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

Neuroimage. 2009 Mar;45(1 Suppl):S163-72. doi: 10.1016/j.neuroimage.2008.10.057. Epub 2008 Nov 13.


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.

Publication types

  • Research Support, N.I.H., Extramural
  • Review

MeSH terms

  • Brain*
  • Evoked Potentials*
  • Humans
  • Image Processing, Computer-Assisted / methods*
  • Magnetic Resonance Imaging*
  • Polymorphism, Single Nucleotide*
  • Principal Component Analysis* / methods