Creation and use of a Talairach-compatible atlas for accurate, automated, nonlinear intersubject registration, and analysis of functional imaging data

Authors

  • Roger P. Woods,

    Corresponding author
    1. Division of Brain Mapping, Department of Neurology, Neuropsychiatric Institute, UCLA School of Medicine, Los Angeles, California
    • Ahmanson-Lovelace Brain Mapping Center, Division of Brain Mapping and Department of Neurology, Neuropsychiatric Institute, UCLA School of Medicine, Los Angeles, CA 90095-7085.
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  • Mirella Dapretto,

    1. Division of Brain Mapping, Department of Neurology, Neuropsychiatric Institute, UCLA School of Medicine, Los Angeles, California
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  • Nancy L. Sicotte,

    1. Division of Brain Mapping, Department of Neurology, Neuropsychiatric Institute, UCLA School of Medicine, Los Angeles, California
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  • Arthur W. Toga,

    1. Division of Brain Mapping, Department of Neurology, Neuropsychiatric Institute, UCLA School of Medicine, Los Angeles, California
    2. Laboratory of Neuro Imaging, Neuropsychiatric Institute, UCLA School of Medicine, Los Angeles, California
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  • John C. Mazziotta

    1. Division of Brain Mapping, Department of Neurology, Neuropsychiatric Institute, UCLA School of Medicine, Los Angeles, California
    2. Departments of Pharmacology and Radiology, Neuropsychiatric Institute, UCLA School of Medicine, Los Angeles, California
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Abstract

Spatial normalization in functional imaging can encompass various processes, including nonlinear warping to correct for intersubject differences, linear transformations to correct for identifiable head movements, and data detrending to remove residual motion correlated artifacts. We describe the use of AIR to create a custom, site-specific, normal averaged brain atlas that can be used to map T2 weighted echo-planar images and coplanar functional images directly into a Talairach-compatible space. We also discuss extraction of characteristic descriptors from sets of linear transformation matrices describing head movements in a functional imaging series. Scores for these descriptors, derived using principal components analysis with singular value decomposition, can be treated as confounds associated with each individual image in the series and systematically removed prior to voxel-by-voxel statistical analysis. Hum. Brain Mapping 8:73–79, 1999. © 1999 Wiley-Liss, Inc.

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