The DOI matches its Crossref record; 61 of the 61 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Advances in functional and structural MR image analysis and implementation as FSL
Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E. J., Johansen-Berg, H., Bannister, P. R., De Luca, M., Drobnjak, I., Flitney, D. E., et al. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.051 · Cited by 13,630
Self-citationDesikan, R. S., Ségonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C., Blacker, D., Buckner, R. L., Dale, A. M., Maguire, R. P., Hyman, B. T., et al. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.01.021 · Cited by 13,324
Self-citationJenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002) · NeuroImage · doi:10.1006/nimg.2002.1132 · Cited by 11,888
Self-citationJenkinson, M., Beckmann, C. F., Behrens, T. E. J., Woolrich, M. W., & Smith, S. M. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.09.015 · Cited by 11,314
Self-citationCortical Surface-Based Analysis
Dale, A. M., Fischl, B., & Sereno, M. I. (1999) · NeuroImage · doi:10.1006/nimg.1998.0395 · Cited by 10,910
Self-citationFast robust automated brain extraction
Smith, S. M. (2002) · Human Brain Mapping · doi:10.1002/hbm.10062 · Cited by 10,496
Fischl, B., Salat, D. H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., van der Kouwe, A., Killiany, R., Kennedy, D., Klaveness, S., et al. (2002) · Neuron · doi:10.1016/s0896-6273(02)00569-x · Cited by 8,610
Self-citationZhang, Y., Brady, M., & Smith, S. M. (2001) · IEEE Transactions on Medical Imaging · doi:10.1109/42.906424 · Cited by 7,628
Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.10.018 · Cited by 7,429
A global optimisation method for robust affine registration of brain images
Jenkinson, M., & Smith, S. M. (2001) · Medical Image Analysis · doi:10.1016/s1361-8415(01)00036-6 · Cited by 7,018
Self-citation
Cited by
Sorted by citations
Items 101–121 of 121
Brain Decoding from Functional MRI Using Long Short-Term Memory Recurrent Neural Networks
Li, H., & Fan, Y. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-00931-1_37 · Cited by 45
Pushing the Boundaries of Psychiatric Neuroimaging to Ground Diagnosis in Biology
Saggar, M., & Uddin, L. Q. (2019) · eneuro · doi:10.1523/eneuro.0384-19.2019 · Cited by 38
The human brain uses spatial schemas to represent segmented environments
Peer, M., & Epstein, R. A. (2021) · Current Biology · doi:10.1016/j.cub.2021.08.012 · Cited by 38
Adaptive contextualization: a new role for the default mode network in affective learning
Marstaller, L., Burianová, H., & Reutens, D. C. (2017) · Human Brain Mapping · doi:10.1002/hbm.23442 · Cited by 37
Adolescent Decision-Making Under Risk: Neural Correlates and Sex Differences
Korucuoglu, O., Harms, M. P., Kennedy, J. T., Golosheykin, S., Astafiev, S. V., Barch, D. M., & Anokhin, A. P. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhz269 · Cited by 33
Whitman, E. T., Elliott, M. L., Knodt, A. R., Abraham, W. C., Anderson, T. J., Cutfield, N. J., Hogan, S., Ireland, D., Melzer, T. R., Ramrakha, S., et al. (2025) · Nature Aging · doi:10.1038/s43587-025-00897-z · Cited by 37
Task-based fMRI predicts response and remission to exposure therapy in obsessive-compulsive disorder
Pagliaccio, D., Middleton, R., Hezel, D., Steinman, S., Snorrason, I., Gershkovich, M., Campeas, R., Pinto, A., Van Meter, P., Simpson, H. B., et al. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1909199116 · Cited by 30
Park, B.-Y., Kim, M., Seo, J., Lee, J.-M., & Park, H. (2016) · Brain Topography · doi:10.1007/s10548-015-0463-1 · Cited by 31
The buildup of an urge in obsessive–compulsive disorder: Behavioral and neuroimaging correlates
Stern, E. R., Brown, C., Ludlow, M., Shahab, R., Collins, K., Lieval, A., Tobe, R. H., Iosifescu, D. V., Burdick, K. E., & Fleysher, L. (2020) · Human Brain Mapping · doi:10.1002/hbm.24898 · Cited by 29
“…was performed using a combination of Statistical Parametric Mapping v.12, scripts taken from the Human Connectome Project preprocessing pipeline (Glasser et al., 2013), and FSL v. 5.0.10, and included gradient nonlinearity distortion correction, realignment of functional images, fieldmap-based…”
MethodInfluentialHigh-dose ondansetron reduces activation of interoceptive and sensorimotor brain regions
Stern, E. R., Shahab, R., Grimaldi, S. J., Leibu, E., Murrough, J. W., Fleysher, L., Parides, M. K., Coffey, B. J., Burdick, K. E., & Goodman, W. K. (2019) · Neuropsychopharmacology · doi:10.1038/s41386-018-0174-x · Cited by 27
Human habit neural circuitry may be perturbed in eating disorders
Wang, A. R., Kuijper, F. M., Barbosa, D. A. N., Hagan, K. E., Lee, E., Tong, E., Choi, E. Y., McNab, J. A., Bohon, C., & Halpern, C. H. (2023) · Science Translational Medicine · doi:10.1126/scitranslmed.abo4919 · Cited by 27
Pujol, S., Cabeen, R., Sébille, S. B., Yelnik, J., François, C., Fernandez Vidal, S., Karachi, C., Zhao, Y., Cosgrove, G. R., Jannin, P., et al. (2017) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2016.00119 · Cited by 21
Hierarchical microstructure informed tractography
Ocampo-Pineda, M., Schiavi, S., Rheault, F., Girard, G., Petit, L., Descoteaux, M., & Daducci, A. (2021) · Brain Connectivity · doi:10.1089/brain.2020.0907 · Cited by 19
Katsumi, Y., Kamona, N., Zhang, J., Bunce, J. G., Hutchinson, J. B., Yarossi, M., Tunik, E., Quigley, K. S., Dickerson, B. C., & Barrett, L. F. (2021) · doi:10.1101/2021.09.01.456844 · Cited by 89
Popp, J. L., Thiele, J. A., Faskowitz, J., Seguin, C., Sporns, O., & Hilger, K. (2025) · Communications Biology · doi:10.1038/s42003-025-08231-4 · Cited by 17
Li, H., & Fan, Y. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-00931-1_27 · Cited by 13
Torres-Velazquez, M., Hwang, G., Cook, C. J., Hermann, B., Prabhakaran, V., Meyerand, M. E., & McMillan, A. B. (2020) · 2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops) · doi:10.1109/isbiworkshops50223.2020.9153359 · Cited by 13
Sulpizio, V., Strappini, F., Fattori, P., Galati, G., Galletti, C., Pecchinenda, A., & Pitzalis, S. (2022) · Brain Structure and Function · doi:10.1007/s00429-022-02549-z · Cited by 12
Hart, M. G., Romero-Garcia, R., Price, S. J., Santarius, T., & Suckling, J. (2020) · World Neurosurgery · doi:10.1016/j.wneu.2020.03.116 · Cited by 7
Rubien-Thomas, E., Berrian, N., Cervera, A., Nardos, B., Cohen, A. O., Lowrey, A., Daumeyer, N. M., Camp, N. P., Hughes, B. L., Eberhardt, J. L., et al. (2021) · Cognitive, Affective, & Behavioral Neuroscience · doi:10.3758/s13415-021-00896-8 · Cited by 6
Camastra, C., Pelagi, A., Quattrone, A., & Sarica, A. (2025) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-82487-6_17 · Cited by 1
Metadata
- 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 ›
Are you an author, or do you see a problem with this page? Request a review
