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

• Published in: NeuroImage (Orlando, Fla.) Vol. 40; no. 1; pp. 86 - 109
• Main Authors: Lee, Jong-Hwan, Lee, Te-Won, Jolesz, Ferenc A., Yoo, Seung-Schik
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.03.2008; Elsevier Limited
• Subjects: Algorithms; Brain; Brain - anatomy & histology; Cerebrovascular Circulation - physiology; Computer Simulation; fMRI; Generalized linear models; Group study; Humans; Image Processing, Computer-Assisted - statistics & numerical data; Imagination - physiology; Independent component analysis; Independent vector analysis; Individuality; Linear Models; Magnetic Resonance Imaging - statistics & numerical data; Methods; Models, Statistical; Multivariate Analysis; Principal Component Analysis; Principal components analysis; Psychomotor Performance - physiology; Speech - physiology; Time series; fMRI; Independent vector analysis; Multivariate analysis; Independent component analysis; Group study
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2007.11.019

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.