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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.