NDLI: Spatially independent activity patterns in functional MRI data during the stroop color-naming task.

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

Content Provider	World Health Organization (WHO)-Global Index Medicus
Author	Brown, G. Jung, T. P. Kindermann, S. S. Sejnowski, T. J. Lee, T. W. Mckeown, M. J. Makeig, S.
Description	Author Affiliation: McKeown MJ ( Howard Hughes Medical Institute, Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92186-5800, USA. martin@salk.edu);
Abstract	A method is given for determining the time course and spatial extent of consistently and transiently task-related 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 three-dimensional 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 higher-order 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.
ISSN	00278424
e-ISSN	10916490
Journal	Proceedings of the National Academy of Sciences of the United States of America
Issue Number	3
Volume Number	95
Language	English
Publisher	National Academy of Sciences
Publisher Date	1998-03-01
Publisher Place	United States
Access Restriction	Open
Subject Keyword	Brain Mapping Brain Anatomy & Histology Physiology Color Perception Tests Magnetic Resonance Imaging Statistics As Topic Algorithms Models, Neurological Psychomotor Performance Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. Multidisciplinary
Content Type	Text
Resource Type	Article
Subject	Multidisciplinary

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