Group Ica of Functional Mri Data: Separability, Stationarity, and Inference

Abstract

Independent component analysis (ICA) is being increasingly applied to functional MRI (fMRI) data. A principal advantage of this approach is its applicability to cognitive paradigms for which detailed a priori models of brain activity are not available. ICA has been successfully utilized to analyze singlesubject fMRI data sets, and we have recently extended this work to provide for group inferences. In order to perform group analysis, we concatenate the single-subject images in time and perform a single ICA estimation, then back-reconstruct individual subject maps and time courses. When applied to fMRI data acquired during a simple visual paradigm, our group ICA analysis revealed task-related components in left and right visual cortex, a transiently task-related component in bilateral occipital/parietal cortex, and a non task-related component in bilateral visual association cortex. In this work, we develop three important areas needed for applying ICA to group data: separability, stationarity, and inference. Our results further demonstrate the utility of using such a method for making group inferences on fMRI data using ICA.

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@inproceedings{Calhoun2001GroupIO, title={Group Ica of Functional Mri Data: Separability, Stationarity, and Inference}, author={V. D. Calhoun and Tulay Adali and Godfrey D. Pearlson and James J. Pekar}, year={2001} }