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 multi-modal 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 study-specific 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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We thank the members of the WU-Minn-Ox HCP Consortium for invaluable contributions to data acquisition, analysis, and sharing and E. Reid and S. Danker for assistance with preparing the manuscript. Supported by NIH F30 MH097312 (M.F.G.), ROIMH-60974 (D.C.V.E.), NIH F30 MH099877 (C.D.H.), the Human Connectome Project grant (1U54MH091657) from the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research, and the Wellcome Trust Strategic Award 098369/Z/12/Z (S.M.S., J.A., C.F.B., M.J.).

Author information

Author notes

    • Timothy S. Coalson
    • , Emma C. Robinson
    •  & Carl D. Hacker

    These authors contributed equally to this work.


  1. Department of Neuroscience, Washington University Medical School, Saint Louis, Missouri 63110, USA

    • Matthew F. Glasser
    • , Timothy S. Coalson
    • , John Harwell
    •  & David C. Van Essen
  2. FMRIB Centre, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, UK

    • Emma C. Robinson
    • , Jesper Andersson
    • , Mark Jenkinson
    •  & Stephen M. Smith
  3. Department of Computing, Imperial College, London SW7 2AZ, UK

    • Emma C. Robinson
  4. Department of Biomedical Engineering, Washington University, Saint Louis, Missouri 63110, USA

    • Carl D. Hacker
  5. Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, Minnesota 55455, USA

    • Essa Yacoub
    •  & Kamil Ugurbil
  6. Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen 6525 EN, The Netherlands

    • Christian F. Beckmann
  7. Department of Cognitive Neuroscience, Radboud University Medical Centre Nijmegen, Postbus 9101, Nijmegen 6500 HB, The Netherlands

    • Christian F. Beckmann


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M.F.G. and D.C.V.E. designed the study and carried out the analyses. M.F.G., T.S.C., E.C.R., C.D.H., J.H., E.Y., K.U., J.A., C.F.B., M.J., and S.M.S. contributed novel methods. M.F.G., T.S.C., E.C.R., C.D.H., E.Y., J.A., C.F.B., M.J., S.M.S., and D.C.V.E. wrote the paper.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Matthew F. Glasser or David C. Van Essen.

Reviewer Information

Nature thanks R. Poldrack, F. Tong and T. Yeo for their contribution to the peer review of this work.

Supplementary information

PDF files

  1. 1.

    Supplementary Methods

    This file explains the experimental methods in detail. This information will be of particular interest for methods oriented neuroimaging scientists interested in exactly what was done and why. It contains 11 Supplementary figures.

  2. 2.

    Supplementary Results and Discussion

    This file contains 12 supplementary figures and supplementary text expanding on the reproducibility of the data used to generate the parcellation, cross validation of parcellation, an exploration of atypical parcellations in individual subjects, a peek inside the areal classifier, and a discussion.

  3. 3.

    Supplementary Neuroanatomical Results

    This file provides a detailed neuroanatomical description of how each border between each pair of cortical areas was delineated and how each area was identified and named. This information will be of particular interest to neuroanatomists. It contains 25 figures and 3 tables.

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