Spatially independent activity patterns in functional MRI data during the Stroop color-naming task

Abstract

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...

Group independent component analysis of language fMRI from word generation tasks

“…A practical challenge for the ICA technique is in the objective selection of the components that are of interest for a given application. Methods have been proposed for this purpose based on spatial, temporal, and spectral criteria (McKeown et al, 1998;Calhoun et al, 2001b;Moritz et al, 2003). In this study, we proposed a strategy for identifying components of interest based on a similarity analysis on the components' spatial maps across different language tasks.…”

“…ICA has been shown to be useful in the extraction of statistically independent features from fMRI data (McKeown et al, 1998;Calhoun et al, 2001a;Duann et al, 2002;Seifritz et al, 2002). In spatial ICA (sICA), as ICA is usually implemented for fMRI, the blood-oxygen-level dependent (BOLD) contrast image volume is assumed to be a linear mixture of spatially independent components which may originate from different sources.…”

Hybrid ICA–Bayesian network approach reveals distinct effective connectivity differences in schizophrenia

“…The key point here for our analysis is that ICA is capable of finding noise and movement related signal sources which are known to show characteristics of independence (McKeown et al, 1998). To determine which components were related to noise we correlated each ICA component spatial map with prior probabilistic maps of grey matter, white matter, and cerebral spinal fluid (CSF) within a standardized brain space provided by MNI templates in SPM5.…”

Joint independent component analysis for simultaneous EEG–fMRI: Principle and simulation

“…ICA models spatiotemporal data as a linear combination of maps and timecourses while attempting to maximize the independence between either the maps (spatial ICA, sICA) or the time courses (temporal ICA, tICA). ICA has been applied successfully to a variety of problems in auditory signal processing (Bell, et al, 1995), image processing , averaged ERPs (Makeig, et al, 1997), single trial EEG Onton, et al, 2006), fMRI (Biswal, et al, 1999;Calhoun, et al, 2001;McKeown, et al, 1998), and EEG-fMRI integration (Debener, et al, 2005;Eichele, et al, 2007;Feige, et al, 2005).…”