The DOI matches its Crossref record; 64 of the 65 references it cites were found in a registry or an index.
Limits to anatomical accuracy of diffusion tractography using modern approaches
Open accessPublished version of a preprint
Abstract
Diffusion MRI fiber tractography is widely used to probe the structural connectivity of the brain, with a range of applications in both clinical and basic neuroscience. Despite widespread use, tractography has well-known pitfalls that limits the anatomical accuracy of this technique. Numerous modern methods have been developed to address these shortcomings through advances in acquisition, modeling, and computation. To test whether these advances improve tractography accuracy, we organized the 3-D Validation of Tractography with Experimental MRI (3D-VoTEM) challenge at the ISBI 2018 conference. We made available three unique independent tractography validation datasets - a physical phantom and two ex vivo brain specimens - resulting in 176 distinct submissions from 9 research groups. By comparing results over a wide range of fiber complexities and algorithmic strategies, this challenge provides a more comprehensive assessment of tractography's inherent limitations than has been reported previously. The central results were consistent across all sub-challenges in that, despite advances in tractography methods, the anatomical accuracy of tractography has not dramatically improved in recent years. Taken together, our results independently confirm findings from decades of tractography validation studies, demonstrate inherent limitations in reconstructing white matter pathways using diffusion MRI data alone, and highlight the need for alternative or combinatorial strategies to accurately map the fiber pathways of the brain.
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“A specific limitation inherent to probabilistic streamline tractography is the fiber-length bias: local uncertainties in the fiber orientation distribution at each step in the tracking process accumulate to produce global errors for longer streamlines, leading to an underestimation of connectivity weights at greater distances.”
From The role of spatial embedding in mouse brain networks constructed from diffusion tractography and tracer injections (2021) · cites this paper as Schilling et al., 2019
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Even with these relatively simple post-processing approaches, the results of this study serve to echo recent tractography validation reviews that suggest that the future of tractography connectomics hinges on the incorporation of more advanced anatomical and microstructural priors to tractography pipelines in order to address geometric and other biases and make streamlines more quantitative and”
From The role of spatial embedding in mouse brain networks constructed from diffusion tractography and tracer injections (2021) · cites this paper as Schilling et al., 2019
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Recent efforts to validate tractography have uncovered several limitations, and even modern approaches still produce high numbers of both false positive and false negative tracts (Aydogan et al., 2018; Maier-Hein et al., 2017; Schilling et al., 2019; Thomas et al., 2014).”
From The role of spatial embedding in mouse brain networks constructed from diffusion tractography and tracer injections (2021) · cites this paper as Schilling et al., 2019
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
Jenkinson, 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
Principal warps: thin-plate splines and the decomposition of deformations
Bookstein, F. L. (1989) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.24792 · Cited by 4,906
Le Bihan, D., Breton, E., Lallemand, D., Grenier, P., Cabanis, E., & Laval-Jeantet, M. (1986) · Radiology · doi:10.1148/radiology.161.2.3763909 · Cited by 3,912
Tracking neuronal fiber pathways in the living human brain
Conturo, T. E., Lori, N. F., Cull, T. S., Akbudak, E., Snyder, A. Z., Shimony, J. S., McKinstry, R. C., Burton, H., & Raichle, M. E. (1999) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.96.18.10422 · Cited by 1,774
Tractography-based connectomes are dominated by false-positive connections
Maier-Hein, K. H., Neher, P., Houde, J.-C., Côté, M.-A., Garyfallidis, E., Zhong, J., Chamberland, M., Yeh, F.-C., Lin, Y.-C., Ji, Q., et al. (2016) · doi:10.1101/084137 · Cited by 1,376
Self-citationSchmahmann, J. D., & Pandya, D. N. (2006) · doi:10.1093/acprof:oso/9780195104233.001.0001 · Cited by 1,157
About “axial” and “radial” diffusivities
Wheeler‐Kingshott, C. A. M., & Cercignani, M. (2009) · Magnetic Resonance in Medicine · doi:10.1002/mrm.21965 · Cited by 885
Twenty‐five pitfalls in the analysis of diffusion MRI data
Jones, D. K., & Cercignani, M. (2010) · NMR in Biomedicine · doi:10.1002/nbm.1543 · Cited by 827
SIFT: Spherical-deconvolution informed filtering of tractograms
Smith, R. E., Tournier, J.-D., Calamante, F., & Connelly, A. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2012.11.049 · Cited by 752
Thomas, C., Ye, F. Q., Irfanoglu, M. O., Modi, P., Saleem, K. S., Leopold, D. A., & Pierpaoli, C. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1405672111 · Cited by 725
Self-citationProceedings of the International Society for Magnetic Resonance in Medicine
(2018) · Proceedings of the Society of Magnetic Resonance in Medicine · doi:10.1002/(issn)1557-3672 · Cited by 718
Detection and modeling of non‐Gaussian apparent diffusion coefficient profiles in human brain data
Alexander, D. C., Barker, G. J., & Arridge, S. (2002) · Magnetic Resonance in Medicine · doi:10.1002/mrm.10209 · Cited by 538
Diffusion MRI: Theory, methods, and applications
Jones, D. K. (2011) · Cited by 450
Reveley, C., Seth, A. K., Pierpaoli, C., Silva, A. C., Yu, D., Saunders, R. C., Leopold, D. A., & Ye, F. Q. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1418198112 · Cited by 417
Self-citationThe adaptive bases algorithm for intensity-based nonrigid image registration
Rohde, G. K., Aldroubi, A., & Dawant, B. M. (2003) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2003.819299 · Cited by 401
Donahue, C. J., Sotiropoulos, S. N., Jbabdi, S., Hernandez-Fernandez, M., Behrens, T. E., Dyrby, T. B., Coalson, T., Kennedy, H., Knoblauch, K., Van Essen, D. C., et al. (2016) · The Journal of Neuroscience · doi:10.1523/jneurosci.0493-16.2016 · Cited by 380
Self-citationDetermining and visualizing uncertainty in estimates of fiber orientation from diffusion tensor MRI
Jones, D. K. (2002) · Magnetic Resonance in Medicine · doi:10.1002/mrm.10331 · Cited by 371
Towards quantitative connectivity analysis: reducing tractography biases
Girard, G., Whittingstall, K., Deriche, R., & Descoteaux, M. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.04.074 · Cited by 324
Self-citationQuickBundles, a Method for Tractography Simplification
Garyfallidis, E., Brett, M., Correia, M., Williams, G. B., & Nimmo‐Smith, I. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2012.00175 · Cited by 315
In vivo histology of the myelin g-ratio with magnetic resonance imaging
Stikov, N., Campbell, J. S. W., Stroh, T., Lavelée, M., Frey, S., Novek, J., Nuara, S. G., Ho, M., Bedell, B. J., Dougherty, R. F., et al. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.05.023 · Cited by 306
COMMIT: Convex Optimization Modeling for Microstructure Informed Tractography
Daducci, A., Dal Palu, A., Lemkaddem, A., & Thiran, J.-P. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2352414 · Cited by 255
Self-citationEssayed, W. I., Zhang, F., Unadkat, P., Cosgrove, G. R., Golby, A. J., & O’Donnell, L. J. (2017) · NeuroImage Clinical · doi:10.1016/j.nicl.2017.06.011 · Cited by 249
Validation of in vitro probabilistic tractography
Dyrby, T. B., Søgaard, L. V., Parker, G. J., Alexander, D. C., Lind, N. M., Baaré, W. F. C., Hay-Schmidt, A., Eriksen, N., Pakkenberg, B., Paulson, O. B., et al. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.06.022 · Cited by 236
Self-citationOptimal imaging parameters for fiber-orientation estimation in diffusion MRI
Alexander, D. C., & Barker, G. J. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.04.008 · Cited by 231
Tractometer: Towards validation of tractography pipelines
Côté, M.-A., Girard, G., Boré, A., Garyfallidis, E., Houde, J.-C., & Descoteaux, M. (2013) · Medical Image Analysis · doi:10.1016/j.media.2013.03.009 · Cited by 228
Self-citationA diffusion MRI tractography connectome of the mouse brain and comparison with neuronal tracer data
Calabrese, E., Badea, A., Cofer, G., Qi, Y., & Johnson, G. A. (2015) · Cerebral Cortex · doi:10.1093/cercor/bhv121 · Cited by 221
Dyrby, T. B., Baaré, W. F. C., Alexander, D. C., Jelsing, J., Garde, E., & Søgaard, L. V. (2010) · Human Brain Mapping · doi:10.1002/hbm.21043 · Cited by 216
Self-citationIrfanoglu, M. O., Walker, L., Sarlls, J. E., Marenco, S., & Pierpaoli, C. M. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.02.054 · Cited by 216
Self-citationHistological validation of diffusion MRI fiber orientation distributions and dispersion
Schilling, K. G., Janve, V., Gao, Y., Stepniewska, I., Landman, B. A., & Anderson, A. W. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2017.10.046 · Cited by 195
Self-citationThe effects of brain tissue decomposition on diffusion tensor imaging and tractography
D'Arceuil, H. E., & de Crespigny, A. J. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.02.039 · Cited by 185
Leergaard, T. B., White, N. S., de Crespigny, A. J., Bolstad, I., D'Arceuil, H. E., Bjaalie, J. G., & Dale, A. M. (2010) · PLoS ONE · doi:10.1371/journal.pone.0008595 · Cited by 180
Pujol, S., Wells, W., Pierpaoli, C., Brun, C., Gee, J., Cheng, G., Vemuri, B., Commowick, O., Prima, S., Stamm, A., et al. (2015) · Journal of Neuroimaging · doi:10.1111/jon.12283 · Cited by 171
Self-citationQuantitative Comparison of Reconstruction Methods for Intra-Voxel Fiber Recovery From Diffusion MRI
Daducci, A., Canales-Rodríguez, E. J., Descoteaux, M., Garyfallidis, E., Gur, Y., Lin, Y., Mani, M., Merlet, S., Paquette, M., Ramírez-Manzanares, A., et al. (2013) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2013.2285500 · Cited by 167
Self-citationConfirmation of a gyral bias in diffusion MRI fiber tractography
Schilling, K. G., Gao, Y., Janve, V. A., Stepniewska, I., Landman, B. A., & Anderson, A. W. (2017) · Human Brain Mapping · doi:10.1002/hbm.23936 · Cited by 143
Self-citationMicrostructure informed tractography: pitfalls and open challenges
Daducci, A., Dal Palú, A., Descoteaux, M., & Thiran, J.-P. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00247 · Cited by 138
Self-citationValidation of tractography: Comparison with manganese tracing
Knösche, T. R., Anwander, A., Liptrot, M., & Dyrby, T. B. (2015) · Human Brain Mapping · doi:10.1002/hbm.22902 · Cited by 127
Self-citationFiberfox: Facilitating the creation of realistic white matter software phantoms
Neher, P., Laun, F. B., Stieltjes, B., & Maier-Hein, K. H. (2013) · Magnetic Resonance in Medicine · doi:10.1002/mrm.25045 · Cited by 112
Self-citationValidation of High-Resolution Tractography Against In Vivo Tracing in the Macaque Visual Cortex
Azadbakht, H., Parkes, L. M., Haroon, H. A., Augath, M., Logothetis, N. K., de Crespigny, A., D'Arceuil, H. E., & Parker, G. J. M. (2015) · Cerebral Cortex · doi:10.1093/cercor/bhu326 · Cited by 103
Validation strategies for the interpretation of microstructure imaging using diffusion MRI
Dyrby, T. B., Innocenti, G. M., Bech, M., & Lundell, H. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.06.049 · Cited by 94
Self-citationNing, L., Laun, F. B., Gur, Y., DiBella, E., Deslauriers‐Gauthier, S., Megherbi, T., Ghosh, A., Zucchelli, M., Menegaz, G., Fick, R., et al. (2015) · Medical Image Analysis · doi:10.1016/j.media.2015.10.012 · Cited by 84
Self-citationClose, T., Tournier, J., Calamante, F., Johnston, L. A., Mareels, I., & Connelly, A. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.03.077 · Cited by 82
Accuracy of image registration between MRI and light microscopy in the ex vivo brain
Choe, A. S., Gao, Y., Li, X., Compton, K. B., Stepniewska, I., & Anderson, A. W. (2011) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2011.02.022 · Cited by 76
Self-citationAydogan, D. B., Jacobs, R., Dulawa, S., Thompson, S. L., Francois, M. C., Toga, A. W., Dong, H., Knowles, J. A., & Shi, Y. (2018) · Brain Structure and Function · doi:10.1007/s00429-018-1663-8 · Cited by 72
Self-citationFiber tractography using machine learning
Neher, P. F., Côté, M.-A., Houde, J.-C., Descoteaux, M., & Maier-Hein, K. H. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.07.028 · Cited by 68
Self-citationGao, Y., Choe, A. S., Stepniewska, I., Li, X., Avison, M. J., & Anderson, A. W. (2013) · PLoS ONE · doi:10.1371/journal.pone.0075065 · Cited by 62
Self-citationAxTract: Toward microstructure informed tractography
Girard, G., Daducci, A., Petit, L., Thiran, J., Whittingstall, K., Deriche, R., Wassermann, D., & Descoteaux, M. (2017) · Human Brain Mapping · doi:10.1002/hbm.23741 · Cited by 62
Self-citationActive delineation of Meyer's loop using oriented priors through MAGNEtic tractography (MAGNET)
Chamberland, M., Scherrer, B., Prabhu, S. P., Madsen, J. R., Fortin, D., Whittingstall, K., Descoteaux, M., & Warfield, S. K. (2016) · Human Brain Mapping · doi:10.1002/hbm.23399 · Cited by 51
Self-citationSchilling, K. G., Gao, Y., Stepniewska, I., Janve, V. A., Landman, B. A., & Anderson, A. W. (2018) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2018.09.004 · Cited by 48
Self-citationTractography optimization using quantitative T1 mapping in the human optic radiation
Schurr, R., Duan, Y., Norcia, A. M., Ogawa, S., Yeatman, J. D., & Mezer, A. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.06.060 · Cited by 35
The VALiDATe29 MRI Based Multi-Channel Atlas of the Squirrel Monkey Brain
Schilling, K. G., Gao, Y., Stepniewska, I., Wu, T., Wang, F., Landman, B. A., Gore, J. C., Chen, L. M., & Anderson, A. W. (2017) · Neuroinformatics · doi:10.1007/s12021-017-9334-0 · Cited by 26
Self-citationSchilling, K. G., Gao, Y., Li, M., Wu, T., Blaber, J. A., Landman, B. A., Anderson, A. W., Ding, Z., & Gore, J. C. (2018) · Magnetic Resonance in Medicine · doi:10.1002/mrm.27512 · Cited by 24
Self-citationDauguet, J., Peled, S., Berezovskii, V. K., Delzescaux, T., Warfield, S. K., Born, R. T., & Westin, C. (2006) · Lecture notes in computer science · doi:10.1007/11866565_14 · Cited by 23
Self-citationSchilling, K. G., Gao, Y., Stepniewska, I., Choe, A. S., Landman, B. A., & Anderson, A. W. (2016) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2016.08.015 · Cited by 23
Self-citationDynamic Multiscale Modes of Resting State Brain Activity Detected by Entropy Field Decomposition
Frank, L. R., & Galinsky, V. L. (2016) · Neural Computation · doi:10.1162/neco_a_00871 · Cited by 21
A Unified Theory of Neuro-MRI Data Shows Scale-Free Nature of Connectivity Modes
Galinsky, V. L., & Frank, L. R. (2017) · Neural Computation · doi:10.1162/neco_a_00955 · Cited by 20
Rheault, F., St‐Onge, E., Sidhu, J., Chenot, Q., Petit, L., & Descoteaux, M. (2018) · Mathematics and visualization · doi:10.1007/978-3-319-73839-0_10 · Cited by 19
Self-citationThe biophysics of crossing fibers
Tournier, J.-D. (2010) · Diffusion MRI · doi:10.1093/med/9780195369779.003.0028 · Cited by 16
A Web-Based Atlas Combining MRI and Histology of the Squirrel Monkey Brain
Schilling, K. G., Gao, Y., Christian, M., Janve, V. A., Stepniewska, I., Landman, B. A., & Anderson, A. W. (2018) · Neuroinformatics · doi:10.1007/s12021-018-9391-z · Cited by 13
Self-citationA brain MRI atlas of the common squirrel monkey,Saimiri sciureus
Gao, Y., Schilling, K. G., Khare, S., Panda, S., Choe, A. S., Stepniewska, I., Li, X., Ding, Z., Anderson, A., & Landman, B. A. (2014) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2043589 · Cited by 12
Self-citationGao, Y., Parvathaneni, P., Schilling, K. G., Wang, F., Stepniewska, I., Xu, Z., Choe, A. S., Ding, Z., Gore, J. C., Chen, L. M., et al. (2016) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2217325 · Cited by 12
Self-citationThe biophysics of crossing fibres
Tournier, J. (2010) · Cited by 10
Inference and Visualization of Information Flow in the Visual Pathway Using dMRI and EEG
Deslauriers‐Gauthier, S., Lina, J., Butler, R., Bernier, P., Whittingstall, K., Rachid Deriche, B., & Descoteaux, M. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66182-7_58 · Cited by 6
Self-citationGrinberg, F., Maximov, I. I., Farrher, E., & Shah, N. J. (2017) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2017.08.007 · Cited by 5
Deslauriers‐Gauthier, S., Lina, J., Butler, R., Whittingstall, K., Bernier, P., & Descotaux, M. (2016) · Cited by 2
Research works printed but not linked: 1
- Maier-Hein, 2017, The challenge of mapping the human connectome based on diffusion tractography, Nat. Commun., 8, 1349, 10.1038/s41467-017-01285-x · DOI
Cited by
Sorted by citations
Standardizing workflows in imaging transcriptomics with the abagen toolbox
Markello, R. D., Arnatkevičiūtė, A., Poline, J., Fulcher, B. D., Fornito, A., & Mišić, B. (2021) · eLife · doi:10.7554/elife.72129 · Cited by 569
Comparing spatial null models for brain maps
Markello, R. D., & Mišić, B. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118052 · Cited by 330
The human connectome in Alzheimer disease — relationship to biomarkers and genetics
Yu, M., Sporns, O., & Saykin, A. J. (2021) · Nature Reviews Neurology · doi:10.1038/s41582-021-00529-1 · Cited by 265
Epilepsy and brain network hubs
Royer, J., Bernhardt, B. C., Larivière, S., Gleichgerrcht, E. L., Vorderwülbecke, B. J., Vulliémoz, S., & Bonilha, L. (2022) · Epilepsia · doi:10.1111/epi.17171 · Cited by 227
Theaud, G., Houde, J., Boré, A., Rheault, F., Morency, F. C., & Descoteaux, M. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116889 · Cited by 200
Self-citation
Metadata
- Authors
- Schilling, K. G., Nath, V., Hansen, C., Parvathaneni, P., Blaber, J., Gao, Y., Neher, P., Aydogan, D. B., Shi, Y., Ocampo-Pineda, M., Schiavi, S., Daducci, A., Girard, G., Barakovic, M., Rafael-Patino, J., Romascano, D., Rensonnet, G., Pizzolato, M., Bates, A., Fischi, E., Thiran, J.-P., Canales-Rodríguez, E. J., Huang, C., Zhu, H., Zhong, L., Cabeen, R., Toga, A. W., Rheault, F., Theaud, G., Houde, J.-C., Sidhu, J., Chamberland, M., Westin, C.-F., Dyrby, T. B., Verma, R., Rathi, Y., Irfanoglu, M. O., Thomas, C., Pierpaoli, C., Descoteaux, M., Anderson, A. W., & Landman, B. A.
- Affiliations
- Vanderbilt University (US)
- German Cancer Research Center (DE)
- University of Southern California (US)
- University of Verona (IT)
- École Polytechnique Fédérale de Lausanne (CH)
- Centre Hospitalier Universitaire Vaudois (CH)
- University of North Carolina at Chapel Hill (US)
- Southern Medical University (CN)
- Université de Sherbrooke (CA)
- Cardiff University (GB)
- Harvard University (US)
- Hvidovre Hospital (DK)
- University of Pennsylvania (US)
- National Institutes of Health (US)
- National Institute of Mental Health (US)
- Corresponding author
- Schilling, Kurt G.
- ORCID
- Schilling, Kurt G.
- Nath, Vishwesh
- Hansen, Colin
- Parvathaneni, Prasanna
- Gao, Yurui
- Neher, Peter
- Aydogan, Dogu Baran
- Shi, Yonggang
- Schiavi, Simona
- Daducci, Alessandro
- Girard, Gabriel
- Romascano, David
- Rensonnet, Gaëtan
- Pizzolato, Marco
- Bates, Alice
- Thiran, Jean-Philippe
- Canales-Rodríguez, Erick J.
- Huang, Chao
- Zhong, Liming
- Cabeen, Ryan
- Toga, Arthur W.
- Theaud, Guillaume
- Houde, Jean-Christophe
- Chamberland, Maxime
- Westin, Carl-Fredrik
- Dyrby, Tim B.
- Verma, Ragini
- Irfanoglu, M. Okan
- Pierpaoli, Carlo
- Descoteaux, Maxime
- Anderson, Adam W.
- Landman, Bennett A.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2019-01
- Volume
- 185
- Pages
- 1-11
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC6551229
- OpenAlex
- W2887072805
- Cited by
- 260
- References
- 65
- Citations by year
- 2026: 33 · 2025: 33 · 2024: 19 · 2023: 35 · 2022: 32 · 2021: 52 · 2020: 27 · 2019: 28
- FWCI
- 18.65
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- NeuroImage (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- ORCA Online Research @Cardiff (Cardiff University) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- IRIS (submittedVersion)
- Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) (submittedVersion, cc-by-nc-nd)
- SERVAL (Université de Lausanne) (submittedVersion)
- Funders
- National Institutes of Health (R01EB017230, T32EB001628)
- ViSE/VICTR VR3029
- National Center for Research Resources (UL1 RR024975-01)
- 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
- diffusion tractography, anatomical accuracy, white matter fiber reconstruction, diffusion MRI, structural brain connectivity, physical phantom
- MeSH terms
- Brain, Brain Mapping, Humans, Image Processing, Computer-Assisted, Neural Pathways, Diffusion Tensor Imaging
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
