Independent vector analysis (IVA): Multivariate approach for fMRI group study

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

Research output: Contribution to journalArticle

100 Citations (Scopus)

Abstract

Independent component analysis (ICA) of fMRI data generates session/individual specific brain activation maps without a priori assumptions regarding the timing or pattern of the blood-oxygenation-level-dependent (BOLD) signal responses. However, because of a random permutation among output components, ICA does not offer a straightforward solution for the inference of group-level activation. In this study, we present an independent vector analysis (IVA) method to address the permutation problem during fMRI group data analysis. In comparison to ICA, IVA offers an analysis of additional dependent components, which were assigned for use in the automated grouping of dependent activation patterns across subjects. Upon testing using simulated trial-based fMRI data, our proposed method was applied to real fMRI data employing both a single-trial task-paradigm (right hand motor clenching and internal speech generation tasks) and a three-trial task-paradigm (right hand motor imagery task). A generalized linear model (GLM) and the group ICA of the fMRI toolbox (GIFT) were also applied to the same data set for comparison to IVA. Compared to GLM, IVA successfully captured activation patterns even when the functional areas showed variable hemodynamic responses that deviated from a hypothesized response. We also showed that IVA effectively inferred group-activation patterns of unknown origins without the requirement for a pre-processing stage (such as data concatenation in ICA-based GIFT). IVA can be used as a potential alternative or an adjunct to current ICA-based fMRI group processing methods.

Original languageEnglish
Pages (from-to)86-109
Number of pages24
JournalNeuroImage
Volume40
Issue number1
DOIs
Publication statusPublished - 2008 Mar 1
Externally publishedYes

Fingerprint

Multivariate Analysis
Magnetic Resonance Imaging
Linear Models
Hand
Imagery (Psychotherapy)
Hemodynamics
Brain

Keywords

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

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Neurology

Cite this

Independent vector analysis (IVA) : Multivariate approach for fMRI group study. / Lee, Jong-Hwan; Lee, Te W.; Jolesz, Ferenc A.; Yoo, Seung Schik.

In: NeuroImage, Vol. 40, No. 1, 01.03.2008, p. 86-109.

Research output: Contribution to journalArticle

Lee, Jong-Hwan ; Lee, Te W. ; Jolesz, Ferenc A. ; Yoo, Seung Schik. / Independent vector analysis (IVA) : Multivariate approach for fMRI group study. In: NeuroImage. 2008 ; Vol. 40, No. 1. pp. 86-109.
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