Subspaces of Spatially Varying Independent Components in fMRI

Abstract. In contrast to the traditional hypothesis-driven methods, independent component analysis (ICA) is commonly used in functional magnetic resonance imaging (fMRI) studies to identify, in a blind manner, spatially independent elements of functional brain activity. ICA is particularly useful in studies with multi-modal stimuli or natural environments, where the brain responses are poorly predictable, and their individual elements may not be directly relatable to the given stimuli. This paper extends earlier work on analyzing the consistency of ICA estimates, by focusing on the spatial variability of the components, and presents a novel method for reliably identifying subspaces of functionally related independent components. Furthermore, two approaches are considered for refining the decomposition within the subspaces. Blind refinement is based on clustering all estimates in the subspace to reveal its internal structure. Guided refinement, incorporating the temporal dynamics of ...

• Type: Conference
• Year: 2007
• Where: ICA
• Authors: Jarkko Ylipaavalniemi, Ricardo Vigário
• Keywords: Functional Magnetic Resonance Imaging, ICA 2007, Independent Component Analysis, Independent Components, Signal Processing