Analysis of fMRI Data by Blind Separation into Independent Spatial Components
NAVAL HEALTH RESEARCH CENTER SAN DIEGO CA
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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 brain electrical signals. Here we describe a new method for analyzing fMRI data based on the independent component analysis ICA algorithm of Bell and Sejnowski. We decomposed eight fMRI data sets from 4 normal subjects performing various cognitive tasks. By utilizing higher-order statistics to enforce successively stricter criteria for spatial independence between component maps, both the ICA algorithm and related fourth-order decomposition technique were superior to principal component analysis PCA in determining the spatial and temporal extent of task-related activation. ICA appears to be a highly promising method for the analysis of fMRI data from normal and clinical populations.
- Medicine and Medical Research
- Numerical Mathematics