Elsevier

NeuroImage

Volume 45, Issue 1, Supplement 1, March 2009, Pages S163-S172
NeuroImage

A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data

https://doi.org/10.1016/j.neuroimage.2008.10.057Get rights and content

Abstract

Independent component analysis (ICA) has become an increasingly utilized approach for analyzing brain imaging data. In contrast to the widely used general linear model (GLM) that requires the user to parameterize the data (e.g. the brain’s response to stimuli), ICA, by relying upon a general assumption of independence, allows the user to be agnostic regarding the exact form of the response. In addition, ICA is intrinsically a multivariate approach, and hence each component provides a grouping of brain activity into regions that share the same response pattern thus providing a natural measure of functional connectivity. There are a wide variety of ICA approaches that have been proposed, in this paper we focus upon two distinct methods. The first part of this paper reviews the use of ICA for making group inferences from fMRI data. We provide an overview of current approaches for utilizing ICA to make group inferences with a focus upon the group ICA approach implemented in the GIFT software. In the next part of this paper, we provide an overview of the use of ICA to combine or fuse multimodal data. ICA has proven particularly useful for data fusion of multiple tasks or data modalities such as single nucleotide polymorphism (SNP) data or event-related potentials. As demonstrated by a number of examples in this paper, ICA is a powerful and versatile data-driven approach for studying the brain.

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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