Analysis of FMRI Data by Blind Separation into Independent Spatial Components

Howard Hughes Medical Institute, Salk Institute for Biological Studies, La Jolla, California 92186-5800, USA.
Human Brain Mapping (Impact Factor: 5.97). 01/1998; 6(3):160-88. DOI: 10.1002/(SICI)1097-0193(1998)6:3<160::AID-HBM5>3.0.CO;2-1
Source: PubMed


Current analytical techniques applied to functional magnetic resonance imaging (fMRI) data require a priori knowledge or specific assumptions about the time courses of processes contributing to the measured signals. Here we describe a new method for analyzing fMRI data based on the independent component analysis (ICA) algorithm of Bell and Sejnowski ([1995]: Neural Comput 7:1129-1159). We decomposed eight fMRI data sets from 4 normal subjects performing Stroop color-naming, the Brown and Peterson work/number task, and control tasks into spatially independent components. Each component consisted of voxel values at fixed three-dimensional locations (a component "map"), and a unique associated time course of activation. Given data from 144 time points collected during a 6-min trial, ICA extracted an equal number of spatially independent components. In all eight trials, ICA derived one and only one component with a time course closely matching the time course of 40-sec alternations between experimental and control tasks. The regions of maximum activity in these consistently task-related components generally overlapped active regions detected by standard correlational analysis, but included frontal regions not detected by correlation. Time courses of other ICA components were transiently task-related, quasiperiodic, or slowly varying. By utilizing higher-order statistics to enforce successively stricter criteria for spatial independence between component maps, both the ICA algorithm and a related fourth-order decomposition technique (Comon [1994]: Signal Processing 36:11-20) were superior to principal component analysis (PCA) in determining the spatial and temporal extent of task-related activation. For each subject, the time courses and active regions of the task-related ICA components were consistent across trials and were robust to the addition of simulated noise. Simulated movement artifact and simulated task-related activations added to actual fMRI data were clearly separated by the algorithm. ICA can be used to distinguish between nontask-related signal components, movements, and other artifacts, as well as consistently or transiently task-related fMRI activations, based on only weak assumptions about their spatial distributions and without a priori assumptions about their time courses. ICA appears to be a highly promising method for the analysis of fMRI data from normal and clinical populations, especially for uncovering unpredictable transient patterns of brain activity associated with performance of psychomotor tasks.

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    • "In multi-subject applications of ICA to fMRI data, typically one of two approaches is adopted [Calhoun et al., 2009]. The first approach applies ICA to each subject's data and establishes correspondence of ICs across subjects using subjective identification [Calhoun, 2001; McKeown et al., 1998], spatial matching with a predefined template [Greicius et al., 2004], clustering [Esposito et al., 2005; Moritz et al., 2003], or cross-correlation [Schopf et al., 2010]. However, sometimes it is difficult to effectively establish correspondence of functional networks across subjects due to that some identified networks from different subjects are not similar enough to match. "

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    • "Data driven approaches such as independent component analysis (ICA) (Hyvarinen, et al., 2001) applied to spontaneous activity produce a set of independent networks with a particular spatial distribution and a characteristic frequency power spectrum (Beckmann, et al., 2005; De Luca, et al., 2006; Esposito, et al., 2008; McKeown, et al., 1998; Perlbarg and Marrelec, 2008). "
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    • "Such statistical methods as principal component analysis (PCA), general linear models (GLM), and independent component analysis (ICA), have been developed to extract both spatial and temporal patterns of interest from functional signals, and to understand how different brain regions interact with each other. For instance, ICA has been widely used in single-subject fMRI/EEG studies to separate spatially or temporally independent components (McKeown et al. (1998); Beckmann and Smith (2004)). However, the extension of these methods to group inference is not straightforward due to striking neuroanatomic variations, and thus it remains an active research topic (Calhoun, Liu, and Adalı (2009)). "
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