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.

A method is given for determining the time course and spatial extent of consistently and transiently taskrelated activations from other physiological and artifactual components that contribute to functional MRI (fMRI) recordings. Independent component analysis (ICA) was used to analyze two fMRI data sets from a subject performing 6-min trials composed of alternating 40-sec Stroop color-naming and control task blocks. Each component consisted of a fixed threedimensional spatial distribution of brain voxel values (a ''map'') and an associated time course of activation. For each trial, the algorithm detected, without a priori knowledge of their spatial or temporal structure, one consistently task-related component activated during each Stroop task block, plus several transiently task-related components activated at the onset of one or two of the Stroop task blocks only. Activation patterns occurring during only part of the fMRI trial are not observed with other techniques, because their time courses cannot easily be known in advance. Other ICA components were related to physiological pulsations, head movements, or machine noise. By using higherorder statistics to specify stricter criteria for spatial independence between component maps, ICA produced improved estimates of the temporal and spatial extent of task-related activation in our data compared with principal component analysis (PCA). ICA appears to be a promising tool for exploratory analysis of fMRI data, particularly when the time courses of activation are not known in advance.Univariate methods for the analysis of functional MRI (fMRI) data typically examine each brain volume element or voxel individually, to determine whether the activity level at that voxel reaches a prespecified criterion for task-related activity. A common criterion is a predetermined level of significance for a statistic, such as the Student t (1) or KolmogorovSmirnov (2) statistic, under the null hypothesis that the distribution of a voxel's values during the behavioral control task is identical to that during performance of the experimental task(s). Correlational analysis (3) determines whether the similarity between a voxel's time course and a prediction of the task-related modulation, the reference function, exceeds a specified threshold. These methods then assemble individually selected (or ''active'') voxels, ignoring statistical relationships between voxels, to create a spatially distributed map demonstrating areas of significant activation.To enhance the statistical power of standard analysis techniques based on correlation or univariate statistical tests, fMRI experimenters often use alternating task-block designs in which the subject performs two or more tasks successively in alternating 20-to 40-sec blocks. By averaging over a number of task-block cycles, small consistently task-related (CTR) differences in hemodynamic activation can be detected. Isolated stimulus paradigms, such as that employed by Buckner et al. (4), avoid overlapping hemodynamic responses produc...

Working memory produces a larger neuronal response in some cortical regions than vigilance. Alcohol-dependent women showed less differential response to working memory than controls in frontal and parietal regions, especially in the right hemisphere. Heavy, chronic drinking appears to produce adverse neural effects that are detectable by functional magnetic resonance imaging.