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...
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| Published in | NeuroImage (Orlando, Fla.) Vol. 80; pp. 105 - 124 |
|---|---|
| Main Authors | , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Inc
15.10.2013
Elsevier Limited |
| Subjects | |
| Online Access | Get full text |
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| Abstract | 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 3Tesla 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.
•Multi-modal preprocessing pipelines for the Human Connectome Project•Description of CIFTI file format and grayordinate coordinate system•Combined surface and volume neuroimaging analysis |
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| AbstractList | 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 grayordinates 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 for the pipelines. 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. 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.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. 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 3Tesla 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. •Multi-modal preprocessing pipelines for the Human Connectome Project•Description of CIFTI file format and grayordinate coordinate system•Combined surface and volume neuroimaging analysis 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 3Tesla 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. |
| Author | Andersson, Jesper L. Wilson, J. Anthony Sotiropoulos, Stamatios N. Jenkinson, Mark Glasser, Matthew F. Fischl, Bruce Xu, Junqian Polimeni, Jonathan R. Coalson, Timothy S. Webster, Matthew Jbabdi, Saad Van Essen, David C. |
| AuthorAffiliation | a Department of Anatomy and Neurobiology, Washington University Medical School, 660 S. Euclid Avenue, St. Louis, MO 63110, USA c Mallinckrodt Institute of Radiology, Washington University Medical School b Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, Oxford, UK f Center for Magnetic Resonance Research, University of Minnesota Medical School, Minneapolis, Minnesota, USA e Computer Science and AI Lab, Mass. Institute of Technology, Cambridge, MA g Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA d Athinoula A Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School/Mass. General Hospital, Boston, MA, USA |
| AuthorAffiliation_xml | – name: c Mallinckrodt Institute of Radiology, Washington University Medical School – name: g Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA – name: a Department of Anatomy and Neurobiology, Washington University Medical School, 660 S. Euclid Avenue, St. Louis, MO 63110, USA – name: b Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, Oxford, UK – name: e Computer Science and AI Lab, Mass. Institute of Technology, Cambridge, MA – name: d Athinoula A Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School/Mass. General Hospital, Boston, MA, USA – name: f Center for Magnetic Resonance Research, University of Minnesota Medical School, Minneapolis, Minnesota, USA |
| Author_xml | – sequence: 1 givenname: Matthew F. surname: Glasser fullname: Glasser, Matthew F. email: glasserm@wusm.wustl.edu organization: Department of Anatomy and Neurobiology, Washington University Medical School, 660 S. Euclid Avenue, St. Louis, MO 63110, USA – sequence: 2 givenname: Stamatios N. surname: Sotiropoulos fullname: Sotiropoulos, Stamatios N. email: stam@fmrib.ox.ac.uk organization: Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, Oxford, UK – sequence: 3 givenname: J. Anthony surname: Wilson fullname: Wilson, J. Anthony email: wilsont@mir.wustl.edu organization: Mallinckrodt Institute of Radiology, Washington University Medical School, USA – sequence: 4 givenname: Timothy S. surname: Coalson fullname: Coalson, Timothy S. email: tsc5yc@mst.edu organization: Department of Anatomy and Neurobiology, Washington University Medical School, 660 S. Euclid Avenue, St. Louis, MO 63110, USA – sequence: 5 givenname: Bruce surname: Fischl fullname: Fischl, Bruce email: fischl@nmr.mgh.harvard.edu organization: Athinoula A Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School/Mass. General Hospital, Boston, MA, USA – sequence: 6 givenname: Jesper L. surname: Andersson fullname: Andersson, Jesper L. email: jesper@FMRIB.OX.AC.UK organization: Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, Oxford, UK – sequence: 7 givenname: Junqian surname: Xu fullname: Xu, Junqian email: jxu@umn.edu organization: Center for Magnetic Resonance Research, University of Minnesota Medical School, Minneapolis, MN, USA – sequence: 8 givenname: Saad surname: Jbabdi fullname: Jbabdi, Saad email: saad@FMRIB.OX.AC.UK organization: Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, Oxford, UK – sequence: 9 givenname: Matthew surname: Webster fullname: Webster, Matthew email: mwebster@fmrib.ox.ac.uk organization: Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, Oxford, UK – sequence: 10 givenname: Jonathan R. surname: Polimeni fullname: Polimeni, Jonathan R. email: jonp@nmr.mgh.harvard.edu organization: Athinoula A Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School/Mass. General Hospital, Boston, MA, USA – sequence: 11 givenname: David C. surname: Van Essen fullname: Van Essen, David C. email: vanessen@wustl.edu organization: Department of Anatomy and Neurobiology, Washington University Medical School, 660 S. Euclid Avenue, St. Louis, MO 63110, USA – sequence: 12 givenname: Mark surname: Jenkinson fullname: Jenkinson, Mark email: mark.jenkinson@ndcn.ox.ac.uk organization: Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, Oxford, UK |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/23668970$$D View this record in MEDLINE/PubMed |
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76 Frost (10.1016/j.neuroimage.2013.04.127_bb0095) 2012; 59 Smith (10.1016/j.neuroimage.2013.04.127_bb0270) 2004; 23 Cohen-Adad (10.1016/j.neuroimage.2013.04.127_bb0040) 2013 Barch (10.1016/j.neuroimage.2013.04.127_bb9000) 2013; 80 Jenkinson (10.1016/j.neuroimage.2013.04.127_bb0155) 2012; 62 Mugler (10.1016/j.neuroimage.2013.04.127_bb0185) 1990; 15 |
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| SubjectTerms | Acquisitions & mergers Algorithms Brain Brain - anatomy & histology Brain - physiology CIFTI Connectome - methods Cross-modal Diffusion Tensor Imaging - methods Grayordinates Human Connectome Project Humans Image analysis pipeline Image Interpretation, Computer-Assisted - methods Medical imaging Models, Anatomic Models, Neurological Multi-modal data integration Nerve Net - anatomy & histology Nerve Net - physiology Neurosciences Surface-based analysis |
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| Title | The minimal preprocessing pipelines for the Human Connectome Project |
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