Analysis of fMRI Data by Blind Separation into Independent Spatial Components.

摘要

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.