The minimal preprocessing pipelines for the Human Connectome Project
Open accessAbstract
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.
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If MSM is also fed cortical myelin-weighted maps and low-dimensional resting-state network maps, it can utilise richer structural and connectional information and further improve intersubject alignment.
“However, if MSM is also fed cortical myelin-weighted maps [76], as well as low-dimensional resting-state network maps, it can utilise richer structural and connectional information, and further improve the intersubject alignment (“MSMmultimodal”) [43].”
From Functional connectomics from resting-state fMRI (2013) · cites this paper as [43]
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- Authors
- Glasser, M. F., Sotiropoulos, S. N., Wilson, J. A., Coalson, T. S., Fischl, B., Andersson, J. L., Xu, J., Jbabdi, S., Webster, M., Polimeni, J. R., Van Essen, D. C., & Jenkinson, M.
- Affiliations
- Washington University in St. Louis (US)
- University of Oxford (GB)
- Harvard University (US)
- University of Minnesota (US)
- Corresponding author
- Glasser, Matthew F.
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2013-10
- Volume
- 80
- Pages
- 105-124
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC3720813
- OpenAlex
- W1983208069
- Cited by
- 5,253
- References
- 61
- Citations by year
- 2026: 527 · 2025: 611 · 2024: 678 · 2023: 593 · 2022: 586 · 2021: 576 · 2020: 488 · 2019: 383 · 2018: 294 · 2017: 220 · 2016: 155 · 2015: 81 · 2014: 43 · 2013: 18
- FWCI
- 76.22
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion)
- Nottingham ePrints (University of Nottingham) (submittedVersion, cc-by-nc-nd)
- Repository@Nottingham (University of Nottingham) (submittedVersion, cc-by)
- UniTERAMO Research Catalog (University of Teramo) (submittedVersion)
- Funders
- National Institutes of Health (1U54MH091657-01, F30 MH097312, ROI MH-60974)
- National Institute of Biomedical Imaging and Bioengineering (R01EB006758)
- National Institute of Neurological Disorders and Stroke (1S10RR023043, R01 NS052585-01, 1S10RR019307, 1R01NS070963, 1R21NS072652-01, 1S10RR023401)
- Ellison Medical Foundation
- NIH Blueprint for Neuroscience Research (5U01-MH093765)
- National Institute on Aging (5R01AG008122-22, AG022381)
- National Center for Research Resources (P41-RR14075, BIRN002, U24 RR021382)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- Human Connectome Project, structural MRI, functional MRI, diffusion MRI, spatial distortion correction, cross-modal registration, automated image processing
- MeSH terms
- Algorithms, Brain, Humans, Image Interpretation, Computer-Assisted, Models, Anatomic, Models, Neurological, Nerve Net, Diffusion Tensor Imaging, Connectome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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