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A multi-modal parcellation of human cerebral cortex

Nature volume 536, pages 171–178 (2016)Cite this article

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Abstract

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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Figure 1: Consistency of fine spatial details in independent group averages.
Figure 2: Parcellation of exemplar area 55b using multi-modal information.
Figure 3: The HCP’s multi-modal parcellation, version 1.0 (HCP_MMP1.0).
Figure 4: Example parcellated analyses using the HCP’s multi-modal cortical parcellation.
Figure 5: Areal detection rates, probabilistic areas, and parcellation reproducibility.

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Acknowledgements

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

  1. Timothy S. Coalson, Emma C. Robinson and Carl D. Hacker: These authors contributed equally to this work.

Authors and Affiliations

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

    Matthew F. Glasser, Timothy S. Coalson, John Harwell & David C. Van Essen

  2. Nuffield Department of Clinical Neurosciences, FMRIB Centre, 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, 63110, Missouri, USA

    Carl D. Hacker

  5. Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, 55455, Minnesota, 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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Contributions

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.

Corresponding authors

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

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Competing interests

The authors declare no competing financial interests.

Additional information

Reviewer Information

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

Supplementary information

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. (PDF 4565 kb)

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. (PDF 6176 kb)

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. (PDF 13162 kb)

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Glasser, M., Coalson, T., Robinson, E. et al. A multi-modal parcellation of human cerebral cortex. Nature 536, 171–178 (2016). https://doi.org/10.1038/nature18933

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