A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data
Section snippets
Introduction and background
Independent component analysis (ICA) is increasingly utilized as a tool for evaluating the hidden spatiotemporal structure contained within brain imaging data. In this paper, we first provide a brief overview of ICA and ICA as applied to functional magnetic resonance imaging (fMRI) data. Next, we discuss group ICA and ICA for data fusion with an emphasis upon the methods developed within our group and also discuss within a larger context of the many alternative approaches that are currently in
Theory and implementation
In this section, we review the methods behind group ICA, joint ICA, and parallel ICA.
Examples
In this section, we present examples of results from previous work using group ICA, joint ICA, and parallel ICA. The first example shows an analysis of a simulated driving paradigm, a case in which ICA is particularly useful as it is a naturalistic task that is difficult to parameterize for use in a traditional GLM analysis. fMRI data from 15 subjects were collected during a 10 min paradigm with alternating 1 min blocks of fixation, simulated driving, and watching (Calhoun et al., 2002). ICA
Summary
ICA is a powerful data driven approach that can be used to analyze group fMRI data or to analyze multimodal data including fMRI, ERP, and genetic data. The examples demonstrate the utility and diversity of ICA-based approaches for the analysis of brain imaging data.
Conflict of interest
The authors declare that there are no conflicts of interest.
Acknowledgments
This research was supported in part by the National Institutes of Health (NIH), under grants 1 R01 EB 000840, 1 R01 EB 005846, and 1 R01 EB 006841.
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