Spatiotemporal exploratory analysis of fMRI data
نویسنده
چکیده
Introduction Most fMRI data display significant higher-order statistics representing tendencies to grouping along various shapes, even if such feature is commonly hidden by the overall distribution. Since the spatiotemporal characteristics of brain activity are frequently unknown and variable, their evaluation using hypothesis-driven methods only is rather difficult. Analysis of an fMRI block-based auditory stimulation paradigm was comparatively performed by stationary, noise free, linear spatial independent component analysis (sICA) [1] and temporally fuzzy cluster analysis (tFCA) [2].
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تاریخ انتشار 2009