Independent component analysis (ICA) is a data-driven technique which has grown in it’s application to fMRI over the past 10 years. Unlike univariate methods ICA does not naturally generalize to a method suitable for drawing inferences about groups of subjects. For example, when using the general linear model, the investigator specifies the regressors of interest, and so drawing inferences about group data comes naturally, since all individuals in the group share the same regressors. In ICA, by contrast, different individuals in the group will have different time courses, and they will be sorted differently, so it is not immediately clear how to draw inferences about group data using ICA. Despite this, several ICA multi-subject analysis approaches have been proposed. The various approaches differ in terms of how the data is organized prior to the ICA analysis, what types of output are available (e.g. single subject contributions, group averages, etc), and how the statistical inference is made. In this talk we first provide an introduction to ICA and ICA of fMRI, then we provide an overview of current approaches for utilizing ICA to make group inferences and also show example of how group ICA can be utilized to make inferences from fMRI data.
Audio (MP3 File, Podcast Ready)
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