Capturing subject variability in fMRI data: A graph-theoretical analysis of GICA vs. IVA.

Journal of Neuroscience Methods
Jonathan LaneyTülay Adalı

Abstract

Recent studies using simulated functional magnetic resonance imaging (fMRI) data show that independent vector analysis (IVA) is a superior solution for capturing spatial subject variability when compared with the widely used group independent component analysis (GICA). Retaining such variability is of fundamental importance for identifying spatially localized group differences in intrinsic brain networks. Few studies on capturing subject variability and order selection have evaluated real fMRI data. Comparison of multivariate components generated by multiple algorithms is not straightforward. The main difficulties are finding concise methods to extract meaningful features and comparing multiple components despite lack of a ground truth. In this paper, we present a graph-theoretical (GT) approach to effectively compare the ability of multiple multivariate algorithms to capture subject variability for real fMRI data for effective group comparisons. The GT approach is applied to components generated from fMRI data, collected from individuals with stroke, before and after a rehabilitation intervention. IVA is compared with widely used GICA for the purpose of group discrimination in terms of GT features. In addition, masks are appli...Continue Reading

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