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Analysis of fMRI Data by Blind Separation Into Independent Spatial Components (1998)
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| Venue: | HUMAN BRAIN MAPPING 6:160–188(1998) |
| Citations: | 317 - 18 self |
BibTeX
@MISC{Mckeown98analysisof,
author = {Martin J. Mckeown and Scott Makeig and Greg G. Brown and Tzyy-ping Jung and Sandra S. Kindermann and Anthony J. Bell and Terrence J. Sejnowski},
title = { Analysis of fMRI Data by Blind Separation Into Independent Spatial Components},
year = {1998}
}
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Abstract
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 word/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
Keyphrases
time course fmri data blind separation independent spatial component human brain mapping component map control task independent component stricter criterion 6-min trial current analytical technique new method equal number independent component analysis spatial independence fmri data set data require functional magnetic resonance imaging three-dimensional location peterson word number task 40-sec alternation priori knowledge higher-order statistic task-related component standard correlational analysis stroop color-naming neural comput maximum activity normal subject frontal region voxel value specific assumption active region ica component time point ica algorithm
