Using ICA for the Analysis of fMRI Data
Using ICA for the Analysis of fMRI Data
This chapter examines the most relevant aspects concerning the use of independent component analysis (ICA) for the analysis of functional magnetic resonance imaging (fMRI) data. In particular, after illustrating the fMRI-ICA model (“Problem formulation and application to fMRI”), the chapter compares the most commonly used ICA algorithms in the context of fMRI data analysis. The problems of choosing the dimensionality of the ICA decomposition, and of selecting the “meaningful” components, are considered. Optimizations of the ICA algorithms for dealing with the specific spatiotemporal properties of the fMRI data, and extensions of the ICA to multisubject fMRI studies, are described. For each of these aspects, different approaches from various groups are briefly reviewed.
Keywords: EEG, independent component analysis, functional magnetic resonance, fMRI
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