SPATIALLY INDEPENDENT ACTIVITY PATTERNS IN FUNCTIONAL MRI DATA DURINGTHE STROOP COLOR-NAMING TASK

A method is given for determining the time course and spatial extent o f consistently and transiently task-related activations from other phy siological and artifactual components that contribute to functional MR I (fMRI) recordings. Independent component analysis (ICA) was used to analyze two fMRI data sets from a subject performing 6-min trials comp osed of alternating 40-sec Stroop color-naming and control task blocks , Each component consisted of a fixed three-dimensional spatial distri bution of brain voxel values (a ''map'') and an associated time course of activation, For each trial, the algorithm detected, without a prio ri knowledge of their spatial or temporal structure, one consistently task-related component activated during each Stroop task block, plus s everal transiently task-related components activated at the onset of o ne or two of the Stroop task blocks only, Activation patterns occurrin g during only part of the fMRI trial are not observed with other techn iques, because their time courses cannot easily be known in advance, O ther ICA components were related to physiological pulsations, head mov ements, or machine noise, By using higher-order statistics to specify stricter criteria for spatial independence between component maps, ICA produced improved estimates of the temporal and spatial extent of tas k-related activation in our data compared with principal component ana lysis (PCA), ICA appears to be a promising tool for exploratory analys is of fMRI data, particularly when the time courses of activation are not known in advance.