1994
DOI: 10.1097/00004728-199403000-00005
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Automatic 3D Intersubject Registration of MR Volumetric Data in Standardized Talairach Space

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Cited by 3,120 publications
(2,120 citation statements)
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“…Studies were included only if they investigated unmedicated healthy children or adults, used fMRI scanning (unless reported otherwise), and reported the coordinates of activations in the space of the MNI template [Collins et al, 1994] or the atlas of Talairach and Tournoux (1988). When activations were reported in MNI space they were transformed into Talairach and Tournoux coordinates, using a nonlinear transformation (http://imaging.mrc-cbu.cam.ac.…”
Section: Methodsmentioning
confidence: 99%
“…Studies were included only if they investigated unmedicated healthy children or adults, used fMRI scanning (unless reported otherwise), and reported the coordinates of activations in the space of the MNI template [Collins et al, 1994] or the atlas of Talairach and Tournoux (1988). When activations were reported in MNI space they were transformed into Talairach and Tournoux coordinates, using a nonlinear transformation (http://imaging.mrc-cbu.cam.ac.…”
Section: Methodsmentioning
confidence: 99%
“…These volumes were corrected for image intensity nonuniformity , and linearly and nonlinearly transformed into standardized stereotaxic space (Talairach and Tournoux, 1988) using automated feature matching to the MNI305 template (Collins et al, 1994b;Collins and Evans, 1997). Each individual's MRI was then coregistered to their summed radioactivity PET images (Evans et al, 1992).…”
Section: Methodsmentioning
confidence: 99%
“…The first step is to transform the native MRIs into a standardized stereotaxic coordinate space based on the Talairach atlas (Talairach and Tournoux, 1988). This procedure uses an automatic, multi-scale feature-matching algorithm (Collins et al, 1994) that performs a 9-parameter linear transformation to match each brain to a template brain. The images were corrected for nonuniformity artifacts (Sled et al, 1998).…”
Section: Automated Lesion Detectionmentioning
confidence: 99%