Large Sample Group Independent Component Analysis of Functional Magnetic Resonance Imaging Using Anatomical Atlas-Based Reduction and Bootstrapped Clustering

Abstract

Independent component analysis (ICA) is a popular method for the analysis of functional magnetic resonance imaging (fMRI) signals that is capable of revealing connected brain systems of functional significance. To be computationally tractable, estimating the independent components (ICs) inevitably requires one or more dimension reduction steps. Whereas most algorithms perform such reductions in the time domain, the input data are much more extensive in the spatial domain, and there is broad consensus that the brain obeys rules of localization of function into regions that are smaller in number than the number of voxels in a brain image. These functional units apparently reorganize dynamically into networks under different task conditions. Here we develop a new approach to ICA, producing group results by bagging and clustering over hundreds of pooled single-subject ICA results that have been projected to a lower-dimensional subspace. Averages of anatomically based regions are used to compress the single subject-ICA results prior to clustering and resampling via bagging. The computational advantages of this approach make it possible to perform group-level analyses on datasets consisting of hundreds of scan sessions by combining the results of within-subject analysis, while retaining the theoretical advantage of mimicking what is known of the functional organization of the brain. The result is a compact set of spatial activity patterns that are common and stable across scan sessions and across individuals. Such representations may be used in the context of statistical pattern recognition supporting real-time state classification.

Figure 1

Clusters discovered using ICA showing common components found in smokers who performed visual cue-induced craving, crave resist, and neutral control tasks while undergoing fMRI scanning. The maps represent in color the relative weighting of each of 110 brain regions expressed in the components. The amplitude values are expressed in normalized units. Areas of the brain containing principally myelinated axonal fibers (as opposed to neuronal cell bodies) are not shown in color and were not entered into the analyses. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]

Figure 2

Results using the standard univariate generalized linear modeling method to find areas of the brain active in smokers undergoing an fMRI scan. The results show the signal difference between a visual cue-induced craving task and a resting baseline. By convention, the color scale indicates z scores derived from t-statistics in the task contrast. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]