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-citation
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
Items 101–200 of 259
Predicting the Extent of Resection of Motor-Eloquent Gliomas Based on TMS-Guided Fiber Tracking
Belotti, F., Tuncer, M. S., Rosenstock, T., Ivren, M., Vajkoczy, P., & Picht, T. (2021) · Brain Sciences · doi:10.3390/brainsci11111517 · Cited by 13
Shams, B., Wang, Z., Roine, T., Aydogan, D. B., Vajkoczy, P., Lippert, C., Picht, T., & Fekonja, L. S. (2022) · Brain Communications · doi:10.1093/braincomms/fcac141 · Cited by 14
Self-citationMenzel, M., Gräßel, D., Rajković, I., Zeineh, M., & Georgiadis, M. (2023) · eLife · doi:10.7554/elife.84024 · Cited by 13
Gatto, R. G., Martin, P. R., Utianski, R. L., Duffy, J. R., Clark, H. M., Botha, H., Machulda, M. M., Josephs, K. A., & Whitwell, J. L. (2023) · Cortex · doi:10.1016/j.cortex.2023.08.019 · Cited by 15
Tiefenbach, J., Lu, V. M., Metzler, A., Palejwala, A. H., Haider, S. A., Ivan, M. E., Komotar, R. J., & Shah, A. H. (2024) · Neurosurgical FOCUS · doi:10.3171/2023.11.focus23649 · Cited by 13
Evaluation of tractogram filtering methods using human-like connectome phantoms
Sarwar, T., Ramamohanarao, K., Daducci, A., Schiavi, S., Smith, R. E., & Zalesky, A. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120376 · Cited by 15
Self-citationTwo cortical representations of voice control are differentially involved in speech fluency
Neef, N. E., Primaßin, A., von Gudenberg, A. W., Dechent, P., Riedel, C. H., Paulus, W., & Sommer, M. (2021) · Brain Communications · doi:10.1093/braincomms/fcaa232 · Cited by 13
Liang, Z., Arefin, T. M., Lee, C. H., & Zhang, J. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.119999 · Cited by 12
Jelescu, I., Grussu, F., Ianuș, A., Hansen, B., Barrett, R. L. C., Aggarwal, M., Michielse, S., Nasrallah, F. A., Syeda, W. T., Wang, N., et al. (2025) · Magnetic Resonance in Medicine · doi:10.1002/mrm.30429 · Cited by 21
Self-citationWade, R. G., Teh, I., Andersson, G., Yeh, F.-C. F., Wiberg, M., & Bourke, G. (2021) · Scientific Reports · doi:10.1038/s41598-020-79840-8 · Cited by 11
Kancheva, I. K., Buma, F. E., Kwakkel, G., Kancheva, A. K., Ramsey, N. F., & Raemaekers, M. A. H. (2022) · NeuroImage Clinical · doi:10.1016/j.nicl.2022.102945 · Cited by 13
A review of diffusion MRI in mood disorders: mechanisms and predictors of treatment response
Al‐Sharif, N., Zavaliangos‐Petropulu, A., & Narr, K. L. (2024) · Neuropsychopharmacology · doi:10.1038/s41386-024-01894-3 · Cited by 14
Imaging the structural connectome with hybrid MRI-microscopy tractography
Zhu, S., Huszár, I. N., Cottaar, M., Daubney, G., Eichert, N., Hanayik, T., Khrapitchev, A. A., Mars, R. B., Mollink, J., Sallet, J., et al. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103498 · Cited by 11
Diffusion tensor imaging of superficial prefrontal white matter in healthy aging
Pietrasik, W., Cribben, I., Olsen, F., & Malykhin, N. V. (2022) · Brain Research · doi:10.1016/j.brainres.2022.148152 · Cited by 11
Normative connectomes and their use in DBS
Elias, G. J. B., Germann, J., Loh, A., Boutet, A., Taha, A., Wong, E. H. Y., Parmar, R., & Lozano, A. M. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-821861-7.00014-2 · Cited by 12
Learning white matter subject‐specific segmentation from structural MRI
Yang, Q., Hansen, C. B., Cai, L. Y., Rheault, F., Lee, H. H., Bao, S., Chandio, B. Q., Williams, O. A., Resnick, S. M., Garyfallidis, E., et al. (2022) · Medical Physics · doi:10.1002/mp.15495 · Cited by 11
Self-citationCabeen, R. P., Glass, L., ERWIN, J. M., Hof, P. R., Toga, A. W., & Allman, J. M. (2020) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2020.101941 · Cited by 10
Self-citationCaminiti, R., Girard, G., Battaglia‐Mayer, A., Borra, E., Schito, A., Innocenti, G. M., & Luppino, G. (2021) · eNeuro · doi:10.1523/eneuro.0102-21.2021 · Cited by 10
Self-citationIn vivo imaging in experimental spinal cord injury – Techniques and trends
Hubertus, V., Meyer, L., Roolfs, L. T., Waldmann, L., Nieminen-Kelhä, M., Fehlings, M. G., & Vajkoczy, P. (2021) · Brain and Spine · doi:10.1016/j.bas.2021.100859 · Cited by 10
Bridging functional and anatomical neural connectivity through cluster synchronization
Baruzzi, V., Lodi, M., Sorrentino, F., & Storace, M. (2023) · Scientific Reports · doi:10.1038/s41598-023-49746-2 · Cited by 9
Gatto, R. G., Pham, N. T. T., Duffy, J. R., Clark, H. M., Utianski, R. L., Botha, H., Machulda, M. M., Lowe, V. J., Schwarz, C. G., Jack, C. R., et al. (2024) · Human Brain Mapping · doi:10.1002/hbm.26704 · Cited by 10
Ciolac, D., Luessi, F., González‐Escamilla, G., Koirala, N., Riedel, C. H., Fleischer, V., Bittner, S., Krämer, J., Meuth, S. G., Muthuraman, M., et al. (2019) · Frontiers in Immunology · doi:10.3389/fimmu.2019.01779 · Cited by 9
CHIASM, the human brain albinism and achiasma MRI dataset
Puźniak, R. J., McPherson, B., Ahmadi, K., Herbik, A., Kaufmann, J., Liebe, T., Gouws, A. D., Morland, A. B., Gottlob, I., Hoffmann, M. B., et al. (2021) · Scientific Data · doi:10.1038/s41597-021-01080-w · Cited by 8
Real-time tractography-assisted neuronavigation for TMSPreprint
Aydogan, D. B., Souza, V. H., Matsuda, R. H., Lioumis, P., & Ilmoniemi, R. J. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.09.531565 · Cited by 9
Self-citationTrinkle, S., Foxley, S., Wildenberg, G., Kasthuri, N., & La Rivière, P. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118576 · Cited by 8
Deep brain stimulation for Tourette syndrome: modulation of the limbic-motor interface network
Avecillas-Chasin, J. M., Galbiati, T. F., Porta, M., & Servello, D. (2023) · Journal of neurosurgery · doi:10.3171/2023.10.jns231317 · Cited by 8
Dhanaraj, V., Rolfe, N. W., Dadario, N. B., Dhaliwal, J. K., Samuel, N., Hormovas, J., Young, I. M., Odonkor, C. A., Yeung, J. T., Teo, C., et al. (2025) · Brain Communications · doi:10.1093/braincomms/fcaf343 · Cited by 9
Tractography indicates lateralized differences between trigeminal and olfactory pathways
Thaploo, D., Joshi, A., Georgiopoulos, C., Warr, J., & Hummel, T. R. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119518 · Cited by 8
Petersen, M. S., & McIntyre, C. C. (2023) · Brain Connectivity · doi:10.1089/brain.2022.0068 · Cited by 8
Fibre orientation atlas guided rapid segmentation of white matter tracts
Young, F., Aquilina, K., Seunarine, K. K., Mancini, L., Clark, C. A., & Clayden, J. D. (2024) · Human Brain Mapping · doi:10.1002/hbm.26578 · Cited by 7
Diffusion Kurtosis Imaging Fiber Tractography of Major White Matter Tracts in Neurosurgery
Bopp, M. H. A., Emde, J., Carl, B., Nimsky, C., & Saß, B. (2021) · Brain Sciences · doi:10.3390/brainsci11030381 · Cited by 8
Morales, H. (2021) · Seminars in Ultrasound CT and MRI · doi:10.1053/j.sult.2021.07.005 · Cited by 9
Magat, J., Yon, M., Bihan-Poudec, Y., & Ozenne, V. (2022) · PLoS ONE · doi:10.1371/journal.pone.0271279 · Cited by 8
Presurgical Executive Functioning in Low-Grade Glioma Patients Cannot Be Topographically Mapped
Landers, M. J. F., Smolders, L., Rutten, G.-J. M., Sitskoorn, M. M., Mandonnet, E., & Baene, W. D. (2023) · Cancers · doi:10.3390/cancers15030807 · Cited by 7
Valdes, P. A., Ng, S., Bernstock, J., & Duffau, H. (2023) · Acta Neurochirurgica · doi:10.1007/s00701-023-05626-2 · Cited by 6
Supervised tractogram filtering using Geometric Deep Learning
Astolfi, P., Verhagen, R., Petit, L., Olivetti, E., Sarubbo, S., Masci, J., Boscaini, D., & Avesani, P. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102893 · Cited by 9
Chamberland, M., Winter, M., Brice, T. A. W., Jones, D. K., & Tallantyre, E. C. (2021) · Mathematics and visualization · doi:10.1007/978-3-030-73018-5_18 · Cited by 7
Self-citationThalamocortical disconnection affects the somatic marker and social cognition: a case report
Serra, L., Bruschini, M., Ottaviani, C., Domenico, C. D., Fadda, L., Caltagirone, C., Cercignani, M., Carlesimo, G. A., & Bozzali, M. (2019) · Neurocase · doi:10.1080/13554794.2019.1599025 · Cited by 7
Wu, D., Lei, J., Xie, H., Dong, J., & Burd, I. (2020) · Brain Imaging and Behavior · doi:10.1007/s11682-019-00246-w · Cited by 8
Clinical connectomics: Mapping structural brain networks in disease
Zalesky, A., & Biase, M. A. D. (2026) · Neuron · doi:10.1016/j.neuron.2026.02.007 · Cited by 7
Yao, S., Yang, R., Du, C., Jiang, C., Wang, Y., Peng, C., & Bai, H. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1089139 · Cited by 6
GeoLab: Geometry-Based Tractography Parcellation of Superficial White Matter
Vindas, N., Labra, N., Zhang, F., Xue, T., O’Donnell, L. J., & Mangin, J. (2023) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi53787.2023.10230336 · Cited by 6
Anisotropy Across Fields and Scales
Özarslan, E., Schultz, T., Zhang, E., & Fuster, A. (2021) · Mathematics and visualization · doi:10.1007/978-3-030-56215-1 · Cited by 6
Challenges for Tractogram Filtering
Jörgens, D., Descoteaux, M., & Moreno, R. (2021) · Mathematics and visualization · doi:10.1007/978-3-030-56215-1_7 · Cited by 9
Self-citationSchilling, K. G., Li, M., Rheault, F., Gao, Y., Cai, L. Y., Zhao, Y., Xu, L., Ding, Z., Anderson, A. W., Landman, B. A., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.02.14.528557 · Cited by 6
Self-citationDeep learning-based diffusion MRI tractography: Integrating spatial and anatomical information
Yang, Y., Yuan, Y., Ren, B., Wu, Y., Feng, Y., & Zhang, X. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121314 · Cited by 6
Biological substrates of structure-function coupling in brain networks
Fotiadis, P., Arnsten, A. F. T., Parkes, L., Satterthwaite, T. D., Shinohara, R. T., & Bassett, D. S. (2026) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2026.106581 · Cited by 5
Weak and unstable prediction of personality from the structural connectome
Rauland, A., Jung, K., Satterthwaite, T. D., Cieslak, M., Reetz, K., Eickhoff, S. B., & Popovych, O. V. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00416 · Cited by 5
Gatto, R. G., Youssef, H., Pham, N. T. T., Ali, F., Clark, H. M., Stierwalt, J. A. G., Stephens, Y. C., Machulda, M. M., Josephs, K. A., & Whitwell, J. L. (2025) · Journal of Neuroimaging · doi:10.1111/jon.70062 · Cited by 5
Hamamcı, A. (2019) · Neuroinformatics · doi:10.1007/s12021-019-09425-y · Cited by 5
Tomita, H., Kamagata, K., Andica, C., Uchida, W., Fukuo, M., Waki, H., Sugano, H., Tange, Y., Mitsuhashi, T., Lukies, M. W., et al. (2021) · Journal of Neuroscience Research · doi:10.1002/jnr.24912 · Cited by 4
Wu, Z., Peng, Y., Xu, D. R., Hong, M., & Zhang, Y. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.103384 · Cited by 5
Sitek, K. R., Calabrese, E., Johnson, G. A., Ghosh, S., & Chandrasekaran, B. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.751595 · Cited by 7
Assessing informative tract segmentation and nTMS for pre-operative planning
Lucena, O., Lavrador, J. P., Irzan, H., Semedo, C., Borges, P., Vergani, F., Granados, A., Sparks, R. E., Ashkan, K., & Sebastien Ourselin, A. (2023) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2023.109933 · Cited by 5
Reconstructing the human brain’s wiring diagram from axons up
Heilbronner, S. R., Reid, R. C., Casta, T. K., & Pestilli, F. (2025) · Trends in Neurosciences · doi:10.1016/j.tins.2025.11.001 · Cited by 4
In-vivo data-driven parcellation of Heschl’s gyrus using structural connectivity
Lee, H., Byeon, K., Park, B., Lee, S. H., & Park, H. (2022) · Scientific Reports · doi:10.1038/s41598-022-15083-z · Cited by 4
Neurodevelopmental Outcomes in Children with Complex Congenital Heart Disease
Bragg, J. B. (2019) · Current Pediatrics Reports · doi:10.1007/s40124-019-00189-2 · Cited by 4
Tanner, J., Faskowitz, J., Teixeira, A. S., Seguin, C., Coletta, L., Gozzi, A., Mišić, B., & Betzel, R. F. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.12.19.519033 · Cited by 4
Jaatela, J., Aydogan, D. B., Nurmi, T., Vallinoja, J., & Piitulainen, H. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab444 · Cited by 3
Self-citationStreamline tractography of the fetal brain in utero with machine learning
Liu, W., Calixto, C. A., Warfield, S. K., & Karimi, D. (2025) · Imaging Neuroscience · doi:10.1162/imag_a_00537 · Cited by 3
The scientific value of tractography: accuracy vs usefulness
Rheault, F., Mayberg, H. S., de Schotten, M. T., Roebroeck, A. F., & Forkel, S. J. (2025) · Brain Structure and Function · doi:10.1007/s00429-025-02921-9 · Cited by 3
Self-citationMultiscale gradients of corticopontine structural connectivity
Rousseau, P.-N., Bazin, P., & Steele, C. J. (2025) · Scientific Reports · doi:10.1038/s41598-025-00886-7 · Cited by 3
Asymmetric Cingulum Bundle Connectivity Is Modulated by Paracingulate Sulcus Morphology
Danstrom, I. A., Adkinson, J. A., Robinson, M. E., Lin, L., Maheshwari, A., Shofty, B., Banks, G. P., Hasen, M. A., Sheth, S. A., Goldman, A. M., et al. (2025) · Human Brain Mapping · doi:10.1002/hbm.70230 · Cited by 3
Bublíková, I., Mareček, S., Krajča, T., Malá, C., Dušek, P., & Krupička, R. (2025) · Applied Sciences · doi:10.3390/app15147902 · Cited by 3
Mujika, K. M., Méndez, J. A. J., & de Miguel, A. F. (2019) · Journal of Medical Systems · doi:10.1007/s10916-019-1242-4 · Cited by 3
Connections of the sheep basolateral amygdala: A diffusion tensor imaging study
Graïc, J., Tagliavia, C., Salamanca, G., Gerussi, T., Grandis, A., Cozzi, B., & Bombardi, C. (2023) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2023.109883 · Cited by 3
A Reproducible Pipeline for Parcellation of the Anterior Limb of the Internal Capsule
Wong, K. S., Braun, H. C., Liu, Z., Bullock, D. N., Palnitkar, T., Patriat, R., Chandrasekaran, J., Brenny, S., Johnson, M. D., Widge, A. S., et al. (2024) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2024.07.008 · Cited by 5
Geodesic Uncertainty in Diffusion MRI
Sengers, R., Florack, L. M. J., & Fuster, A. (2021) · Frontiers in Computer Science · doi:10.3389/fcomp.2021.718131 · Cited by 3
The Case for Optimized Edge-Centric Tractography at Scale
Moon, J. Y., Mukherjee, P., Madduri, R., Markowitz, A. J., Cai, L. T., Palacios, E. M., Manley, G. T., & Bremer, P. (2022) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2022.752471 · Cited by 3
SIFT in pathological connectomes: Follow‐up response to Smith and colleagues
Zalesky, A., Sarwar, T., & Ramamohanarao, K. (2020) · Magnetic Resonance in Medicine · doi:10.1002/mrm.28412 · Cited by 3
A precise atlas of the human subcortexPreprint
Friedrich, H., Sahin, I. A., Alho, E. J. L., Rajamani, N., Holanda, V. M., Oxenford, S., Brammerloh, M., Mustin, M., Zvarova, P., Krüger, L., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.13.705755 · Cited by 3
Kahana, N., Korn, A., Friedmann, N., Richetta, C., Gurevitch, G., Artzi, M., Keren, N., Ram, Z., Shahar, T., & Grossman, R. (2025) · Journal of Neurosurgery · doi:10.3171/2025.1.jns241036 · Cited by 1
A thousand ways to tailor your tractography-based connectome
Griffa, A., Rheault, F., Daducci, A., & Zalesky, A. (2025) · Brain Structure and Function · doi:10.1007/s00429-025-02998-2 · Cited by 2
Self-citationAmandola, M., Kim, M. E., Rheault, F., Landman, B. A., & Schilling, K. (2026) · Human Brain Mapping · doi:10.1002/hbm.70520 · Cited by 2
Self-citationCaiani, G., Arrigoni, E., Pisoni, A., & Fiocchi, S. (2026) · Frontiers in Neuroscience · doi:10.3389/fnins.2026.1749851 · Cited by 1
Akimoto, H., Suzuki, H., Kan, S., Funaba, M., Nishida, N., Fujimoto, K., Ikeda, H., Yonezawa, T., Ikushima, K., Shimizu, Y., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-53051-x · Cited by 2
Singer, R., Oganezova, I., Hu, W., Ding, Y., Papaioannou, A., de Groot, H. J. M., Spaink, H. P., & Alia, A. (2024) · Molecules · doi:10.3390/molecules29194637 · Cited by 2
Tractography validation Part 1: Foundations, numerical simulations, and phantom models
Dyrby, T. B., Fieremans, E., Rheault, F., Anderson, A. W., Palombo, M., Sarubbo, S., Neher, P., & Schilling, K. G. (2025) · Elsevier eBooks · doi:10.1016/b978-0-12-818894-1.00017-3 · Cited by 2
Self-citationHarnessing Advanced Tractography in Neurosurgical Practice
Beyh, A., Ohlerth, A., & Forkel, S. J. (2025) · Springer eBooks · doi:10.1007/978-3-031-97155-6_21 · Cited by 2
Neural fiber prediction with deep learning
Koryciński, M., Ciecierski, K. A., & Niewiadomska‐Szynkiewicz, E. (2022) · IEEE International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob) · doi:10.1109/wimob55322.2022.9941562 · Cited by 2
Demir, A., & Rosas, H. D. (2023) · Neuroimage Reports · doi:10.1016/j.ynirp.2023.100174 · Cited by 2
Tractography in Curvilinear Coordinates
Hussain, U., Baron, C. A., & KHAN, A. R. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.716538 · Cited by 2
Tractography in the presence of white matter lesions in multiple sclerosisPreprint
Lipp, I., Parker, G. D., Tallantyre, E. C., Goodall, A., Grama, S., Patitucci, E., Heveron, P., Tomassini, V., & Jones, D. K. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/559708 · Cited by 2
Personalized circuit modeling captures variation in cortical functional connectivityPreprint
Cooper, R. A., Demirtaş, M., Burt, J. B., Howell, A. M., Ji, J., Repovš, G., Sotiropoulos, S. N., Anticevic, A., & Murray, J. D. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.02.19.581085 · Cited by 2
Thalamo-insular cortex connections in the rat and human
Özkan, M., Altınöz, D., Erkan, E., Güneş, Y. C., Algın, O., & Çavdar, S. (2024) · Neuroscience Letters · doi:10.1016/j.neulet.2024.138111 · Cited by 1
Quantitative tractography: joys and sorrows
Daducci, A., & Schiavi, S. (2025) · Brain Structure and Function · doi:10.1007/s00429-025-02939-z · Cited by 1
Self-citationJoshi, A., Li, H., Parikh, N. A., & He, L. (2026) · Frontiers in Neuroscience · doi:10.3389/fnins.2025.1734498 · Cited by 1
Lindsey, H. M., Rath, J. F., Bushnik, T. L., Flanagan, S. R., Lazar, M., & Voelbel, G. T. (2026) · Journal of Neurotrauma · doi:10.1177/08977151251414085 · Cited by 1
Filipiak, P., Shepherd, T. M., Clarke, K., Ressa, G., Placantonakis, D. G., Boada, F. E., & Baete, S. H. (2026) · Magnetic Resonance in Medicine · doi:10.1002/mrm.70314 · Cited by 2
Altered structural connectivity in epilepsy with myoclonic atonic seizures
Pozeg, P., Korff, C. M., Fluss, J., Denervaud, S., Maeder, J., Hagmann, P., & Lebon, S. (2026) · Epilepsy Research · doi:10.1016/j.eplepsyres.2026.107816 · Cited by 1
Altieri, R., Ugga, L., Bianconi, A., Caneva, S., Caranci, F., Cirillo, G., Cofano, F., Corvino, S., DIVITIIS, O. D., Pepa, G. M. D., et al. (2026) · Journal of Neuro-Oncology · doi:10.1007/s11060-026-05691-4 · Cited by 1
Jammal, R. E., Suzuki, H., Mattar, L. S., Hamre, T. A., Soubra, S. S., Ryan, M. A., Mathura, R. K., Mathew, S. J., Allawala, A. B., Storch, E. A., et al. (2026) · Deep Brain Stimulation · doi:10.1016/j.jdbs.2026.08.002 · Cited by 1
Learning 3D White Matter Microstructure from 2D Histology
Nath, V., Schilling, K. G., Remedios, S. W., Bayrak, R. G., Gao, Y., Blaber, J. A., Huo, Y., Landman, B. A., & Anderson, A. W. (2019) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2019.8759388 · Cited by 1
Self-citationAzor, A. M., Sharp, D. J., Jolly, A., Bourke, N., & Hellyer, P. J. (2022) · PLoS ONE · doi:10.1371/journal.pone.0268233 · Cited by 1
Yu, T., Li, Y., Kim, M. E., Gao, C., Yang, Q., Cai, L. Y., Resnick, S. M., Beason‐Held, L. L., Moyer, D., Schilling, K. G., et al. (2024) · SPIE Medical Imaging: Image Processing · doi:10.1117/12.3006286 · Cited by 1
Self-citationMachine learning in tractography
Neher, P., Poulin, P., Jörgens, D., Reisert, M., Benou, I., & Maier-Hein, K. H. (2025) · Elsevier eBooks · doi:10.1016/b978-0-12-818894-1.00032-x · Cited by 2
Self-citationA Wasserstein-Space-Based Framework for Processing Fiber Orientation Geometry in Diffusion MRI
Nie, X., & Shi, Y. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3595367 · Cited by 1
Self-citationKim, D., Chae, J., & Han, Y. (2019) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22325 · Cited by 1
Learning Global Brain Microstructure Maps Using Trainable Sparse Encoders
Rafael‐Patiño, J., Baraković, M., Girard, G., Daducci, A., & Thiran, J. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8803733 · Cited by 1
Self-citationDeterministic Tractography Analysis of Rat Brain Using SIGMA Atlas in 9.4T MRI
Im, S.-J., Suh, J., Shim, J., & Baek, H. (2021) · Brain Sciences · doi:10.3390/brainsci11121656 · Cited by 2
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