TY - JOUR
T1 - Multi-subject fMRI analysis via combined independent component analysis and shift-invariant canonical polyadic decomposition
AU - Kuang, Li Dan
AU - Lin, Qiu Hua
AU - Gong, Xiao Feng
AU - Cong, Fengyu
AU - Sui, Jing
AU - Calhoun, Vince D.
N1 - Publisher Copyright:
© 2015 Elsevier B.V.
PY - 2015/12/30
Y1 - 2015/12/30
N2 - Background: Canonical polyadic decomposition (CPD) may face a local optimal problem when analyzing multi-subject fMRI data with inter-subject variability. Beckmann and Smith proposed a tensor PICA approach that incorporated an independence constraint to the spatial modality by combining CPD with ICA, and alleviated the problem of inter-subject spatial map (SM) variability. New method: This study extends tensor PICA to incorporate additional inter-subject time course (TC) variability and to connect CPD and ICA in a new way. Assuming multiple subjects share common TCs but with different time delays, we accommodate subject-dependent TC delays into the CP model based on the idea of shift-invariant CP (SCP). We use ICA as an initialization step to provide the aggregating mixing matrix for shift-invariant CPD to estimate shared TCs with subject-dependent delays and intensities. We then estimate shared SMs using a least-squares fit post shift-invariant CPD. Results: Using simulated fMRI data as well as actual fMRI data we demonstrate that the proposed approach improves the estimates of the shared SMs and TCs, and the subject-dependent TC delays and intensities. The default mode component illustrates larger TC delays than the task-related component. Comparison with existing method(s): The proposed approach shows improvements over tensor PICA in particular when TC delays are large, and also outperforms SCP with SM orthogonality constraint and SCP with ICA-based SM initialization. Conclusions: TCs with subject-dependent delays conform to the true situation of multi-subject fMRI data. The proposed approach is suitable for decomposing multi-subject fMRI data with large inter-subject temporal and spatial variability.
AB - Background: Canonical polyadic decomposition (CPD) may face a local optimal problem when analyzing multi-subject fMRI data with inter-subject variability. Beckmann and Smith proposed a tensor PICA approach that incorporated an independence constraint to the spatial modality by combining CPD with ICA, and alleviated the problem of inter-subject spatial map (SM) variability. New method: This study extends tensor PICA to incorporate additional inter-subject time course (TC) variability and to connect CPD and ICA in a new way. Assuming multiple subjects share common TCs but with different time delays, we accommodate subject-dependent TC delays into the CP model based on the idea of shift-invariant CP (SCP). We use ICA as an initialization step to provide the aggregating mixing matrix for shift-invariant CPD to estimate shared TCs with subject-dependent delays and intensities. We then estimate shared SMs using a least-squares fit post shift-invariant CPD. Results: Using simulated fMRI data as well as actual fMRI data we demonstrate that the proposed approach improves the estimates of the shared SMs and TCs, and the subject-dependent TC delays and intensities. The default mode component illustrates larger TC delays than the task-related component. Comparison with existing method(s): The proposed approach shows improvements over tensor PICA in particular when TC delays are large, and also outperforms SCP with SM orthogonality constraint and SCP with ICA-based SM initialization. Conclusions: TCs with subject-dependent delays conform to the true situation of multi-subject fMRI data. The proposed approach is suitable for decomposing multi-subject fMRI data with large inter-subject temporal and spatial variability.
KW - Canonical polyadic decomposition (CPD)
KW - Independent component analysis (ICA)
KW - Inter-subject variability
KW - Multi-subject fMRI data
KW - Shift-invariant CP (SCP)
KW - Tensor PICA
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U2 - 10.1016/j.jneumeth.2015.08.023
DO - 10.1016/j.jneumeth.2015.08.023
M3 - Article
C2 - 26327319
AN - SCOPUS:84942084952
SN - 0165-0270
VL - 256
SP - 127
EP - 140
JO - Journal of Neuroscience Methods
JF - Journal of Neuroscience Methods
ER -