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.
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
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?
Structural connectome analyses are most convincing when integrated with neuroanatomical knowledge, functional data, and careful control of tractography biases.
“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
Section 3.2 (Biological plausibility; page unavailable) recommends comparing tractography estimates with known anatomy, while the Introduction and Section 5 warn that overlooked tractography pitfalls can bias connectivity analyses. The passages do not recommend integrating structural analyses with functional data; a search for functional MRI, functional data, and functional connectivity found no supporting passage. (LLM, conf=0.86)
Experimental — accuracy not yet measuredHigher angular-resolution methods, better distortion correction, and anatomically informed tractography improve matters, yet they do not eliminate the inverse problem.
“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
Section 5 (Discussion and conclusion; page unavailable) says anatomical constraints and careful handling of partial fibers matter, but global methods can still overfit and yield anatomically implausible results. Section 4.1 discusses correction of signal-intensity inhomogeneity, not distortion correction. The paper does not establish that higher angular resolution or better distortion correction improves tractography while leaving the inverse problem unresolved. (LLM, conf=0.85)
Experimental — accuracy not yet measuredWhite matter fibers synapse in the gray matter.
“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
Tractography visualizes a model of anatomical connectivity constrained by diffusion data, not anatomy itself.
“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
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Donoho, D. L. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2006.871582 · Cited by 23,994 · see citing works
Donoho, D. L. (2004) · Cited by 17,049 · see citing works
Candes, E. J., Romberg, J., & Tao, T. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2005.862083 · Cited by 15,639 · see citing works
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 · see citing works
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 4,653 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 2,605 · see citing works
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 2,479 · cited 6× · see citing works
“…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…”
BackgroundInfluentialTournier, J., Calamante, F., & Connelly, A. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.02.016 · Cited by 2,191 · see citing works
High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity
Tuch, D. S., Reese, T. G., Wiegell, M. R., Makris, N., Belliveau, J. W., & Wedeen, V. J. (2002) · Magnetic Resonance in Medicine · doi:10.1002/mrm.10268 · Cited by 1,548 · cited 2× · see citing works
“…signal corresponding to this configuration and the response functions of the dictionary have been simulated using the classical multi-tensor model (Tuch et al., 2002); all the observations in this article remain the same if more complex generative models are used (Soderman and Jonsson, 1995;…”
MethodDipy, a library for the analysis of diffusion MRI data
Garyfallidis, E., Brett, M., Amirbekian, B., Rokem, A., van der Walt, S., Descoteaux, M., Nimmo-Smith, I., & Contributors, D. (2014) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2014.00008 · Cited by 1,371 · see citing works
Self-citationMRtrix: Diffusion tractography in crossing fiber regions
Tournier, J., Calamante, F., & Connelly, A. (2012) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22005 · Cited by 1,370 · see citing works
Composite hindered and restricted model of diffusion (CHARMED) MR imaging of the human brain
Assaf, Y., & Basser, P. J. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.03.042 · Cited by 856 · see citing works
A Review of Methods for Correction of Intensity Inhomogeneity in MRI
Vovk, U., Pernuš, F., & Likar, B. (2007) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2006.891486 · Cited by 852 · see citing works
Regularized, fast, and robust analytical Q-ball imaging
Descoteaux, M., Angelino, E., Fitzgibbons, S., & Deriche, R. (2007) · Magnetic Resonance in Medicine · doi:10.1002/mrm.21277 · Cited by 850 · see citing works
Self-citationSmith, R. E., Tournier, J., Calamante, F., & Connelly, A. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.06.092 · Cited by 758 · see citing works
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 · see citing works
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 · see citing works
Orientationally invariant indices of axon diameter and density from diffusion MRI
Alexander, D. C., Hubbard, P. L., Hall, M. G., Moore, E. A., Ptito, M., Parker, G. J. M., & Dyrby, T. B. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.05.043 · Cited by 683 · see citing works
Tractography: Where Do We Go from Here?
Jbabdi, S., & Johansen-Berg, H. (2011) · Brain Connectivity · doi:10.1089/brain.2011.0033 · Cited by 614 · see citing works
Accelerated microstructure imaging via Convex Optimization (AMICO) from diffusion MRI data
Daducci, A., Canales-Rodríguez, E. J., Zhang, H., Dyrby, T. B., Alexander, D. C., & Thiran, J.-P. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2014.10.026 · Cited by 593 · see citing works
Self-citationParker, G. J. M., Haroon, H. A., & Wheeler‐Kingshott, C. A. M. (2003) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.10350 · Cited by 527 · see citing works
A Douglas–Rachford Splitting Approach to Nonsmooth Convex Variational Signal Recovery
Combettes, P. L., & Pesquet, J.-C. (2007) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2007.910264 · Cited by 472 · see citing works
Compartment models of the diffusion MR signal in brain white matter: A taxonomy and comparison
Panagiotaki, E., Schneider, T., Siow, B., Hall, M. G., Lythgoe, M. F., & Alexander, D. C. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.09.081 · Cited by 422 · see citing works
Quantitative evaluation of 10 tractography algorithms on a realistic diffusion MR phantom
Fillard, P., Descoteaux, M., Goh, A., Gouttard, S., Jeurissen, B., Malcolm, J., Ramirez-Manzanares, A., Reisert, M., Sakaie, K., Tensaouti, F., et al. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.01.032 · Cited by 418 · see citing works
Self-citationMeasuring macroscopic brain connections in vivo
Jbabdi, S., Sotiropoulos, S. N., Haber, S. N., Van Essen, D. C., & Behrens, T. E. (2015) · Nature Neuroscience · doi:10.1038/nn.4134 · Cited by 334 · see citing works
Towards quantitative connectivity analysis: reducing tractography biases
Girard, G., Whittingstall, K., Deriche, R., & Descoteaux, M. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.04.074 · Cited by 324 · see citing works
Self-citationChallenges and limitations of quantifying brain connectivity <i>in vivo</i> with diffusion MRI
Jones, D. K. (2010) · Imaging in Medicine · doi:10.2217/iim.10.21 · Cited by 321 · see citing works
Global fiber reconstruction becomes practical
Reisert, M., Mader, I., Anastasopoulos, C., Weigel, M., Schnell, S., & Kiselev, V. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.09.016 · Cited by 312 · see citing works
Jespersen, S. N., Bjarkam, C. R., Nyengaard, J. R., Chakravarty, M. M., Hansen, B., Vosegaard, T., Østergaard, L., Yablonskiy, D., Nielsen, N. C., & Vestergaard-Poulsen, P. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.08.053 · Cited by 290 · see citing works
Evaluation and statistical inference for human connectomes
Pestilli, F., Yeatman, J. D., Rokem, A., Kay, K. N., & Wandell, B. A. (2014) · Nature Methods · doi:10.1038/nmeth.3098 · Cited by 259 · see citing works
COMMIT: Convex Optimization Modeling for Microstructure Informed Tractography
Daducci, A., Dal Palu, A., Lemkaddem, A., & Thiran, J.-P. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2352414 · Cited by 255 · see citing works
Self-citationIntensity non-uniformity correction in MRI: Existing methods and their validation
Belaroussi, B., Milles, J., Carme, S., Zhu, Y., & Benoit-Cattin, H. (2005) · Medical Image Analysis · doi:10.1016/j.media.2005.09.004 · Cited by 250 · see citing works
Tractometer: Towards validation of tractography pipelines
Côté, M.-A., Girard, G., Boré, A., Garyfallidis, E., Houde, J.-C., & Descoteaux, M. (2013) · Medical Image Analysis · doi:10.1016/j.media.2013.03.009 · Cited by 228 · see citing works
Self-citationGibbs tracking: A novel approach for the reconstruction of neuronal pathways
Kreher, B., Mader, I., & Kiselev, V. G. (2008) · Magnetic Resonance in Medicine · doi:10.1002/mrm.21749 · Cited by 157 · see citing works
Global tractography of multi-shell diffusion-weighted imaging data using a multi-tissue model
Christiaens, D., Reisert, M., Dhollander, T., Sunaert, S., Suetens, P., & Maes, F. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.08.008 · Cited by 152 · see citing works
Restricted Diffusion in Cylindrical Geometry
Söderman, O., & Jönsson, B. T. L. (1995) · Journal of Magnetic Resonance Series A · doi:10.1006/jmra.1995.0014 · Cited by 126 · see citing works
Mangin, J.-F., Fillard, P., Cointepas, Y., Le Bihan, D., Frouin, V., & Poupon, C. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.009 · Cited by 121 · see citing works
Diffusion Basis Functions Decomposition for Estimating White Matter Intravoxel Fiber Geometry
Ramírez-Manzanares, A., Rivera, M., Vemuri, B. C., Carney, P. R., & Mareci, T. H. (2007) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2007.900461 · Cited by 115 · see citing works
A Novel Global Tractography Algorithm Based on an Adaptive Spin Glass Model
Fillard, P., Poupon, C., & Mangin, J. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04268-3_114 · Cited by 105 · see citing works
Think Global, Act Local: Scalar Challenges to Sustainable Development of Marine Environments
Stratford, E. (2004) · UTAS Research Repository · Cited by 82 · see citing works
MicroTrack: An Algorithm for Concurrent Projectome and Microstructure Estimation
Sherbondy, A. J., Rowe, M. C., & Alexander, D. C. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15705-9_23 · Cited by 75 · see citing works
Sparse regularization for fiber ODF reconstruction: From the suboptimality of and priors to
Daducci, A., Ville, D. V. D., Thiran, J., & Wiaux, Y. (2014) · Medical Image Analysis · doi:10.1016/j.media.2014.01.011 · Cited by 60 · see citing works
Self-citationChen, S., & Suzuki, K. (2013) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2013.2284016 · Cited by 53 · see citing works
MesoFT: Unifying Diffusion Modelling and Fiber Tracking
Reisert, M., Kiselev, V. G., Dihtal, B., Kellner, E., & Novikov, D. S. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10443-0_26 · Cited by 51 · see citing works
Think Global, Act Local; Projectome Estimation with BlueMatter
Sherbondy, A. J., Dougherty, R. F., Ananthanarayanan, R., Modha, D. S., & Wandell, B. A. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04268-3_106 · Cited by 48 · see citing works
Global tractography with embedded anatomical priors for quantitative connectivity analysis
Lemkaddem, A., Skiöldebrand, D., Dal Palú, A., Thiran, J.-P., & Daducci, A. (2014) · Frontiers in Neurology · doi:10.3389/fneur.2014.00232 · Cited by 47 · see citing works
Self-citationA convex optimization framework for global tractography
Daducci, A., Palù, A. D., Lemkaddem, A., & Thiran, J. (2013) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2013.6556527 · Cited by 35 · see citing works
Self-citationAxTract: Microstructure-Driven Tractography Based on the Ensemble Average Propagator
Girard, G., Fick, R., Descoteaux, M., Rachid Deriche, B., & Wassermann, D. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-19992-4_53 · Cited by 24 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
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 1,361 · see citing works
Self-citationDiffusion MRI fiber tractography of the brain
Jeurissen, B., Descoteaux, M., Mori, S., & Leemans, A. (2019) · NMR in Biomedicine · doi:10.1002/nbm.3785 · Cited by 574 · see citing works
Self-citationZhang, 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
Self-citationBuilding connectomes using diffusion MRI: why, how and but
Sotiropoulos, S. N., & Zalesky, A. (2019) · NMR in Biomedicine · doi:10.1002/nbm.3752 · Cited by 290 · see citing works
Limits 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
Self-citationEssayed, W. I., Zhang, F., Unadkat, P., Cosgrove, G. R., Golby, A. J., & O’Donnell, L. J. (2017) · NeuroImage Clinical · doi:10.1016/j.nicl.2017.06.011 · Cited by 249 · see citing works
Brain networks and their relevance for stroke rehabilitation
Guggisberg, A. G., Koch, P., Hummel, F. C., & Buetefisch, C. M. (2019) · Clinical Neurophysiology · doi:10.1016/j.clinph.2019.04.004 · Cited by 247 · see citing works
Design and Validation of Diffusion MRI Models of White Matter
Jelescu, I. O., & Budde, M. D. (2017) · Frontiers in Physics · doi:10.3389/fphy.2017.00061 · Cited by 240 · see citing works
Mapping Structural Connectivity Using Diffusion <scp>MRI</scp> : Challenges and Opportunities
Yeh, C., Jones, D. K., Liang, X., Descoteaux, M., & Connelly, A. (2021) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.27188 · Cited by 187 · see citing works
Self-citationChallenges in diffusion MRI tractography – Lessons learned from international benchmark competitions
Schilling, K. G., Daducci, A., Maier-Hein, K. H., Poupon, C., Houde, J., Nath, V., Anderson, A. W., Landman, B. A., & Descoteaux, M. (2018) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2018.11.014 · Cited by 147 · see citing works
Self-citationCommon misconceptions, hidden biases and modern challenges of dMRI tractography
Rheault, F., Poulin, P., Caron, A. V., St‐Onge, E., & Descoteaux, M. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab6aad · Cited by 105 · see citing works
Self-citationSchiavi, S., Ocampo‐Pineda, M., Baraković, M., Petit, L., Descoteaux, M., Thiran, J., & Daducci, A. (2020) · Science Advances · doi:10.1126/sciadv.aba8245 · Cited by 106 · see citing works
Self-citationCalamante, F. (2019) · Diagnostics · doi:10.3390/diagnostics9030115 · Cited by 92 · see citing works
Quantitative streamlines tractography: methods and inter-subject normalisation
Smith, R. E., Raffelt, D. A., Tournier, J., & Connelly, A. (2022) · Aperture Neuro · doi:10.52294/apertureneuro.2022.2.neod9565 · Cited by 90 · see citing works
Bundle‐specific tractography with incorporated anatomical and orientational priors
Rheault, F., St-Onge, E., Sidhu, J., Maier-Hein, K. H., Tzourio-mazoyer, N., Petit, L., & Descoteaux, M. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2018.11.018 · Cited by 87 · see citing works
Self-citationThe visual white matter: The application of diffusion MRI and fiber tractography to vision science
Rokem, A., Takemura, H., Bock, A. S., Scherf, K. S., Behrmann, M., Wandell, B. A., Fine, I., Bridge, H., & Pestilli, F. (2017) · Journal of Vision · doi:10.1167/17.2.4 · Cited by 84 · see citing works
Girard, G., Caminiti, R., Battaglia-Mayer, A., St-Onge, E., Ambrosen, K. S., Eskildsen, S. F., Krug, K., Dyrby, T. B., Descoteaux, M., Thiran, J.-P., et al. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117201 · Cited by 83 · see citing works
Self-citationGriffa, A., Ricaud, B., Benzi, K., Bresson, X., Daducci, A., Vandergheynst, P., Thiran, J., & Hagmann, P. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.04.015 · Cited by 79 · see citing works
Self-citationYang, J. Y.-M., Yeh, C.-H., Poupon, C., & Calamante, F. (2021) · Physics in Medicine & Biology · doi:10.1088/1361-6560/ac0d90 · Cited by 80 · see citing works
Integrating across neuroimaging modalities boosts prediction accuracy of cognitive ability
Rasero, J., Sentis, A. I., Yeh, F.-C. F., & Verstynen, T. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008347 · Cited by 74 · see citing works
Models of Network Spread and Network Degeneration in Brain Disorders
Raj, A., & Powell, F. (2018) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2018.07.012 · Cited by 65 · see citing works
Attar, F. M., Kirilina, E. P., Haenelt, D., Pine, K., Trampel, R., Edwards, L., & Weiskopf, N. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa049 · Cited by 62 · see citing works
Roine, T., Jeurissen, B., Perrone, D., Aelterman, J., Philips, W. R., Sijbers, J., & Leemans, A. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.10.009 · Cited by 63 · see citing works
Ax<scp>T</scp>ract: Toward microstructure informed tractography
Girard, G., Daducci, A., Petit, L., Thiran, J., Whittingstall, K., Deriche, R., Wassermann, D., & Descoteaux, M. (2017) · Human Brain Mapping · doi:10.1002/hbm.23741 · Cited by 62 · see citing works
Self-citationParallel Transport Tractography
Aydogan, D. B., & Shi, Y. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3034038 · Cited by 61 · see citing works
MEDUSA: A GPU-based tool to create realistic phantoms of the brain microstructure using tiny spheres
Ginsburger, K., Matuschke, F., Poupon, F., Mangin, J., Axer, M., & Poupon, C. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.02.055 · Cited by 60 · see citing works
Larivière, S., de Wael, R. V., Paquola, C., Hong, S., Mišić, B., Bernasconi, N., Bernasconi, A., Bonilha, L., & Bernhardt, B. C. (2018) · Brain Connectivity · doi:10.1089/brain.2018.0587 · Cited by 51 · see citing works
Track-weighted imaging methods: extracting information from a streamlines tractogram
Calamante, F. (2017) · Magnetic Resonance Materials in Physics Biology and Medicine · doi:10.1007/s10334-017-0608-1 · Cited by 55 · see citing works
Brain connectivity alterations in early psychosis: from clinical to neuroimaging staging
Griffa, A., Baumann, P., Klauser, P., Mullier, E., Cleusix, M., Jenni, R., van den Heuvel, M. P., Kim, Q., Conus, P., & Hagmann, P. (2019) · Translational Psychiatry · doi:10.1038/s41398-019-0392-y · Cited by 51 · see citing works
Fan, Q., Eichner, C., Afzali, M., Mueller, L., Tax, C. M. W., Davids, M., Mahmutović, M., Keil, B. R., Bilgiç, B., Setsompop, K., et al. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.118958 · Cited by 51 · see citing works
Sarubbo, S., Petit, L., BENEDICTIS, A. D., Chioffi, F., Ptito, M., & Dyrby, T. B. (2019) · Brain Structure and Function · doi:10.1007/s00429-019-01856-2 · Cited by 48 · see citing works
Sparse wars: A survey and comparative study of spherical deconvolution algorithms for diffusion MRI
Canales-Rodríguez, E. J., Legarreta, J. H., Pizzolato, M., Rensonnet, G., Girard, G., Rafael‐Patiño, J., Baraković, M., Romascano, D., Alemán‐Gómez, Y., Raduà, J., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.08.071 · Cited by 44 · see citing works
Self-citationMancini, M., Giulietti, G., Dowell, N., Spanò, B., Harrison, N., Bozzali, M., & Cercignani, M. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2017.09.018 · Cited by 43 · see citing works
Evaluating arcuate fasciculus laterality measurements across dataset and tractography pipelines
Bain, J. S., Yeatman, J. D., Schurr, R., Rokem, A., & Mezer, A. (2019) · Human Brain Mapping · doi:10.1002/hbm.24626 · Cited by 41 · see citing works
Multidimensional encoding of brain connectomes
Caiafa, C. F., & Pestilli, F. (2017) · Scientific Reports · doi:10.1038/s41598-017-09250-w · Cited by 42 · see citing works
Validation of structural brain connectivity networks: The impact of scanning parameters
Ambrosen, K. S., Eskildsen, S. F., Hinne, M., Krug, K., Lundell, H., Schmidt, M. N., van Gerven, M. A. J., Mørup, M., & Dyrby, T. B. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116207 · Cited by 40 · see citing works
The role of diffusion tractography in refining glial tumor resection
Vanderweyen, D., Theaud, G., Sidhu, J., Rheault, F., Sarubbo, S., Descoteaux, M., & Fortin, D. (2020) · Brain Structure and Function · doi:10.1007/s00429-020-02056-z · Cited by 40 · see citing works
Self-citationSubtle white matter alterations in schizophrenia identified with a new measure of fiber density
Stämpfli, P., Sommer, S., Manoliu, A., Burrer, A., Schmidt, A., Herdener, M., Seifritz, E., Kaiser, S., & Kirschner, M. (2019) · Scientific Reports · doi:10.1038/s41598-019-40070-2 · Cited by 42 · see citing works
Maffei, C., Sarubbo, S., & Jovicich, J. (2019) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2019.00027 · Cited by 38 · see citing works
Toward nonparametric diffusion‐ characterization of crossing fibers in the human brain
Reymbaut, A., Critchley, J., Durighel, G., Sprenger, T., Sughrue, M., Bryskhe, K., & Topgaard, D. (2020) · Magnetic Resonance in Medicine · doi:10.1002/mrm.28604 · Cited by 32 · see citing works
Modelling white matter in gyral blades as a continuous vector field
Cottaar, M., Bastiani, M., Boddu, N., Glasser, M. F., Haber, S. N., Essen, D. C. V., Sotiropoulos, S. N., & Jbabdi, S. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117693 · Cited by 36 · see citing works
Takemura, H., Pestilli, F., & Weiner, K. S. (2018) · Neuroscience Research · doi:10.1016/j.neures.2018.10.011 · Cited by 35 · see citing works
Tractography optimization using quantitative T1 mapping in the human optic radiation
Schurr, R., Duan, Y., Norcia, A. M., Ogawa, S., Yeatman, J. D., & Mezer, A. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.06.060 · Cited by 33 · see citing works
Schiavi, S., Petracca, M., Battocchio, M., Mendili, M. M. E., Paduri, S., Fleysher, L., Inglese, M., & Daducci, A. (2020) · Human Brain Mapping · doi:10.1002/hbm.24989 · Cited by 33 · see citing works
Self-citationConnectome reorganization associated with temporal lobe pathology and its surgical resection
Larivière, S., Park, B., Royer, J., DeKraker, J., Ngo, A., Sahlas, E., Chen, J., Rodríguez‐Cruces, R., Weng, Y., Frauscher, B., et al. (2024) · Brain · doi:10.1093/brain/awae141 · Cited by 32 · see citing works
Advances in computational and statistical diffusion MRI
O’Donnell, L. J., Daducci, A., Wassermann, D., & Lenglet, C. (2017) · NMR in Biomedicine · doi:10.1002/nbm.3805 · Cited by 32 · see citing works
Self-citationGinsburger, K., Poupon, F., Beaujoin, J., Estournet, D., Matuschke, F., Mangin, J., Axer, M., & Poupon, C. (2018) · Frontiers in Physics · doi:10.3389/fphy.2018.00012 · Cited by 32 · see citing works
Subdividing the superior longitudinal fasciculus using local quantitative MRI
Schurr, R., Zelman, A., & Mezer, A. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116439 · Cited by 31 · see citing works
Intra– and inter–hemispheric network dynamics supporting object recognition and speech production
Kitazawa, Y., Sonoda, M., Sakakura, K., Mitsuhashi, T., Firestone, E., Ueda, R., Kambara, T., Iwaki, H., Luat, A. F., Marupudi, N. I., et al. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.119954 · Cited by 28 · see citing works
Innocenti, G. M., Caminiti, R., Rouiller, E. M., Knott, G. W., Dyrby, T. B., Descoteaux, M., & Thiran, J. (2017) · Cerebral Cortex · doi:10.1093/cercor/bhx363 · Cited by 30 · see citing works
Self-citationGriffa, A., van de Ville, D., Herrmann, F., & Allali, G. (2020) · Neuroscience and Biobehavioral Reviews · doi:10.1016/j.neubiorev.2020.02.023 · Cited by 28 · see citing works
Tracking and validation techniques for topographically organized tractography
Aydogan, D. B., & Shi, Y. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.06.071 · Cited by 28 · see citing works
Ultra-High Field Diffusion MRI Reveals Early Axonal Pathology in Spinal Cord of ALS mice
Gatto, R. G., Amin, M., Deyoung, D., Hey, M. T., Mareci, T. H., & Magin, R. L. (2018) · Translational Neurodegeneration · doi:10.1186/s40035-018-0122-z · Cited by 28 · see citing works
A multi-scale probabilistic atlas of the human connectome
Alemán‐Gómez, Y., Griffa, A., Houde, J., Najdenovska, E., Magon, S., Cuadra, M. B., Descoteaux, M., & Hagmann, P. (2022) · Scientific Data · doi:10.1038/s41597-022-01624-8 · Cited by 26 · see citing works
Self-citationBundle-Specific Axon Diameter Index as a New Contrast to Differentiate White Matter Tracts
Baraković, M., Girard, G., Schiavi, S., Romascano, D., Descoteaux, M., Granziera, C., Jones, D. K., Innocenti, G. M., Thiran, J., & Daducci, A. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.646034 · Cited by 24 · see citing works
Self-citationSchiavi, S., Lu, P., Weigel, M., Lutti, A., Jones, D. K., Kappos, L., Granziera, C., & Daducci, A. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.118922 · Cited by 24 · see citing works
Self-citationTractography delineation of the vertical occipital fasciculus using quantitative T1 mapping
Schurr, R., Filo, S., & Mezer, A. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116121 · Cited by 25 · see citing works
Gatto, R. G., Li, W., & Magin, R. L. (2017) · Brain Research · doi:10.1016/j.brainres.2017.11.017 · Cited by 24 · see citing works
The Hidden Control Architecture of Complex Brain Networks
Lee, B., Kang, U., Chang, H., & Cho, K. (2019) · iScience · doi:10.1016/j.isci.2019.02.017 · Cited by 24 · see citing works
Local connectome phenotypes predict social, health, and cognitive factors
Powell, M. A., Garcia, J. O., Yeh, F.-C. F., Vettel, J. M., & Verstynen, T. (2017) · Network Neuroscience · doi:10.1162/netn_a_00031 · Cited by 24 · see citing works
Topological principles and developmental algorithms might refine diffusion tractography
Innocenti, G. M., Dyrby, T. B., Girard, G., St‐Onge, E., Thiran, J., Daducci, A., & Descoteaux, M. (2018) · Brain Structure and Function · doi:10.1007/s00429-018-1759-1 · Cited by 20 · see citing works
Self-citationHe, J., Zhang, F., Pan, Y., Feng, Y., Rushmore, J., Torio, E., Yogesh Rathi, A., Makris, N., Kikinis, R., Golby, A. J., et al. (2023) · Human Brain Mapping · doi:10.1002/hbm.26497 · Cited by 20 · see citing works
Modelling the brain: Elementary components to explain ensemble functions
D'Angelo, E. U., & Wheeler‐Kingshott, C. A. M. G. (2017) · UCL Discovery (University College London) · doi:10.1393/ncr/i2017-10137-5 · Cited by 18 · see citing works
Dynamic spatiotemporal patterns of brain connectivity reorganize across development
Vohryzek, J., Griffa, A., Mullier, E., Friedrichs‐Maeder, C., Sandini, C., Schaer, M., D, S. E. M., & Hagmann, P. (2019) · Network Neuroscience · doi:10.1162/netn_a_00111 · Cited by 17 · see citing works
Hierarchical microstructure informed tractography
Ocampo-Pineda, M., Schiavi, S., Rheault, F., Girard, G., Petit, L., Descoteaux, M., & Daducci, A. (2021) · Brain Connectivity · doi:10.1089/brain.2020.0907 · Cited by 19 · see citing works
Self-citationPhysical and digital phantoms for validating tractography and assessing artifacts
Drobnjak, I., Neher, P., Poupon, C., & Sarwar, T. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118704 · Cited by 18 · see citing works
Gatto, R. G., & Weissmann, C. (2018) · Current Medical Imaging Formerly Current Medical Imaging Reviews · doi:10.2174/1573405614666181115113400 · Cited by 18 · see citing works
Developmental Pathoconnectomics and Advanced Fetal MRI
András, J. (2019) · Topics in Magnetic Resonance Imaging · doi:10.1097/rmr.0000000000000220 · Cited by 18 · see citing works
Reducing false positives in tractography with microstructural and anatomical priorsPreprint
Schiavi, S., Barakovic, M., Ocampo-Pineda, M., Descoteaux, M., Thiran, J.-P., & Daducci, A. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/608349 · Cited by 18 · see citing works
Self-citationA Novel Anatomically-Constrained Global Tractography Approach to Monitor Sharp Turns in Gyri
Teillac, A., Beaujoin, J., Poupon, F., Mangin, J., & Poupon, C. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66182-7_61 · Cited by 17 · see citing works
Asymmetric fiber trajectory distribution estimation using streamline differential equation
Feng, Y., & He, J. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101686 · Cited by 15 · see citing works
Differences in topological progression profile among neurodegenerative diseases from imaging data
Garbarino, S., Lorenzi, M., Oxtoby, N. P., Vinke, E. J., Marinescu, R. V., Eshaghi, A., Ikram, M. A., Niessen, W. J., Ciccarelli, O., Barkhof, F., et al. (2019) · eLife · doi:10.7554/elife.49298 · Cited by 15 · see citing works
Fiber up‐sampling and quality assessment of tractograms – towards quantitative brain connectivity
Sommer, S., Kozerke, S., Seifritz, E., & Stämpfli, P. (2016) · Brain and Behavior · doi:10.1002/brb3.588 · Cited by 15 · see citing works
Differential Effects of Brain Disorders on Structural and Functional Connectivity
Vega-Pons, S., Olivetti, E., Avesani, P., Dodero, L., Gozzi, A., & Bifone, A. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00605 · Cited by 15 · see citing works
Real‐Time Tractography‐Assisted Neuronavigation for Transcranial Magnetic Stimulation
Aydogan, D. B., Souza, V. H., Matsuda, R. H., Lioumis, P., & Ilmoniemi, R. J. (2024) · Human Brain Mapping · doi:10.1002/hbm.70122 · Cited by 20 · see citing works
Cortical Surfaces Integration with Tractography for Structural Connectivity Analysis
St‐Onge, E., Al‐Sharif, N., Girard, G., Theaud, G., & Descoteaux, M. (2021) · Brain Connectivity · doi:10.1089/brain.2020.0930 · Cited by 14 · see citing works
Self-citationEvaluation 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 13 · see citing works
Self-citationThe impact of levamisole and alcohol on white matter microstructure in adult chronic cocaine users
Michels, L., Moisa, M., Stämpfli, P., Hirsiger, S., Baumgartner, M., Surbeck, W., Seifritz, E., & Quednow, B. (2022) · Addiction Biology · doi:10.1111/adb.13149 · Cited by 10 · see citing works
Bonet-Carné, E., Grussu, F., Ning, L., Sepehrband, F., & Tax, C. M. W. (2019) · Mathematics and visualization · doi:10.1007/978-3-030-05831-9 · Cited by 13 · see citing works
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 11 · see citing works
Self-citationCerebro-Cerebellar Pathways for Verbal Working Memory
Sobczak‐Edmans, M., Lo, Y., Hsu, Y., Chen, Y.-J., Kwok, F. Y., Chuang, K., Tseng, W. I., & Chen, S. A. (2019) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00530 · Cited by 12 · see citing works
Kumar, P. R., Jha, R., & Katti, A. (2023) · Acta Neurologica Belgica · doi:10.1007/s13760-023-02170-9 · Cited by 9 · see citing works
Papini, M. G., Avila, A. N., Fitzgerald, M., & Hellewell, S. C. (2024) · Journal of Neurotrauma · doi:10.1089/neu.2024.0039 · Cited by 10 · see citing works
Battocchio, M., Schiavi, S., Descoteaux, M., & Daducci, A. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119600 · Cited by 9 · see citing works
Self-citationNon-parametric graphnet-regularized representation of dMRI in space and time
Fick, R., Petiet, A., Santin, M. D., Philippe, A.-C., Lehéricy, S., Rachid Deriche, B., & Wassermann, D. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.09.002 · Cited by 9 · see citing works
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 · see citing works
Mapping human brain pathways: challenges and opportunities in the integration of scales
Charvet, C. (2023) · Brain, Behavior and Evolution · doi:10.1159/000530317 · Cited by 8 · see citing works
Blurred streamlines: A novel representation to reduce redundancy in tractography
Gabusi, I., Battocchio, M., Bosticardo, S., Schiavi, S., & Daducci, A. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103101 · Cited by 8 · see citing works
Self-citationWhite Matter Tractography and Diffusion‐Weighted Imaging
Vettel, J. M., Cooper, N., Garcia, J. O., Yeh, F.-C. F., & Verstynen, T. (2017) · Encyclopedia of Life Sciences · doi:10.1002/9780470015902.a0027162 · Cited by 9 · see citing works
Small brains but big challenges: white matter tractography in early life samples
Dubois, J., Grotheer, M., Yang, J. Y., Tournier, J., Beaulieu, C., & Lebel, C. A. (2025) · Brain Structure and Function · doi:10.1007/s00429-025-02922-8 · Cited by 6 · see citing works
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 · see citing works
Structural connectivity differs between males and females in the brain object manipulation network
Lee, D., & Son, T. (2021) · PLoS ONE · doi:10.1371/journal.pone.0253273 · Cited by 6 · see citing works
Diffusion MRI Fiber Tractography
Smith, R., Connelly, A., & Calamante, F. (2020) · Advances in magnetic resonance technology and applications · doi:10.1016/b978-0-12-817057-1.00023-8 · Cited by 12 · see citing works
Challenges for Tractogram Filtering
Jörgens, D., Descoteaux, M., & Rodrigo Moreno, A. (2021) · Mathematics and visualization · doi:10.1007/978-3-030-56215-1_7 · Cited by 6 · see citing works
Self-citationEvaluation of tractography-based myelin-weighted connectivity across the lifespan
Bosticardo, S., Schiavi, S., Schaedelin, S., Battocchio, M., Barakovic, M., Lu, P.-J., Weigel, M., Melie-García, L., Granziera, C., & Daducci, A. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1228952 · Cited by 4 · see citing works
Self-citationDaducci, A., Schiavi, S., Christiaens, D., Smith, R. E., & Alexander, D. C. (2025) · Elsevier eBooks · doi:10.1016/b978-0-12-818894-1.00014-8 · Cited by 6 · see citing works
Self-citationA multi-compartment model for pathological connectomes
Bosticardo, S., Battocchio, M., Schiavi, S., Zalesky, A., Granziera, C., & Daducci, A. (2025) · Network Neuroscience · doi:10.1162/netn.a.30 · Cited by 4 · see citing works
Self-citationSommer, S., Kozerke, S., Seifritz, E., & Staempfli, P. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00729 · Cited by 3 · see citing works
GPU-accelerated connectome discovery at scale
Sreenivasan, V., Kumar, S., Pestilli, F., Talukdar, P. P., & Sridharan, D. (2022) · Nature Computational Science · doi:10.1038/s43588-022-00250-z · Cited by 4 · see citing works
Improving Tractography Accuracy Using Dynamic Filtering
Battocchio, M., Schiavi, S., Descoteaux, M., & Daducci, A. (2021) · Mathematics and visualization · doi:10.1007/978-3-030-73018-5_4 · Cited by 4 · see citing works
Self-citation
- 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
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
