Multivariate analysis of fMRI group data using independent vector analysis

Jong Hwan Lee, Te Won Lee, Ferenc A. Jolesz, Seung Schik Yoo

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

4 Citations (Scopus)

Abstract

A multivariate non-parametric approach for the processing of fMRI group data is important to address variability of hemodynamic responses across subjects, sessions, and brain regions. Independent component analysis (ICA) has a limitation during the inference of group effects due to a permutation problem of independent components. In order to address this limitation, we present an independent vector analysis (IVA) for the processing of fMRI group data. Compared to the ICA, the IVA offers an extra dimension for the dependent parameters, which can be assigned for the automated grouping of dependent activation patterns across subjects. The IVA was applied to the fMRI data obtained from 12 subjects performing a left-hand motor task. In comparison with conventional univariate methods, IVA successfully characterized the group-representative activation time courses (as component vectors) without extra data processing schemes to circumvent the permutation problem, while effectively detecting the areas with hemodynamic responses deviating from canonical, model-driven ones.

Original languageEnglish
Title of host publicationIndependent Component Analysis and Signal Separation - 7th International Conference, ICA 2007, Proceedings
PublisherSpringer Verlag
Pages633-640
Number of pages8
ISBN (Print)9783540744931
DOIs
Publication statusPublished - 2007
Event7th International Conference on Independent Component Analysis (ICA) and Source Separation, ICA 2007 - London, United Kingdom
Duration: 2007 Sep 92007 Sep 12

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4666 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other7th International Conference on Independent Component Analysis (ICA) and Source Separation, ICA 2007
CountryUnited Kingdom
CityLondon
Period07/9/907/9/12

Keywords

  • Group study
  • Independent vector analysis
  • Multivariate analysis
  • Neuroimaging
  • fMRI

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Lee, J. H., Lee, T. W., Jolesz, F. A., & Yoo, S. S. (2007). Multivariate analysis of fMRI group data using independent vector analysis. In Independent Component Analysis and Signal Separation - 7th International Conference, ICA 2007, Proceedings (pp. 633-640). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4666 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-540-74494-8_79