Limits to anatomical accuracy of diffusion tractography using modern approaches
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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.
“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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Experimental — accuracy not yet measuredRecent tractography validation reviews suggest that the future of tractography connectomics hinges on incorporation of more advanced anatomical and microstructural priors to address geometric and other biases and make streamlines more quantitative and physically meaningful.
“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
The Discussion (‘Advancements needed’; page unavailable) recommends anatomical priors and microstructural information to improve tracking, and the Introduction identifies geometric and other sources of bias. Our copy does not establish the further claim that these additions would make streamlines more quantitative and physically meaningful. (LLM, conf=0.82)
Experimental — accuracy not yet measuredModern tractography approaches still produce high numbers of both false positive and false negative tracts.
“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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Advancements in diffusion MRI tractography for neurosurgery
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A connectomics-based taxonomy of mammals
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Network analysis shows decreased ipsilesional structural connectivity in glioma patients
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A multi-modal, asymmetric, weighted, and signed description of anatomical connectivity
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Modulation of Nigrofugal and Pallidofugal Pathways in Deep Brain Stimulation for Parkinson Disease
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Toward Principles of Brain Network Organization and Function
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Self-citationUltra-high field (10.5T) diffusion-weighted MRI of the macaque brain
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Adaptive phase correction of diffusion-weighted images
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Hierarchical microstructure informed tractography
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Self-citationRedundancy circuits of the commissural pathways in human and rhesus macaque brains
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Generative Sampling in Bundle Tractography using Autoencoders (GESTA)
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Microstructural properties within the amygdala and affiliated white matter tracts across adolescence
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Self-citationTowards reliable reconstruction of the mouse brain corticothalamic connectivity using diffusion MRI
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Tractography-Based Navigated TMS Language Mapping Protocol
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Tractography passes the test: Results from the diffusion-simulated connectivity (disco) challenge
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Self-citationAsymmetric fiber trajectory distribution estimation using streamline differential equation
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A critical review of connectome validation studies
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White Matter Microstructural Differences in Youth With Classical Congenital Adrenal Hyperplasia
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Self-citationDiffusion kurtosis imaging of white matter in bipolar disorder
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Structural underpinnings and long-term effects of resilience in Parkinson’s disease
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Real‐Time Tractography‐Assisted Neuronavigation for Transcranial Magnetic Stimulation
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Self-citationReinforcement Tractography: A Hybrid Approach for Robust Segmentation of Complex Fiber Bundles
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- 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 · 2018: 1
- 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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