Multi-subject fMRI data analysis: Shift-invariant tensor factorization vs. group independent component analysis

Tensor decomposition of fMRI data has gradually drawn attention since it can explore the multi-way data´s structure which exists inherently in brain imaging. For multi-subject fMRI data analysis, time shifts occur inevitably among different participants, therefore, shift-invariant tensor decomposition should be used. This method allows for arbitrary shifts along one modality, and can yield satisfactory results for analyzing multi-set fMRI data with time shifts of different datasets. In this study, we presented the first application of shift-invariant tensor decomposition to simulated multi-subject fMRI data with shifts of time courses and variations of spatial maps. By this method, time shifts, spatial maps, time courses, and subjects´ amplitudes were better estimated in contrast to group independent component analysis. Therefore, shift-invariant tensor decomposition is promising for real multi-set fMRI data analysis.