Independent vector analysis for capturing common components in fMRI group analysis

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Independent component analysis (ICA) is a widely used blind source separation method for decomposing resting state functional magnetic resonance imaging (rs-fMRI) data into latent components. However, it can be challenging to obtain subject-specific component representations in multi-subject studies. Independent vector analysis (IVA) is a promising alternative approach to perform group fMRI analysis, which has been shown to better capture components with high inter-subject variability. The most widely applied IVA method is based on the multivariate Laplace distribution (IVA-GL), which assumes independence within subject components coupled across subjects only through shared scaling. In this study, we propose a more natural formulation of IVA based on a Normal-Inverse-Gamma distribution (IVA-NIG), in which the components can be directly interpreted as realizations of a common mean component with individual subject variability. We evaluate the performance of IVA-NIG compared to IVA-GL and similar decomposition methods, through the application of two types of simulated data and on real task fMRI data. The results show that IVA-NIG offers superior detection of components in simulated fMRI data. On real fMRI data with low inter-subject variability we find that all methods identify similar and plausible components.
Original languageEnglish
Title of host publicationProceedings of the 6th International Workshop on Pattern Recognition in Neuroimaging (PRNI 2016)
Number of pages4
Publication date2016
ISBN (Print)978‐1‐4673‐6530‐7
Publication statusPublished - 2016
Event6th International Workshop on Pattern Recognition in Neuroimaging - Fondazione Bruno Kessler (FBK) Scientific and Technological Hub, Trento, Italy
Duration: 22 Jun 201624 Jun 2016
Conference number: 6


Workshop6th International Workshop on Pattern Recognition in Neuroimaging
LocationFondazione Bruno Kessler (FBK) Scientific and Technological Hub
Internet address
CitationsWeb of Science® Times Cited: No match on DOI

ID: 126822512