The Human Connectome Project's neuroimaging approach
This paper describes an integrated approach for neuroimaging data acquisition, analysis and sharing. Building on methodological advances from the Human Connectome Project (HCP) and elsewhere, the HCP-style paradigm applies to new and existing data sets that meet core requirements and may accelerate...
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| Published in | Nature neuroscience Vol. 19; no. 9; pp. 1175 - 1187 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Nature Publishing Group US
01.09.2016
Nature Publishing Group |
| Subjects | |
| Online Access | Get full text |
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| Abstract | This paper describes an integrated approach for neuroimaging data acquisition, analysis and sharing. Building on methodological advances from the Human Connectome Project (HCP) and elsewhere, the HCP-style paradigm applies to new and existing data sets that meet core requirements and may accelerate progress in understanding the brain in health and disease.
Noninvasive human neuroimaging has yielded many discoveries about the brain. Numerous methodological advances have also occurred, though inertia has slowed their adoption. This paper presents an integrated approach to data acquisition, analysis and sharing that builds upon recent advances, particularly from the Human Connectome Project (HCP). The 'HCP-style' paradigm has seven core tenets: (i) collect multimodal imaging data from many subjects; (ii) acquire data at high spatial and temporal resolution; (iii) preprocess data to minimize distortions, blurring and temporal artifacts; (iv) represent data using the natural geometry of cortical and subcortical structures; (v) accurately align corresponding brain areas across subjects and studies; (vi) analyze data using neurobiologically accurate brain parcellations; and (vii) share published data via user-friendly databases. We illustrate the HCP-style paradigm using existing HCP data sets and provide guidance for future research. Widespread adoption of this paradigm should accelerate progress in understanding the brain in health and disease. |
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| AbstractList | Non-invasive human neuroimaging has yielded many exciting discoveries about the brain. Numerous methodological advances have also occurred, though inertia has slowed their adoption. This paper presents an integrated approach to data acquisition, analysis, and sharing that builds upon recent advances, particularly from the Human Connectome Project (HCP). The “HCP-style” paradigm has seven core tenets: (1) collect multimodal imaging data from many subjects; (2) acquire data at high spatial and temporal resolution; (3) preprocess data to minimize distortions, blurring, and temporal artifacts; (4) represent data using the natural geometry of cortical and subcortical structures; (5) accurately align corresponding brain areas across subjects and studies; (6) analyze data using neurobiologically accurate brain parcellations; and (7) share published data via user-friendly databases. We illustrate the HCP-style paradigm using existing HCP datasets and provide guidance for future research. Widespread adoption of this paradigm should accelerate progress in understanding the brain in health and disease. Noninvasive human neuroimaging has yielded many discoveries about the brain. Numerous methodological advances have also occurred, though inertia has slowed their adoption. This paper presents an integrated approach to data acquisition, analysis and sharing that builds upon recent advances, particularly from the Human Connectome Project (HCP). The 'HCP-style' paradigm has seven core tenets: (i) collect multimodal imaging data from many subjects; (ii) acquire data at high spatial and temporal resolution; (iii) preprocess data to minimize distortions, blurring and temporal artifacts; (iv) represent data using the natural geometry of cortical and subcortical structures; (v) accurately align corresponding brain areas across subjects and studies; (vi) analyze data using neurobiologically accurate brain parcellations; and (vii) share published data via user-friendly databases. We illustrate the HCP-style paradigm using existing HCP data sets and provide guidance for future research. Widespread adoption of this paradigm should accelerate progress in understanding the brain in health and disease. Noninvasive human neuroimaging has yielded many discoveries about the brain. Numerous methodological advances have also occurred, though inertia has slowed their adoption. This paper presents an integrated approach to data acquisition, analysis and sharing that builds upon recent advances, particularly from the Human Connectome Project (HCP). The 'HCP-style' paradigm has seven core tenets: (i) collect multimodal imaging data from many subjects; (ii) acquire data at high spatial and temporal resolution; (iii) preprocess data to minimize distortions, blurring and temporal artifacts; (iv) represent data using the natural geometry of cortical and subcortical structures; (v) accurately align corresponding brain areas across subjects and studies; (vi) analyze data using neurobiologically accurate brain parcellations; and (vii) share published data via user-friendly databases. We illustrate the HCP-style paradigm using existing HCP data sets and provide guidance for future research. Widespread adoption of this paradigm should accelerate progress in understanding the brain in health and disease.Noninvasive human neuroimaging has yielded many discoveries about the brain. Numerous methodological advances have also occurred, though inertia has slowed their adoption. This paper presents an integrated approach to data acquisition, analysis and sharing that builds upon recent advances, particularly from the Human Connectome Project (HCP). The 'HCP-style' paradigm has seven core tenets: (i) collect multimodal imaging data from many subjects; (ii) acquire data at high spatial and temporal resolution; (iii) preprocess data to minimize distortions, blurring and temporal artifacts; (iv) represent data using the natural geometry of cortical and subcortical structures; (v) accurately align corresponding brain areas across subjects and studies; (vi) analyze data using neurobiologically accurate brain parcellations; and (vii) share published data via user-friendly databases. We illustrate the HCP-style paradigm using existing HCP data sets and provide guidance for future research. Widespread adoption of this paradigm should accelerate progress in understanding the brain in health and disease. This paper describes an integrated approach for neuroimaging data acquisition, analysis and sharing. Building on methodological advances from the Human Connectome Project (HCP) and elsewhere, the HCP-style paradigm applies to new and existing data sets that meet core requirements and may accelerate progress in understanding the brain in health and disease. Noninvasive human neuroimaging has yielded many discoveries about the brain. Numerous methodological advances have also occurred, though inertia has slowed their adoption. This paper presents an integrated approach to data acquisition, analysis and sharing that builds upon recent advances, particularly from the Human Connectome Project (HCP). The 'HCP-style' paradigm has seven core tenets: (i) collect multimodal imaging data from many subjects; (ii) acquire data at high spatial and temporal resolution; (iii) preprocess data to minimize distortions, blurring and temporal artifacts; (iv) represent data using the natural geometry of cortical and subcortical structures; (v) accurately align corresponding brain areas across subjects and studies; (vi) analyze data using neurobiologically accurate brain parcellations; and (vii) share published data via user-friendly databases. We illustrate the HCP-style paradigm using existing HCP data sets and provide guidance for future research. Widespread adoption of this paradigm should accelerate progress in understanding the brain in health and disease. |
| Audience | Academic |
| Author | Andersson, Jesper L R Moeller, Steen Robinson, Emma C Van Essen, David C Marcus, Daniel S Smith, Stephen M Behrens, Timothy E J Coalson, Timothy S Sotiropoulos, Stamatios N Yacoub, Essa Jenkinson, Mark Glasser, Matthew F Xu, Junqian Harms, Michael P Ugurbil, Kamil Auerbach, Edward J |
| AuthorAffiliation | 4 Department of Psychiatry, Washington University Medical School, Saint Louis, MO 5 Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA 1 Department of Neuroscience, Washington University Medical School, Saint Louis, MO, USA 2 FMRIB Centre, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK 3 Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA |
| AuthorAffiliation_xml | – name: 3 Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA – name: 1 Department of Neuroscience, Washington University Medical School, Saint Louis, MO, USA – name: 2 FMRIB Centre, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK – name: 5 Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA – name: 4 Department of Psychiatry, Washington University Medical School, Saint Louis, MO |
| Author_xml | – sequence: 1 givenname: Matthew F surname: Glasser fullname: Glasser, Matthew F email: glasserm@wustl.edu organization: Department of Neuroscience, Washington University Medical School – sequence: 2 givenname: Stephen M orcidid: 0000-0001-8166-069X surname: Smith fullname: Smith, Stephen M organization: Nuffield Department of Clinical Neurosciences, FMRIB Centre, John Radcliffe Hospital, University of Oxford – sequence: 3 givenname: Daniel S surname: Marcus fullname: Marcus, Daniel S organization: Department of Radiology, Washington University Medical School – sequence: 4 givenname: Jesper L R surname: Andersson fullname: Andersson, Jesper L R organization: Nuffield Department of Clinical Neurosciences, FMRIB Centre, John Radcliffe Hospital, University of Oxford – sequence: 5 givenname: Edward J surname: Auerbach fullname: Auerbach, Edward J organization: Center for Magnetic Resonance Research (CMRR), University of Minnesota – sequence: 6 givenname: Timothy E J surname: Behrens fullname: Behrens, Timothy E J organization: Nuffield Department of Clinical Neurosciences, FMRIB Centre, John Radcliffe Hospital, University of Oxford – sequence: 7 givenname: Timothy S surname: Coalson fullname: Coalson, Timothy S organization: Department of Neuroscience, Washington University Medical School – sequence: 8 givenname: Michael P surname: Harms fullname: Harms, Michael P organization: Department of Psychiatry, Washington University Medical School – sequence: 9 givenname: Mark surname: Jenkinson fullname: Jenkinson, Mark organization: Nuffield Department of Clinical Neurosciences, FMRIB Centre, John Radcliffe Hospital, University of Oxford – sequence: 10 givenname: Steen surname: Moeller fullname: Moeller, Steen organization: Center for Magnetic Resonance Research (CMRR), University of Minnesota – sequence: 11 givenname: Emma C surname: Robinson fullname: Robinson, Emma C organization: Department of Computing, Imperial College London – sequence: 12 givenname: Stamatios N surname: Sotiropoulos fullname: Sotiropoulos, Stamatios N organization: Nuffield Department of Clinical Neurosciences, FMRIB Centre, John Radcliffe Hospital, University of Oxford – sequence: 13 givenname: Junqian orcidid: 0000-0001-8438-2066 surname: Xu fullname: Xu, Junqian organization: 7Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai – sequence: 14 givenname: Essa surname: Yacoub fullname: Yacoub, Essa organization: Center for Magnetic Resonance Research (CMRR), University of Minnesota – sequence: 15 givenname: Kamil surname: Ugurbil fullname: Ugurbil, Kamil organization: Center for Magnetic Resonance Research (CMRR), University of Minnesota – sequence: 16 givenname: David C surname: Van Essen fullname: Van Essen, David C email: vanessen@wustl.edu organization: Department of Neuroscience, Washington University Medical School |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/27571196$$D View this record in MEDLINE/PubMed |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 ObjectType-Review-3 content type line 23 Author Contributions M.F.G., S.M.S., D.S.M., K.U., and D.C.V.E. framed the issues and generated the initial draft. M.F.G., S.M.S., D.S.M., J.A., E.J.A., T.E.J.B, T.S.C., M.P.H., M.J., S.M., E.C.R., S.N.S., J.X., E.Y., K.U., and D.C.V.E. contributed novel methods or analyses. M.F.G., S.M.S., D.S.M., T.E.J.B, T.S.C., M.P.H., E.C.R., S.N.S., J.X., E.Y., K.U., and D.C.V.E. wrote the manuscript. |
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| Snippet | This paper describes an integrated approach for neuroimaging data acquisition, analysis and sharing. Building on methodological advances from the Human... Noninvasive human neuroimaging has yielded many discoveries about the brain. Numerous methodological advances have also occurred, though inertia has slowed... Non-invasive human neuroimaging has yielded many exciting discoveries about the brain. Numerous methodological advances have also occurred, though inertia has... |
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| Title | The Human Connectome Project's neuroimaging approach |
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