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 the stimulation, finds particular projections that maximally correlate with the stimuli.
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Ylipaavalniemi, J., Vigário, R. (2007). Subspaces of Spatially Varying Independent Components in fMRI. In: Davies, M.E., James, C.J., Abdallah, S.A., Plumbley, M.D. (eds) Independent Component Analysis and Signal Separation. ICA 2007. Lecture Notes in Computer Science, vol 4666. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74494-8_83
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DOI: https://doi.org/10.1007/978-3-540-74494-8_83
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