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

• Published in: Journal of neuroscience methods Vol. 247; pp. 32 - 40
• Main Authors: Laney, Jonathan, Westlake, Kelly P., Ma, Sai, Woytowicz, Elizabeth, Calhoun, Vince D., Adalı, Tülay
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 30.05.2015
• 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; Graph-theoretical analysis; GICA; fMRI; IVA; Stroke patient; Order selection

•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 usi...