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

• Martin J. McKeown

  Howard Hughes Medical Institute, Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92186-5800; Naval Health Research Center, P.O. Box 85122, San Diego, CA 92186-5122; and Departments of Neurosciences and Psychiatry, School of Medicine, and Department of Biology, University of California at San Diego, La Jolla, CA 92093

• Tzyy-Ping Jung

  Howard Hughes Medical Institute, Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92186-5800; Naval Health Research Center, P.O. Box 85122, San Diego, CA 92186-5122; and Departments of Neurosciences and Psychiatry, School of Medicine, and Department of Biology, University of California at San Diego, La Jolla, CA 92093

• Scott Makeig

  Howard Hughes Medical Institute, Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92186-5800; Naval Health Research Center, P.O. Box 85122, San Diego, CA 92186-5122; and Departments of Neurosciences and Psychiatry, School of Medicine, and Department of Biology, University of California at San Diego, La Jolla, CA 92093

• Greg Brown

  Howard Hughes Medical Institute, Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92186-5800; Naval Health Research Center, P.O. Box 85122, San Diego, CA 92186-5122; and Departments of Neurosciences and Psychiatry, School of Medicine, and Department of Biology, University of California at San Diego, La Jolla, CA 92093

• Sandra S. Kindermann

  Howard Hughes Medical Institute, Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92186-5800; Naval Health Research Center, P.O. Box 85122, San Diego, CA 92186-5122; and Departments of Neurosciences and Psychiatry, School of Medicine, and Department of Biology, University of California at San Diego, La Jolla, CA 92093

• Te-Won Lee

  Howard Hughes Medical Institute, Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92186-5800; Naval Health Research Center, P.O. Box 85122, San Diego, CA 92186-5122; and Departments of Neurosciences and Psychiatry, School of Medicine, and Department of Biology, University of California at San Diego, La Jolla, CA 92093

• Terrence J. Sejnowski

  Howard Hughes Medical Institute, Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92186-5800; Naval Health Research Center, P.O. Box 85122, San Diego, CA 92186-5122; and Departments of Neurosciences and Psychiatry, School of Medicine, and Department of Biology, University of California at San Diego, La Jolla, CA 92093

抄録

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.

収録刊行物

• Proceedings of the National Academy of Sciences

  Proceedings of the National Academy of Sciences 95 (3), 803-810, 1998-02-03

  Proceedings of the National Academy of Sciences

キーワード

• Multidisciplinary

詳細情報

• CRID: 1362544421256033408
• NII論文ID: 30016228020
• DOI: 10.1073/pnas.95.3.803
• ISSN: 10916490
• ISSN: 00278424
• Web Site: https://pnas.org/doi/pdf/10.1073/pnas.95.3.803
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