Tractography dissection variability: what happens when 42 groups dissect 14 white matter bundles on the same dataset?
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Abstract
White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human whole-brain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols for each fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process.
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Even when given the same set of underlying streamlines, variability across protocols for bundle segmentation was found to be greater than all other sources of potential unevenness.
“Even when given the same set of underlying streamlines, variability across protocols for bundle segmentation was found to be greater than all other sources of potential unevenness.”
From Superior Longitudinal Fasciculus: A Review of the Anatomical Descriptions With Functional Correlates (2022) · cites this paper as [69]
The Discussion (page unavailable) says that, using the same underlying streamlines, variability across segmentation protocols exceeded variability within protocols, including acquisition, registration, reconstruction, and streamline-generation effects. But it describes inter-protocol variability as “similar to (or greater than)” inter-subject variability, not greater than every other source. The Results for the corticospinal tract also show that the comparison depends on the measure used. (LLM, conf=0.88)
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Self-citationAssessment of the Depiction of Superficial White Matter Using Ultra‐High‐Resolution Diffusion MRI
Zhang, F., Chen, Y., Ning, L., Rushmore, J., Liu, Q., Du, M., Hassanzadeh‐Behbahani, S., Legarreta, J. H., Yeterian, E. H., Makris, N., et al. (2024) · Human Brain Mapping · doi:10.1002/hbm.70041 · Cited by 9 · see citing works
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Self-citationA systematic review of automated methods to perform white matter tract segmentation
Joshi, A., Li, H., Parikh, N. A., & He, L. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1376570 · Cited by 9 · see citing works
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Self-citationReal-time tractography-assisted neuronavigation for TMSPreprint
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Self-citationComparison of distortion correction preprocessing pipelines for DTI in the upper limb
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Self-citationShort fiber bundle filtering and test-retest reproducibility of the Superficial White Matter
Mendoza, C., Román, C., Mangin, J., Hernández, C., & Guevara, P. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1394681 · Cited by 7 · see citing works
Self-citationTractEM: Evaluation of protocols for deterministic tractography white matter atlas
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Self-citation
- Authors
- Schilling, K. G., Rheault, F., Petit, L., Hansen, C. B., Nath, V., Yeh, F.-C., Girard, G., Barakovic, M., Rafael-Patino, J., Yu, T., Fischi-Gomez, E., Pizzolato, M., Ocampo-Pineda, M., Schiavi, S., Canales-Rodríguez, E. J., Daducci, A., Granziera, C., Innocenti, G., Thiran, J.-P., Mancini, L., Wastling, S., Cocozza, S., Petracca, M., Pontillo, G., Mancini, M., Vos, S. B., Vakharia, V. N., Duncan, J. S., Melero, H., Manzanedo, L., Sanz-Morales, E., Peña-Melián, Á., Calamante, F., Attyé, A., Cabeen, R. P., Korobova, L., Toga, A. W., Vijayakumari, A. A., Parker, D., Verma, R., Radwan, A., Sunaert, S., Emsell, L., De Luca, A., Leemans, A., Bajada, C. J., Haroon, H., Azadbakht, H., Chamberland, M., Genc, S., Tax, C. M. W., Yeh, P.-H., Srikanchana, R., Mcknight, C. D., Yang, J. Y.-M., Chen, J., Kelly, C. E., Yeh, C.-H., Cochereau, J., Maller, J. J., Welton, T., Almairac, F., Seunarine, K. K., Clark, C. A., Zhang, F., Makris, N., Golby, A., Rathi, Y., O'Donnell, L. J., Xia, Y., Aydogan, D. B., Shi, Y., Fernandes, F. G., Raemaekers, M., Warrington, S., Michielse, S., Ramírez-Manzanares, A., Concha, L., Aranda, R., Meraz, M. R., Lerma-Usabiaga, G., Roitman, L., Fekonja, L. S., Calarco, N., Joseph, M., Nakua, H., Voineskos, A. N., Karan, P., Grenier, G., Legarreta, J. H., Adluru, N., Nair, V. A., Prabhakaran, V., Alexander, A. L., Kamagata, K., Saito, Y., Uchida, W., Andica, C., Abe, M., Bayrak, R. G., Wheeler-Kingshott, C. A. M. G., D'Angelo, E., Palesi, F., Savini, G., Rolandi, N., Guevara, P., Houenou, J., López-López, N., Mangin, J.-F., Poupon, C., Román, C., Vázquez, A., Maffei, C., Arantes, M., Andrade, J. P., Silva, S. M., Calhoun, V. D., Caverzasi, E., Sacco, S., Lauricella, M., Pestilli, F., Bullock, D., Zhan, Y., Brignoni-Perez, E., Lebel, C., Reynolds, J. E., Nestrasil, I., Labounek, R., Lenglet, C., Paulson, A., Aulicka, S., Heilbronner, S. R., Heuer, K., Chandio, B. Q., Guaje, J., Tang, W., Garyfallidis, E., Raja, R., Anderson, A. W., Landman, B. A., & Descoteaux, M.
- Affiliations
- Vanderbilt University Medical Center (US)
- Université de Sherbrooke (CA)
- Centre National de la Recherche Scientifique (FR)
- Vanderbilt University (US)
- University of Pittsburgh (US)
- Centre d'Imagerie BioMedicale (CH)
- École Polytechnique Fédérale de Lausanne (CH)
- Technical University of Denmark (DK)
- University of Verona (IT)
- University of Basel (CH)
- Karolinska Institutet (SE)
- National Hospital for Neurology and Neurosurgery (GB)
- University of Naples Federico II (IT)
- University of Malta (MT)
- Brighton and Sussex Medical School (GB)
- University College London (GB)
- Epilepsy Society (GB)
- Universidad Complutense de Madrid (ES)
- Universidad Rey Juan Carlos (ES)
- The University of Sydney (AU)
- University of Southern California (US)
- Murdoch Children's Research Institute (AU)
- University of Pennsylvania (US)
- KU Leuven (BE)
- University Medical Center Utrecht (NL)
- Manchester Academic Health Science Centre (GB)
- Cardiff University (GB)
- Walter Reed National Military Medical Center (US)
- Royal Children's Hospital (AU)
- National Neuroscience Institute (SG)
- Université Côte d'Azur (FR)
- Brigham and Women's Hospital (US)
- University of Nottingham (GB)
- Maastricht University (NL)
- Mathematics Research Center (MX)
- Universidad Nacional Autónoma de México (MX)
- Centro de Investigación Científica y de Educación Superior de Ensenada (MX)
- Stanford University (US)
- Charité – Universitätsmedizin Berlin (DE)
- Centre for Addiction and Mental Health (CA)
- University of Wisconsin–Madison (US)
- University of Pavia (IT)
- Fondazione Istituto Neurologico Nazionale Casimiro Mondino (IT)
- University of Concepción (CL)
- Harvard University (US)
- Universidade do Porto (PT)
- Georgia Institute of Technology (US)
- University of California, San Francisco (US)
- The University of Texas at Austin (US)
- Chinese Academy of Sciences (CN)
- University of Calgary (CA)
- University of Minnesota (US)
- Inserm (FR)
- Indiana University Bloomington (US)
- University of Arkansas for Medical Sciences (US)
- Corresponding author
- Schilling, Kurt G.
- ORCID
- Schilling, Kurt G.
- Rheault, François
- Petit, Laurent
- Hansen, Colin B.
- Nath, Vishwesh
- Yeh, Fang-Cheng
- Girard, Gabriel
- Yu, Thomas
- Pizzolato, Marco
- Schiavi, Simona
- Canales-Rodríguez, Erick J.
- Daducci, Alessandro
- Granziera, Cristina
- Thiran, Jean-Philippe
- Mancini, Laura
- Wastling, Stephen
- Cocozza, Sirio
- Petracca, Maria
- Pontillo, Giuseppe
- Mancini, Matteo
- Vos, Sjoerd B.
- Vakharia, Vejay N.
- Duncan, John S.
- Melero, Helena
- Peña-Melián, Ángel
- Calamante, Fernando
- Attyé, Arnaud
- Cabeen, Ryan P.
- Toga, Arthur W.
- Vijayakumari, Anupa Ambili
- Parker, Drew
- Verma, Ragini
- Radwan, Ahmed
- Sunaert, Stefan
- Emsell, Louise
- Leemans, Alexander
- Bajada, Claude J.
- Haroon, Hamied
- Chamberland, Maxime
- Genc, Sila
- Tax, Chantal M.W.
- Yeh, Ping-Hong
- Srikanchana, Rujirutana
- Mcknight, Colin D.
- Yang, Joseph Yuan-Mou
- Chen, Jian
- Kelly, Claire E.
- Yeh, Chun-Hung
- Maller, Jerome J.
- Welton, Thomas
- Almairac, Fabien
- Seunarine, Kiran K
- Clark, Chris A.
- Zhang, Fan
- Makris, Nikos
- Golby, Alexandra
- Xia, Yihao
- Aydogan, Dogu Baran
- Shi, Yonggang
- Fernandes, Francisco Guerreiro
- Raemaekers, Mathijs
- Warrington, Shaun
- Michielse, Stijn
- Ramírez-Manzanares, Alonso
- Concha, Luis
- Aranda, Ramón
- Fekonja, Lucius S.
- Calarco, Navona
- Joseph, Michael
- Nakua, Hajer
- Voineskos, Aristotle N.
- Karan, Philippe
- Legarreta, Jon Haitz
- Adluru, Nagesh
- Nair, Veena A.
- Prabhakaran, Vivek
- Alexander, Andrew L.
- Kamagata, Koji
- Saito, Yuya
- Uchida, Wataru
- Andica, Christina
- Abe, Masahiro
- Bayrak, Roza G.
- D'Angelo, Egidio
- Palesi, Fulvia
- Savini, Giovanni
- Rolandi, Nicolò
- Guevara, Pamela
- Houenou, Josselin
- López-López, Narciso
- Mangin, Jean-François
- Poupon, Cyril
- Román, Claudio
- Maffei, Chiara
- Arantes, Mavilde
- Andrade, José Paulo
- Silva, Susana Maria
- Calhoun, Vince D.
- Caverzasi, Eduardo
- Sacco, Simone
- Pestilli, Franco
- Bullock, Daniel
- Zhan, Yang
- Lebel, Catherine
- Reynolds, Jess E
- Labounek, René
- Lenglet, Christophe
- Paulson, Amy
- Heuer, Katja
- Chandio, Bramsh Qamar
- Guaje, Javier
- Tang, Wei
- Garyfallidis, Eleftherios
- Raja, Rajikha
- Anderson, Adam W.
- Landman, Bennett A.
- Descoteaux, Maxime
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2021-11
- Volume
- 243
- Pages
- 118502
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC8855321
- OpenAlex
- W3091774503
- Cited by
- 182
- References
- 101
- Citations by year
- 2026: 24 · 2025: 31 · 2024: 38 · 2023: 27 · 2022: 45 · 2021: 15 · 2020: 2
- FWCI
- 16.51
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- NeuroImage (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- Research Publications (Maastricht University) (publishedVersion, cc-by-nc-nd)
- Repository@Nottingham (University of Nottingham) (publishedVersion, other-oa)
- PubMed Central (acceptedVersion)
- ORCA Online Research @Cardiff (Cardiff University) (submittedVersion)
- IRIS Research product catalog (Sapienza University of Rome) (submittedVersion, cc-by)
- Lirias (submittedVersion, cc-by-nc-nd)
- Repository@Nottingham (University of Nottingham) (submittedVersion, cc-by-nc-nd)
- HAL (submittedVersion, other-oa)
- Aaltodoc (Aalto University) (submittedVersion, cc-by)
- Communities in ADDI (Universidad del Pais Vasco) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- UCL Discovery (University College London) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by-nc-nd)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- IRIS (submittedVersion)
- Minerva Access (University of Melbourne) (submittedVersion, cc-by-nc-nd)
- Research Explorer (The University of Manchester) (submittedVersion)
- Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) (submittedVersion, cc-by)
- SERVAL (Université de Lausanne) (submittedVersion)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
- Research Publications (Maastricht University) (submittedVersion, cc-by-nc-nd)
- Greater South Information System
- Funders
- National Science Foundation (1916518, T32EB001628, 1636893, 1912270, P30 NS076408, P41 EB027061, P41EB015898, 785907, UL1 RR024975-01, 1734853, MH103213)
- Vanderbilt University (UL1 RR024975-01, UL1 RR024975, VR3029, ViSE/VICTR VR3029)
- Vanderbilt Institute for Clinical and Translational Research (VR3029, RR024975, UL1 RR024975-01, UL1 RR024975)
- Waisman Center (HD090256, U54 HD090256)
- Université de Sherbrooke
- Wellcome Trust (17331, 215944, 213722, MR/N013913/1, 215944/Z/19/Z, 213722/Z/18/Z)
- Children’s Hospital of Wisconsin Research Institute
- Children's Hospital Foundation
- Compute Canada
- Murdoch Children's Research Institute
- Royal Children's Hospital Foundation (RCH-1000)
- National Institute for Health and Care Research (NF-SI-0515-10000)
- European Commission (HORIZON2020, 785907, 215944, 754462, HBP SGA2, 945539)
- University of Nottingham
- Deutsche Forschungsgemeinschaft (785907, EXC 2025, 945539)
- Agence Nationale de la Recherche (ANR-19-CE45-0022, 785907, 945539)
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (213722, PZ00P2, 185814, 215944)
- University of Melbourne
- Consejo Nacional de Ciencia y Tecnología (FB0008)
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (17331, U54 HD090256, 785907, 215944/Z/19/Z)
- Ministry of Science and Technology, Taiwan (MOST 109, 001-MY3, MOST109-2222-E182-001-MY3)
- State Government of Victoria (RCH 1000)
- Agencia Nacional de Investigación y Desarrollo (1190701, FB0008, FONDECYT 1190701)
- National Institutes of Health (1734853, 785907, 945539, VR3029, ViSE/VICTR VR3029, HD090256, RR024975, EB027061, NS076408)
- Intellectual and Developmental Disabilities Research Center
- Natural Sciences and Engineering Research Council of Canada (EB027061, P41EB015902)
- Medical Research Council (1957092, MR/N013913/1, 785907, 945539)
- Australian Research Council (DP170101815)
- National Health and Medical Research Council (1117724, 1091593)
- Japan Society for the Promotion of Science (20F20113)
- Fundação para a Ciência e a Tecnologia (UID/IC/4255/2013)
- National Institute on Aging (P30AG066530)
- National Institute of Mental Health (K24MH116366, R01MH111917, 945539, MH103213, R01MH119222, NIMH T32, 1734853, T32MH103213)
- National Institute of Neurological Disorders and Stroke (P30NS076408)
- National Institute of Biomedical Imaging and Bioengineering (1734853, P41EB015902, VR3029, R01EB029272, R01EB017230, EB027061, P41EB015922, P41EB015898, T32EB001628, P41EB028741, P41EB027061)
- National Institute of Child Health and Human Development (HD090256)
- National Center for Research Resources (215944/Z/19/Z, Grant UL1 RR024975-01, RR024975, P41RR013218, UL1RR024975, RR024975‐01, 17331)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (U54HD090256, P50HD103537)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- tractography, virtual dissection, diffusion MRI, reproducibility, human brain white matter
- MeSH terms
- Algorithms, Dissection, Humans, Image Processing, Computer-Assisted, Neural Pathways, Diffusion Tensor Imaging, White Matter
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.
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