The minimal preprocessing pipelines for the Human Connectome Project
The Human Connectome Project (HCP) faces the challenging task of bringing multiple magnetic resonance imaging (MRI) modalities together in a common automated preprocessing framework across a large cohort of subjects. The MRI data acquired by the HCP differ in many ways from data acquired on conventi...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
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Elsevier
2013
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| Online Access: | https://eprints.nottingham.ac.uk/52878/ |
| _version_ | 1848798830999371776 |
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| author | Glasser, Matthew F. Sotiropoulos, Stamatios N. Wilson, J. Anthony Coalson, Timothy S. Fischl, Bruce Andersson, Jesper L. Xu, Junqian Jbabdi, Saad Webster, Matthew Polimeni, Jonathan R. Van Essen, David C. Jenkinson, Mark |
| author_facet | Glasser, Matthew F. Sotiropoulos, Stamatios N. Wilson, J. Anthony Coalson, Timothy S. Fischl, Bruce Andersson, Jesper L. Xu, Junqian Jbabdi, Saad Webster, Matthew Polimeni, Jonathan R. Van Essen, David C. Jenkinson, Mark |
| author_sort | Glasser, Matthew F. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | The Human Connectome Project (HCP) faces the challenging task of bringing multiple magnetic resonance imaging (MRI) modalities together in a common automated preprocessing framework across a large cohort of subjects. The MRI data acquired by the HCP differ in many ways from data acquired on conventional 3 Tesla scanners and often require newly developed preprocessing methods. We describe the minimal preprocessing pipelines for structural, functional, and diffusion MRI that were developed by the HCP to accomplish many low level tasks, including spatial artifact/distortion removal, surface generation, cross-modal registration, and alignment to standard space. These pipelines are specially designed to capitalize on the high quality data offered by the HCP. The final standard space makes use of a recently introduced CIFTI file format and the associated grayordinate spatial coordinate system. This allows for combined cortical surface and subcortical volume analyses while reducing the storage and processing requirements for high spatial and temporal resolution data. Here, we provide the minimum image acquisition requirements for the HCP minimal preprocessing pipelines and additional advice for investigators interested in replicating the HCP's acquisition protocols or using these pipelines. Finally, we discuss some potential future improvements to the pipelines. |
| first_indexed | 2025-11-14T20:26:01Z |
| format | Article |
| id | nottingham-52878 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:26:01Z |
| publishDate | 2013 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-528782020-05-04T16:39:20Z https://eprints.nottingham.ac.uk/52878/ The minimal preprocessing pipelines for the Human Connectome Project Glasser, Matthew F. Sotiropoulos, Stamatios N. Wilson, J. Anthony Coalson, Timothy S. Fischl, Bruce Andersson, Jesper L. Xu, Junqian Jbabdi, Saad Webster, Matthew Polimeni, Jonathan R. Van Essen, David C. Jenkinson, Mark The Human Connectome Project (HCP) faces the challenging task of bringing multiple magnetic resonance imaging (MRI) modalities together in a common automated preprocessing framework across a large cohort of subjects. The MRI data acquired by the HCP differ in many ways from data acquired on conventional 3 Tesla scanners and often require newly developed preprocessing methods. We describe the minimal preprocessing pipelines for structural, functional, and diffusion MRI that were developed by the HCP to accomplish many low level tasks, including spatial artifact/distortion removal, surface generation, cross-modal registration, and alignment to standard space. These pipelines are specially designed to capitalize on the high quality data offered by the HCP. The final standard space makes use of a recently introduced CIFTI file format and the associated grayordinate spatial coordinate system. This allows for combined cortical surface and subcortical volume analyses while reducing the storage and processing requirements for high spatial and temporal resolution data. Here, we provide the minimum image acquisition requirements for the HCP minimal preprocessing pipelines and additional advice for investigators interested in replicating the HCP's acquisition protocols or using these pipelines. Finally, we discuss some potential future improvements to the pipelines. Elsevier 2013-10-15 Article PeerReviewed Glasser, Matthew F., Sotiropoulos, Stamatios N., Wilson, J. Anthony, Coalson, Timothy S., Fischl, Bruce, Andersson, Jesper L., Xu, Junqian, Jbabdi, Saad, Webster, Matthew, Polimeni, Jonathan R., Van Essen, David C. and Jenkinson, Mark (2013) The minimal preprocessing pipelines for the Human Connectome Project. NeuroImage, 80 . pp. 105-124. ISSN 1053-8119 https://www.sciencedirect.com/science/article/pii/S1053811913005053?via%3Dihub doi:10.1016/j.neuroimage.2013.04.127 doi:10.1016/j.neuroimage.2013.04.127 |
| spellingShingle | Glasser, Matthew F. Sotiropoulos, Stamatios N. Wilson, J. Anthony Coalson, Timothy S. Fischl, Bruce Andersson, Jesper L. Xu, Junqian Jbabdi, Saad Webster, Matthew Polimeni, Jonathan R. Van Essen, David C. Jenkinson, Mark The minimal preprocessing pipelines for the Human Connectome Project |
| title | The minimal preprocessing pipelines for the Human Connectome Project |
| title_full | The minimal preprocessing pipelines for the Human Connectome Project |
| title_fullStr | The minimal preprocessing pipelines for the Human Connectome Project |
| title_full_unstemmed | The minimal preprocessing pipelines for the Human Connectome Project |
| title_short | The minimal preprocessing pipelines for the Human Connectome Project |
| title_sort | minimal preprocessing pipelines for the human connectome project |
| url | https://eprints.nottingham.ac.uk/52878/ https://eprints.nottingham.ac.uk/52878/ https://eprints.nottingham.ac.uk/52878/ |