Journal
NEUROIMAGE
Volume 45, Issue 1, Pages S163-S172Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2008.10.057
Keywords
fMRI; SNP; ERP; Genetics; Independent component analysis
Funding
- Direct For Computer & Info Scie & Enginr
- Division of Computing and Communication Foundations [GRANTS:13775188] Funding Source: National Science Foundation
- Division of Computing and Communication Foundations
- Direct For Computer & Info Scie & Enginr [0840895] Funding Source: National Science Foundation
- NIBIB NIH HHS [R01 EB005846-02, R01 EB000840, R01 EB005846-03, R01 EB 006841, R01 EB006841, R01 EB000840-01, R01 EB000840-02, R01 EB005846-04, R01 EB000840-03, 1 R01 EB 000840, R01 EB000840-06, 1 R01 EB 005846, R01 EB000840-07, R01 EB000840-05, R01 EB006841-03, R01 EB006841-02, R01 EB006841-01A1S1, R01 EB000840-04, R01 EB005846-01, R01 EB005846, R01 EB006841-01A1, R01 EB020407] Funding Source: Medline
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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. (C) 2008 Elsevier Inc. All rights reserved.
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