This study presents a new method for analyzing functional magnetic resonance imaging (fMRI) data using independent component analysis (ICA). The method is based on the ICA algorithm developed by Bell and Sejnowski (1995). The researchers decomposed eight fMRI data sets from four normal subjects performing various tasks into spatially independent components. Each component consisted of voxel values at fixed three-dimensional locations and a unique associated time course of activation. The ICA algorithm extracted an equal number of spatially independent components from 144 time points collected during a 6-min trial. 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. The ICA algorithm and a related fourth-order decomposition technique were superior to principal component analysis (PCA) in determining the spatial and temporal extent of task-related activation. The time courses and active regions of the task-related ICA components were consistent across trials and 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.This study presents a new method for analyzing functional magnetic resonance imaging (fMRI) data using independent component analysis (ICA). The method is based on the ICA algorithm developed by Bell and Sejnowski (1995). The researchers decomposed eight fMRI data sets from four normal subjects performing various tasks into spatially independent components. Each component consisted of voxel values at fixed three-dimensional locations and a unique associated time course of activation. The ICA algorithm extracted an equal number of spatially independent components from 144 time points collected during a 6-min trial. 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. The ICA algorithm and a related fourth-order decomposition technique were superior to principal component analysis (PCA) in determining the spatial and temporal extent of task-related activation. The time courses and active regions of the task-related ICA components were consistent across trials and 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.