ArticleIEEE transactions on medical imaging2020
Shift-Invariant Canonical Polyadic Decomposition of Complex-Valued Multi-Subject fMRI Data With a Phase Sparsity Constraint.
Article in IEEE transactions on medical imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Denoising brain networks using a fixed mathematical phase change in independent component analysis of magnitude-only fMRI data.Human brain mapping · 2023Article
- Multi-Subject Analysis for Brain Developmental Patterns Discovery via Tensor Decomposition of MEG Data.Neuroinformatics · 2023Article
- Discovering hidden brain network responses to naturalistic stimuli via tensor component analysis of multi-subject fMRI data.NeuroImage · 2022Article
- Low-Rank Tucker-2 Model for Multi-Subject fMRI Data Decomposition With Spatial Sparsity Constraint.IEEE transactions on medical imaging · 2022Article
- Three-way parallel group independent component analysis: Fusion of spatial and spatiotemporal magnetic resonance imaging data.Human brain mapping · 2022Article
- Constrained CPD of Complex-Valued Multi-Subject fMRI Data via Alternating Rank-R and Rank-1 Least Squares.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2022Article
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6 authors.
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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 l
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