Comparing Multivariate Techniques for fMRI Data Analysis: A Simulation Study
نویسنده
چکیده
In fMRI data analysis, univariate techniques have been used to detect activation regions. In this study, we propose and compare an alternative approach, multivariate techniques, to extract meaningful activation patterns from fMRI data. When multivariate techniques such as PCA, rPCA, sICA, tICA, and FA were applied to the simulated fMRI-like data, only rPCA and FA extracted meaningful patterns in all conditions.
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تاریخ انتشار 2006