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

Li Dan Kuang, Qiu Hua Lin, Xiao Feng Gong, Jing Fan, Feng Yu Cong, Vince D. Calhoun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publication2013 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2013 - Proceedings
Pages269-272
Number of pages4
DOIs
StatePublished - Dec 11 2013
Externally publishedYes
Event2013 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2013 - Beijing, China
Duration: Jul 6 2013Jul 10 2013

Publication series

Name2013 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2013 - Proceedings

Other

Other2013 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2013
CountryChina
CityBeijing
Period7/6/137/10/13

Keywords

  • CP (CANDECOMP/PARAFAC)
  • fMRI
  • group ICA
  • shift-invariant CP (SCP)
  • tensor decomposition

ASJC Scopus subject areas

  • Information Systems
  • Signal Processing

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  • Cite this

    Kuang, L. D., Lin, Q. H., Gong, X. F., Fan, J., Cong, F. Y., & Calhoun, V. D. (2013). Multi-subject fMRI data analysis: Shift-invariant tensor factorization vs. group independent component analysis. In 2013 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2013 - Proceedings (pp. 269-272). [6625342] (2013 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2013 - Proceedings). https://doi.org/10.1109/ChinaSIP.2013.6625342