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

• Published in: Journal of neuroscience methods 2015-05, Vol.247, p.32-40
• Main Authors: Laney, Jonathan, Westlake, Kelly P., Ma, Sai, Woytowicz, Elizabeth, Calhoun, Vince D., Adalı, Tülay
• Format: Article
• Language: English
• Subjects: Data Interpretation, Statistical; fMRI; Functional Neuroimaging - methods; GICA; Graph-theoretical analysis; Humans; IVA; Magnetic Resonance Imaging; Models, Statistical; Order selection; Stroke - pathology; Stroke patient
• ISSN: 0165-0270; 1872-678X
• DOI: 10.1016/j.jneumeth.2015.03.019

•We show IVA better captures subject variability in real fMRI data.•Graph-theoretic features are used for comparison of algorithm performance.•We discuss the role of order selection for capturing subject variability.•Graph theory is applied to both spatial and temporal components. 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 applied for motor related components generated by both algorithms. Results show that IVA better captures subject variability producing more activated voxels and generating components with less mutual information in the spatial domain than Group ICA. IVA-generated components result in smaller p-values and clearer trends in GT features.