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Matthew F Glasser, Timothy S Coalson, Emma C Robinson, Carl D Hacker, John Harwell, Essa Yacoub, Kamil Ugurbil, Jesper Andersson, Christian F Beckmann, Mark Jenkinson, Stephen M Smith, and David C Van Essen
Understanding the amazingly complex human cerebral cortex requires a map (or parcellation) of its major subdivisions,
known as cortical areas. Making an accurate areal map has been a century-old objective in neuroscience. Using multimodal
magnetic resonance images from the Human Connectome Project (HCP) and an objective semi-automated
neuroanatomical approach, we delineated 180 areas per hemisphere bounded by sharp changes in cortical architecture,
function, connectivity, and/or topography in a precisely aligned group average of 210 healthy young adults. We
characterized 97 new areas and 83 areas previously reported using post-mortem microscopy or other specialized studyspecific
approaches. To enable automated delineation and identification of these areas in new HCP subjects and in future
studies, we trained a machine-learning classifier to recognize the multi-modal ‘fingerprint’ of each cortical area. This
classifier detected the presence of 96.6% of the cortical areas in new subjects, replicated the group parcellation, and
could correctly locate areas in individuals with atypical parcellations. The freely available parcellation and classifier
will enable substantially improved neuroanatomical precision for studies of the structural and functional organization
of human cerebral cortex and its variation across individuals and in development, aging, and disease.
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