IVA for multi-subject FMRI analysis: A comparative study using a new simulation toolbox

Joint blind source separation (JBSS) techniques have proven to be a natural solution for achieving source separation of multiple data sets. JBSS algorithms, such as independent vector analysis (IVA), are a promising alternative to independent component analysis (ICA) based approaches for the analysis of multi-subject functional magnetic resonance imaging (fMRI) data. Unlike ICA, little is known about the effectiveness of JBSS methods for fMRI analysis. In this paper, a new fMRI simulation toolbox (SimTB) is used to simulate multi-subject realistic fMRI datasets that include inter-subject variability. We study the performance of two JBSS algorithms representing two different approaches to the problem: (1) a recently proposed IVA algorithm combining second-order and higher-order statistics denoted by IVA-GL; and (2) a JBSS solution found by jointly diagonalizing cross-cumulant matrices denoted IVA-GJD. We compare these two JBSS algorithms with similar ICA algorithms implemented in the widely used group ICA for fMRI toolbox (GIFT). The results show that in addition to offering an effective solution for making group inferences, IVA algorithms provide superior performance in terms of capturing spatial inter-subject variability.