The Human Connectome Project's neuroimaging approach
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, particula...
| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Article |
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Nature Publishing Group
2016
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| Online Access: | https://eprints.nottingham.ac.uk/50950/ |
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| author | Glasser, Matthew F. Smith, Stephen M. Marcus, Daniel S. Andersson, Jesper L.R. Auerbach, Edward J. Behrens, Timothy E.J. Coalson, Timothy S. Harms, Michael P. Jenkinson, Mark Moeller, Steen Robinson, Emma C. Sotiropoulos, Stamatios N. Xu, Junqian Yacoub, Essa Ugurbil, Kamil Van Essen, David C. |
| author_facet | Glasser, Matthew F. Smith, Stephen M. Marcus, Daniel S. Andersson, Jesper L.R. Auerbach, Edward J. Behrens, Timothy E.J. Coalson, Timothy S. Harms, Michael P. Jenkinson, Mark Moeller, Steen Robinson, Emma C. Sotiropoulos, Stamatios N. Xu, Junqian Yacoub, Essa Ugurbil, Kamil Van Essen, David C. |
| author_sort | Glasser, Matthew F. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T20:18:47Z |
| format | Article |
| id | nottingham-50950 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:18:47Z |
| publishDate | 2016 |
| publisher | Nature Publishing Group |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-509502020-05-04T18:05:39Z https://eprints.nottingham.ac.uk/50950/ The Human Connectome Project's neuroimaging approach Glasser, Matthew F. Smith, Stephen M. Marcus, Daniel S. Andersson, Jesper L.R. Auerbach, Edward J. Behrens, Timothy E.J. Coalson, Timothy S. Harms, Michael P. Jenkinson, Mark Moeller, Steen Robinson, Emma C. Sotiropoulos, Stamatios N. Xu, Junqian Yacoub, Essa Ugurbil, Kamil Van Essen, David C. 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. Nature Publishing Group 2016-08-26 Article PeerReviewed Glasser, Matthew F., Smith, Stephen M., Marcus, Daniel S., Andersson, Jesper L.R., Auerbach, Edward J., Behrens, Timothy E.J., Coalson, Timothy S., Harms, Michael P., Jenkinson, Mark, Moeller, Steen, Robinson, Emma C., Sotiropoulos, Stamatios N., Xu, Junqian, Yacoub, Essa, Ugurbil, Kamil and Van Essen, David C. (2016) The Human Connectome Project's neuroimaging approach. Nature Neuroscience, 19 (9). pp. 1175-1187. ISSN 1546-1726 Language; Neural circuits; Sensory processing https://www.nature.com/articles/nn.4361 doi:10.1038/nn.4361 doi:10.1038/nn.4361 |
| spellingShingle | Language; Neural circuits; Sensory processing Glasser, Matthew F. Smith, Stephen M. Marcus, Daniel S. Andersson, Jesper L.R. Auerbach, Edward J. Behrens, Timothy E.J. Coalson, Timothy S. Harms, Michael P. Jenkinson, Mark Moeller, Steen Robinson, Emma C. Sotiropoulos, Stamatios N. Xu, Junqian Yacoub, Essa Ugurbil, Kamil Van Essen, David C. The Human Connectome Project's neuroimaging approach |
| title | The Human Connectome Project's neuroimaging approach |
| title_full | The Human Connectome Project's neuroimaging approach |
| title_fullStr | The Human Connectome Project's neuroimaging approach |
| title_full_unstemmed | The Human Connectome Project's neuroimaging approach |
| title_short | The Human Connectome Project's neuroimaging approach |
| title_sort | human connectome project's neuroimaging approach |
| topic | Language; Neural circuits; Sensory processing |
| url | https://eprints.nottingham.ac.uk/50950/ https://eprints.nottingham.ac.uk/50950/ https://eprints.nottingham.ac.uk/50950/ |