Conference paper
Independent vector analysis for capturing common components in fMRI group analysis
Department of Applied Mathematics and Computer Science, Technical University of Denmark1
Visual Computing, Department of Applied Mathematics and Computer Science, Technical University of Denmark2
Copenhagen University Hospital Herlev and Gentofte3
Cognitive Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark4
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.
Language: | English |
---|---|
Publisher: | IEEE |
Year: | 2016 |
Pages: | 1-4 |
Proceedings: | 6th International Workshop on Pattern Recognition in Neuroimaging |
ISBN: | 1467365300 , 1467365319 , 9781467365307 and 9781467365314 |
Types: | Conference paper |
DOI: | 10.1109/PRNI.2016.7552351 |
ORCIDs: | Mørup, Morten and Madsen, Kristoffer Hougaard |
Blind source separation Correlation Data models Gaussian distribution IVA Magnetic resonance imaging Mathematical model NIG Probability density function biomedical MRI blind source separation component representation gamma distribution independent component analysis independent vector analysis medical image processing multivariate Laplace distribution normal distribution normal-inverse-gamma distribution resting state functional magnetic resonance imaging rs-fMRI group analysis