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Microstructure Informed Tractography: Pitfalls and Open Challenges
Open accessAbstract
One of the major limitations of diffusion MRI tractography is that the fiber tracts recovered by existing algorithms are not truly quantitative. Local techniques for estimating more quantitative features of the tissue microstructure exist, but their combination with tractography has always been considered intractable. Recent advances in local and global modeling made it possible to fill this gap and a number of promising techniques for microstructure informed tractography have been suggested, opening new and exciting perspectives for the quantification of brain connectivity. The ease-of-use of the proposed solutions made it very attractive for researchers to include such advanced methods in their analyses; however, this apparent simplicity should not hide some critical open questions raised by the complexity of these very high-dimensional problems, otherwise some fundamental issues may be pushed into the background. The aim of this article is to raise awareness in the diffusion MRI community, notably researchers working on brain connectivity, about some potential pitfalls and modeling choices that make the interpretation of the outcomes from these novel techniques rather cumbersome. Through a series of experiments on synthetic and real data, we illustrate practical situations where erroneous and severely biased conclusions may be drawn about the connectivity if these pitfalls are overlooked, like the presence of partial/missing/duplicate fibers or the critical importance of the diffusion model adopted. Microstructure informed tractography is a young but very promising technology, and by acknowledging its current limitations as done in this paper, we hope our observations will trigger further research in this direction and new ideas for truly quantitative and biologically meaningful analyses of the connectivity.
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“Structural connectome analyses are most convincing when integrated with neuroanatomical knowledge, functional data, and careful control of tractography biases (Assaf et al., 2019; Daducci et al., 2016).”
From 40 Years of diffusion MRI in the brain: From history to emerging frontiers (2026) · cites this paper as Daducci et al., 2016
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“Higher angular-resolution methods, better distortion correction, and anatomically informed tractography improve matters, yet they do not eliminate the inverse problem (Daducci et al., 2016; Tournier et al., 2011).”
From 40 Years of diffusion MRI in the brain: From history to emerging frontiers (2026) · cites this paper as Daducci et al., 2016
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“despite the fact that white matter fibers synapse in the gray matter (Daducci et al., 2016)”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Daducci et al., 2016
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“Tractography, therefore, visualizes a model of anatomical connectivity constrained by diffusion data, not anatomy itself (Daducci et al., 2016; Tournier et al., 2011).”
From 40 Years of diffusion MRI in the brain: From history to emerging frontiers (2026) · cites this paper as Daducci et al., 2016
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Cites
Donoho, D. L. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2006.871582 · Cited by 23,994
Donoho, D. L. (2004) · Cited by 16,703
Candes, E. J., Romberg, J., & Tao, T. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2005.862083 · Cited by 15,639
N4ITK: Improved N3 Bias Correction
Tustison, N. J., Avants, B., Cook, P. A., Zheng, Y., Egan, A., Yushkevich, P. A., & Gee, J. C. (2010) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2010.2046908 · Cited by 6,119
The basis of anisotropic water diffusion in the nervous system—A technical review.
Beaulieu, C. (2002) · NMR in Biomedicine · doi:10.1002/nbm.782 · Cited by 4,628
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
NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain
Zhang, H., Schneider, T., Wheeler-Kingshott, C. A., & Alexander, D. C. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.03.072 · Cited by 3,288
Characterization and propagation of uncertainty in diffusion-weighted MR imaging
Behrens, T. E. J., Woolrich, M. W., Jenkinson, M., Johansen‐Berg, H., Nunes, R. G., Clare, S., Matthews, P. M., Brady, J. M., & Smith, S. M. (2003) · Magnetic Resonance in Medicine · doi:10.1002/mrm.10609 · Cited by 3,038
The Human Connectome Project: A data acquisition perspective
Essen, D. C. V., Uǧurbil, K., Auerbach, E. J., Barch, D. M., Behrens, T. E. J., Bucholz, R. D., Chang, A., Chen, L., Corbetta, M., Curtiss, S. W., et al. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.02.018 · Cited by 2,638
White matter integrity, fiber count, and other fallacies: The do's and don'ts of diffusion MRI
Jones, D. K., Knösche, T. R., & Turner, R. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2012.06.081 · Cited by 2,402 · cited 6×
“…the connection strength between the four regions R1, . . . ,R4 is quantified by the fiber count (Jones et al., 2012), then a reduction inR1 ↔ R2 connectivity is observed between the two sessions (from 2 to 1), and an increase in R3 ↔ R4 (from 1 to 3), as the dashed tracts do not connect and so do…”
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Bain, J. S., Filo, S., & Mezer, A. (2019) · Brain Structure and Function · doi:10.1007/s00429-019-01952-3 · Cited by 4
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
Mapping short association fibre connectivity up to V3 in the human brain in vivo
Attar, F. M., Kirilina, E. P., Haenelt, D., Trampel, R., Pine, K., Edwards, L., & Weiskopf, N. (2024) · Cerebral Cortex · doi:10.1093/cercor/bhae279 · Cited by 3
Self-supervised segmentation and characterization of fiber bundles in anatomic tracing data
Sundaresan, V., Lehman, J. F., Maffei, C., Haber, S. N., & Yendiki, A. (2025) · Imaging Neuroscience · doi:10.1162/imag_a_00514 · Cited by 2
Bounding tractogram redundancy
Persson, S., & Moreno, R. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1403804 · Cited by 2
Wu, Z., Yu, J., Chen, X., & Shen, J. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10703-3 · Cited by 2
Current challenges and opportunities for tractography
Rheault, F., Poulin, P., Caron, A. V., St‐Onge, E., Schilling, K. G., Petit, L., Dell’Acqua, F., Leemans, A., & Descoteaux, M. (2025) · Elsevier eBooks · doi:10.1016/b978-0-12-818894-1.00037-9 · Cited by 2
Self-citationTractography validation Part 2: The use of anatomical model systems and measures for validation
Dyrby, T. B., Sarubbo, S., Rheault, F., Fieremans, E., Anderson, A. W., Palombo, M., Neher, P., Rockland, K. S., & Schilling, K. G. (2025) · Elsevier eBooks · doi:10.1016/b978-0-12-818894-1.00020-3 · Cited by 4
Durantel, T., Girard, G., Caruyer, E., Commowick, O., & Coloigner, J. (2025) · PLoS ONE · doi:10.1371/journal.pone.0304449 · Cited by 2
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Frigo, M., Costantini, I., Rachid Deriche, B., & Deslauriers‐Gauthier, S. (2019) · Mathematics and visualization · doi:10.1007/978-3-030-05831-9_26 · Cited by 2
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Persson, S., Wan, X., & Rodrigo Moreno, A. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-86920-4_5 · Cited by 1
Leysen, S., Radwan, A., Maes, F., Sunaert, S. G., & Christiaens, D. (2025) · Imaging Neuroscience · doi:10.1162/imag.a.95 · Cited by 1
TIME: Tractography-Informed myelin estimation
Bosticardo, S., Battocchio, M., Ocampo‐Pineda, M., Cagol, A., Lu, P., Ruberte, E., Siebenborn, N. D. O. S., Chen, X., Melie‐García, L., Weigel, M., et al. (2025) · NeuroImage Clinical · doi:10.1016/j.nicl.2025.103878 · Cited by 1
Self-citationHabijan, M., Krpić, Z., Perić, J., & Galić, I. (2025) · Electronics · doi:10.3390/electronics15010076 · Cited by 1
Li, Y., Zhang, W., Zhu, X., Wu, Y., Yogesh Rathi, A., O’Donnell, L. J., & Zhang, F. (2026) · Medical Image Analysis · doi:10.1016/j.media.2026.103967 · Cited by 1
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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
Morez, J., Luca, A. D., Pietsch, M., Christiaens, D., Baete, S. H., Reisert, M., & Jeurissen, B. (2025) · Elsevier eBooks · doi:10.1016/b978-0-12-818894-1.00008-2 · Cited by 1
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Kumar, K. (2018) · Espace ÉTS (ETS) · doi:10.13140/rg.2.2.12193.71525 · Cited by 1
MRI analysis of brain connectivity in a mouse model of Alzheimer’s disease
Degiorgis, L. (2019) · theses.fr (ABES) · doi:10.70675/5334d86bz9cc5z451cz865fzcf91895a5400 · Cited by 1
40 Years of diffusion MRI in the brain: From history to emerging frontiers
Le Bihan, D. (2026) · Imaging Neuroscience · doi:10.1162/imag.a.1365
“Structural connectome analyses are most convincing when integrated with neuroanatomical knowledge, functional data, and careful control of tractography biases ( Assaf et al., 2019; Daducci et al., 2016).”
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Cao, Z., Jin, E., Zhou, S., Wu, Y., Li, Y., & Feng, Y. (2018) · IEEE Data Driven Control and Learning Systems Conference (DDCLS) · doi:10.1109/ddcls.2018.8515941
MRI-based Measurement of Structural and Functional Connectivity in Health and Disease
Sinke, M. R. T. (2020) · Utrecht University Repository (Utrecht University) · doi:10.33540/77
Cottaar, M., & Bastiani, M. (2021) · Advances in magnetic resonance technology and applications · doi:10.1016/b978-0-12-822479-3.00032-4
Mujika, K. M. (2023) · Gredos (University of Salamanca) · doi:10.14201/gredos.153253
Shirazinodeh, A., Faraji, H., Javidi, S. S., Jafari, A. H., Nazem-Zadeh, M.-R., & Rad, H. S. (2023) · Journal of Biomedical Physics and Engineering · doi:10.31661/jbpe.v0i0.2108-1387
Luca, A. D., & Leemans, A. (2024) · Springer eBooks · doi:10.1007/978-3-031-70337-9_5 · Cited by 1
Zalesky, A., Sotiropoulos, S. N., Jbabdi, S., & Fornito, A. (2025) · Elsevier eBooks · doi:10.1016/b978-0-12-818894-1.00007-0
From Brownian motion to the brain connectome: My perspective and historical view of diffusion MRI
Bihan, D. L. (2025) · Elsevier eBooks · doi:10.1016/b978-0-12-818894-1.00018-5
Improving tractography using anatomical priors and multimodal integration
St‐Onge, E., Girard, G., Schilling, K. G., Daducci, A., Deslauriers‐Gauthier, S., Petit, L., & Descoteaux, M. (2025) · Elsevier eBooks · doi:10.1016/b978-0-12-818894-1.00021-5
Self-citationGhassemi, L., Mehdizadeh, A., Ketabi, A., Ostad, S. P., & Tavakoli, M. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.109520
Sinzinger, F. L., Persson, S., Köpff, M., Pereira, J. B., & Moreno, R. (2026) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2026.1769103
Wen, R., Zhang, J., & Liang, Z. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.06.723316
Graph-Based White Matter Tractometry: Methods, Applications, and the Path to Validation
Li, J., Zhang, C., Wan, Z., Xie, Y., Zhang, H., Liu, X., & Wu, Y. N. (2026) · Neuroinformatics · doi:10.1007/s12021-026-09804-2
Podwalski, P., Dawidowski, B., Franczak, Ł., Tyburski, E. M., Misiak, B., Sagan, L. M., & Samochowiec, J. (2026) · Science Progress · doi:10.1177/00368504261484783
Diffusion Weighted Imaging: Principles and Applications
Fischi-Gómez, E., Girard, G., & Estéban, O. (2026) · Neuromethods · doi:10.1007/978-1-0716-5340-1_11
Metadata
- Authors
- Daducci, A., Dal Palú, A., Descoteaux, M., & Thiran, J.-P.
- Affiliations
- Université de Sherbrooke (CA)
- Centre Hospitalier Universitaire Vaudois (CH)
- Corresponding author
- Daducci, Alessandro
- Venue
- Frontiers in Neuroscience
- Publisher
- Frontiers Media SA
- Published
- 2016-06-06
- Volume
- 10
- Pages
- 247-247
- ISSN
- 1662-453X (ISSN-L)
- PMC
- PMC4893481
- OpenAlex
- W2404527239
- Cited by
- 138
- References
- 51
- Citations by year
- 2026: 9 · 2025: 17 · 2024: 9 · 2023: 8 · 2022: 8 · 2021: 12 · 2020: 14 · 2019: 23 · 2018: 16 · 2017: 21 · 2016: 1
- FWCI
- 17.65
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Frontiers in Neuroscience (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Figshare (cc-by)
- Funders
- École Polytechnique Fédérale de Lausanne
- Centre d'Imagerie BioMédicale
- 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 MRI, brain connectivity
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