-raportointi: Kyllä

Raportointivuosi: 2020

JUFO-taso: 2