When tractography meets tracer injections: a systematic study of trends and variation sources of diffusion-based connectivity
Open accessAbstract
Tractography is a powerful technique capable of non-invasively reconstructing the structural connections in the brain using diffusion MRI images, but the validation of tractograms is challenging due to lack of ground truth. Owing to recent developments in mapping the mouse brain connectome, high-resolution tracer injection-based axonal projection maps have been created and quickly adopted for the validation of tractography. Previous studies using tracer injections mainly focused on investigating the match in projections and optimal tractography protocols. Being a complicated technique, however, tractography relies on multiple stages of operations and parameters. These factors introduce large variabilities in tractograms, hindering the optimization of protocols and making the interpretation of results difficult. Based on this observation, in contrast to previous studies, in this work we focused on quantifying and ranking the amount of performance variation introduced by these factors. For this purpose, we performed over a million tractography experiments and studied the variability across different subjects, injections, anatomical constraints and tractography parameters. By using N-way ANOVA analysis, we show that all tractography parameters are significant and importantly performance variations with respect to the differences in subjects are comparable to the variations due to tractography parameters, which strongly underlines the importance of fully documenting the tractography protocols in scientific experiments. We also quantitatively show that inclusion of anatomical constraints is the most significant factor for improving tractography performance. Although this critical factor helps reduce false positives, our analysis indicates that anatomy-informed tractography still fails to capture a large portion of axonal projections.
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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 Aydogan et al., 2018
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Experimental — accuracy not yet measuredTractography has a gyral bias toward generating streamlines that terminate in gyri rather than sulci, reducing agreement with tracer and histology ground truth.
“Gyral bias in tractography is the bias towards generating streamlines that terminate in gyri rather than sulci (Schilling et al., 2018a; Wu et al., 2020b), which reduces the agreement of the initiations or terminations of fiber streamlines with known ground truth from tracer studies and histology (Aydogan et al., 2018; Budde and Annese, 2013).”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Aydogan et al., 2018
The Introduction (page unavailable) reports imperfect overlap between mouse tractograms and tracer injections, supporting the general point that tractography can disagree with tracer ground truth. But the paper does not attribute that disagreement to gyral bias or preferential termination in gyri rather than sulci. The Discussion (page unavailable) describes validation using histology images as future work, not a result of this study. (LLM, conf=0.79)
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 Aydogan et al., 2018
Opting against SIFT2 provides a more direct comparison between our results and similar studies that used Allen tracer data without reference to spatial embedding.
“This also provides a more direct comparison between our results and those of similar studies that have used the Allen tracer data to benchmark tractography performance without reference to the role of spatial embedding (Aydogan et al., 2018; Calabrese et al., 2015; Chen et al., 2015).”
From The role of spatial embedding in mouse brain networks constructed from diffusion tractography and tracer injections (2021) · cites this paper as Aydogan et al., 2018
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Self-citation7T multi-shell hybrid diffusion imaging (HYDI) for mapping brain connectivity in mice
Daianu, M., Jahanshad, N., Villalón‐Reina, J. E., Prasad, G., Jacobs, R. E., Barnes, S. R., Zloković, B. V., Montagne, A., & Thompson, P. M. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2081491 · Cited by 12 · see citing works
Self-citationBasser (2000) · doi:10.1002/1522-2594(200010)44:4
Flagged first, then by citations
Research works printed but not linked: 2
- Chen H Liu T Zhao Y Zhang T Li Y Li M Zhang H Kuang H Guo L Tsien JZ Liu T 2015b Optimization of large-scale mouse brain connectome via joint evaluation of DTI and neuron tracing data NeuroImage 115 202 213 10.1016/j.neuroimage.2015.04.050 25953631 PMC4461481 · DOI · Paper page
- Chen H Liu T Zhao Y Zhang T Li Y Li M Zhang H Kuang H Guo L Tsien JZ Liu T 2015c Optimization of large-scale mouse brain connectome via joint evaluation of DTI and neuron tracing data Neuroimage 115 202 213 10.1016/j.neuroimage.2015.04.050 25953631 PMC4461481 · DOI · Paper page
Cited by
Sorted by citations
Zhang, F., Daducci, A., He, Y., Schiavi, S., Seguin, C., Smith, R. E., Yeh, C.-H., Zhao, T., & O’Donnell, L. J. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2021.118870 · Cited by 302 · see citing works
“, 2020b), which reduces the agreement of the initiations or terminations of fiber streamlines with known ground truth from tracer studies and histology (Aydogan et al., 2018; Budde and Annese, 2013).”
BackgroundLimits to anatomical accuracy of diffusion tractography using modern approaches
Schilling, K. G., Nath, V., Hansen, C., Parvathaneni, P., Blaber, J., Gao, Y., Neher, P., Aydogan, D. B., Shi, Y., Ocampo-Pineda, M., et al. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2018.10.029 · Cited by 260 · see citing works
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Towards reliable reconstruction of the mouse brain corticothalamic connectivity using diffusion MRI
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- Authors
- Aydogan, D. B., Jacobs, R., Dulawa, S., Thompson, S. L., Francois, M. C., Toga, A. W., Dong, H., Knowles, J. A., & Shi, Y.
- Affiliations
- University of Southern California (US)
- University of California San Diego (US)
- Corresponding author
- Aydogan, Dogu Baran
- ORCID
- Venue
- Brain Structure and Function
- Published
- 2018-04-16
- Volume
- 223
- Issue
- 6
- Pages
- 2841-2858
- ISSN
- 1863-2653 (ISSN-L)
- 1863-2661
- PMC
- PMC5997540
- OpenAlex
- W2801933823
- Cited by
- 72
- References
- 89
- Citations by year
- 2026: 9 · 2025: 3 · 2024: 7 · 2023: 9 · 2022: 10 · 2021: 16 · 2020: 12 · 2018: 6
- FWCI
- 5.87
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Brain Structure and Function (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Eye Institute (U01EY025864)
- National Institute of Biomedical Imaging and Bioengineering (K01EB013633, P41EB015922, R01EB022744)
- National Institute on Aging (P50AG005142, U01AG051218)
- 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, diffusion MRI, tracer injection, anatomical constraints, structural connectivity, false positives
- MeSH terms
- Dependovirus, Algorithms, Analysis of Variance, Animals, Brain, Brain Mapping, Diffusion, Female, Mice, Inbred C57BL, Neural Pathways, ROC Curve, Imaging, Three-Dimensional, Mice, Diffusion Tensor Imaging, Connectome
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.
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