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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.
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“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
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“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
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“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
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Cites
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Tractography-based connectomes are dominated by false-positive connections
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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
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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
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