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Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging
Open accessAbstract
It has long been recognized that the diffusion tensor model is inappropriate to characterize complex fiber architecture, causing tensor-derived measures such as the primary eigenvector and fractional anisotropy to be unreliable or misleading in these regions. There is however still debate about the impact of this problem in practice. A recent study using a Bayesian automatic relevance detection (ARD) multicompartment model suggested that a third of white matter (WM) voxels contain crossing fibers, a value that, whilst already significant, is likely to be an underestimate. The aim of this study is to provide more robust estimates of the proportion of affected voxels, the number of fiber orientations within each WM voxel, and the impact on tensor-derived analyses, using large, high-quality diffusion-weighted data sets, with reconstruction parameters optimized specifically for this task. Two reconstruction algorithms were used: constrained spherical deconvolution (CSD), and the ARD method used in the previous study. We estimate the proportion of WM voxels containing crossing fibers to be ~90% (using CSD) and 63% (using ARD). Both these values are much higher than previously reported, strongly suggesting that the diffusion tensor model is inadequate in the vast majority of WM regions. This has serious implications for downstream processing applications that depend on this model, particularly tractography, and the interpretation of anisotropy and radial/axial diffusivity measures.
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“The majority of white matter voxels are characterized by extensive crossing, kissing, bending or fanning of fibers [11].”
From Differentiating axonal loss and demyelination in chronic MS lesions: A novel approach using single streamline diffusivity analysis (2021) · cites this paper as [11]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“as pointed out by Jeurissen et al. (2013), even regions with crossing fibers, many observed differences/changes in FA, AD and/or RD are likely to reflect actual anatomical and biophysical phenomena, as long as other pitfalls in the acquisition and analysis have been avoided (Jones and Cercignani, 2010).”
From Potential Pitfalls of Using Fractional Anisotropy, Axial Diffusivity, and Radial Diffusivity as Biomarkers of Cerebral White Matter Microstructure (2022) · cites this paper as Jeurissen et al., 2013
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“We caution that the behaviour of the DTI indices in the human brain, which is characterized by a high density of crossing fibre that influence DTI measures (Jeurissen et al., 2013), is only partially understood and remains speculative (Jones et al., 2013).”
From White matter diffusion alterations precede symptom onset in autosomal dominant Alzheimer’s disease (2018) · cites this paper as Jeurissen et al., 2013
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“At standard spatial resolutions around a few mm, multiple fiber orientations affect the diffusivity patterns in up to 90% of white matter voxels in the human brain (Jeurissen et al. 2013).”
From Diffusion MRI-based cortical connectome reconstruction: dependency on tractography procedures and neuroanatomical characteristics (2018) · cites this paper as Jeurissen et al. 2013
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“and is more likely somewhere between 60 and 90% (Jeurissen et al., 2013)”
From Potential Pitfalls of Using Fractional Anisotropy, Axial Diffusivity, and Radial Diffusivity as Biomarkers of Cerebral White Matter Microstructure (2022) · cites this paper as Jeurissen et al., 2013
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“it is now widely recognized that the majority of cerebral white matter voxels contain multiple fiber populations with different trajectories”
From Potential Pitfalls of Using Fractional Anisotropy, Axial Diffusivity, and Radial Diffusivity as Biomarkers of Cerebral White Matter Microstructure (2022) · cites this paper as Jeurissen et al., 2013
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“with many regions thought to contain as many as 3 or more intersecting fiber bundles with different trajectories (Figure 1)”
From Potential Pitfalls of Using Fractional Anisotropy, Axial Diffusivity, and Radial Diffusivity as Biomarkers of Cerebral White Matter Microstructure (2022) · cites this paper as Jeurissen et al., 2013
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“The TDF model addresses well-established limitations of the standard single-tensor diffusion model—which cannot resolve complex profiles of WM architecture such as crossing or mixing fibers, present in up to 90% of WM voxels (Tournier et al., 2004; Descoteaux et al., 2007, 2009; Jeurissen et al., 2013).”
From Diffusion MRI Indices and Their Relation to Cognitive Impairment in Brain Aging: The Updated Multi-protocol Approach in ADNI3 (2019) · cites this paper as Jeurissen et al., 2013
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“It has been estimated that up to 90% of WM voxels in a DTI map contain complex fiber architecture and cannot be modeled accurately using a single tensor model (Jeurissen et al., 2013).”
From The role of myelination in measures of white matter integrity: Combination of diffusion tensor imaging and two-photon microscopy of CLARITY intact brains (2017) · cites this paper as Jeurissen et al., 2013
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Spin Diffusion Measurements: Spin Echoes in the Presence of a Time-Dependent Field Gradient
Stejskal, E. O., & Tanner, J. E. (1965) · The Journal of Chemical Physics · doi:10.1063/1.1695690 · Cited by 8,239
Ashburner, J., & Friston, K. J. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.02.018 · Cited by 7,507
MR diffusion tensor spectroscopy and imaging
Basser, P. J., Mattiello, J., & LeBihan, D. (1994) · Biophysical Journal · doi:10.1016/s0006-3495(94)80775-1 · Cited by 5,970
Elastix: a toolbox for intensity-based medical image registration
Klein, S., Staring, M., Murphy, K., Viergever, M. A., & Pluim, J. (2010) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2009.2035616 · Cited by 4,311
Estimation of the Effective Self-Diffusion Tensor from the NMR Spin Echo
Basser, P. J., Mattiello, J., & Bihan, D. L. (1994) · Journal of Magnetic Resonance Series B · doi:10.1006/jmrb.1994.1037 · Cited by 3,656
Probabilistic diffusion tractography with multiple fibre orientations: What can we gain?
Behrens, T. E. J., Berg, H. J., Jbabdi, S., Rushworth, M. F. S., & Woolrich, M. W. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2006.09.018 · Cited by 3,471
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
Bayesian analysis of neuroimaging data in FSL
Woolrich, M. W., Jbabdi, S., Patenaude, B., Chappell, M., Makni, S., Behrens, T., Beckmann, C., Jenkinson, M., & Smith, S. M. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2008.10.055 · Cited by 2,385
Tournier, J., Calamante, F., & Connelly, A. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.02.016 · Cited by 2,191
Self-citationFiber tracking: principles and strategies – a technical review
Mori, S., & van Zijl, P. C. M. (2002) · NMR in Biomedicine · doi:10.1002/nbm.781 · Cited by 2,019
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Research works printed but not linked: 2
- Jones DK, 2005, Contribution of cardiac pulsation to variability of tractography results, Proc Intl Soc Magn Reson Med, 13, 222
- PierpaoliC MarencoS RohdeG JonesDK BarnettAS(2003):Analyzing the contribution of cardiac pulsation to the variability of quantities derived from the diffusion tensor. Proceedings of the 11th Scientific Meeting International Society for Magnetic Resonance in Medicine Toronto Canada. p70.
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Jia, Y., Bourke, N., Mallas, E.-J., Caeyenberghs, K., Simoni, S. D., Jenkins, P. O., Duque, J. F. D., Sharp, D. J., & Parker, T. D. (2026) · Brain Communications · doi:10.1093/braincomms/fcag042 · Cited by 2
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Zhang, F., Rushmore, J., Li, Y., Karayumak, S. C., Song, Y., Cai, W., Westin, C., Levitt, J. J., Makris, N., Yogesh Rathi, A., et al. (2026) · NeuroImage Clinical · doi:10.1016/j.nicl.2026.103979 · Cited by 1
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Martinez, J. D., Mooshagian, E., Wang, H., Karlsgodt, K. H., Uddin, L. Q., & Nomi, J. S. (2026) · Brain Structure and Function · doi:10.1007/s00429-026-03091-y · Cited by 1
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Goodman, M., Richerson, W. T., & Wolfgram, D. F. (2023) · Medical Student Research Journal · doi:10.15404/msrj/05.2023.234 · Cited by 1
Bonkhoff, A. K., Rost, N. S., & Schirmer, M. D. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-85280-7.00022-1 · Cited by 1
Tvrdík, T., Melicherčík, Ľ., Šebeková, K., Szabó, J., Maková, M., Gogola, D., & Kašparová, S. (2023) · Measurement Science Review · doi:10.2478/msr-2023-0030 · Cited by 1
Oeschger, J. M., Tabelow, K., & Mohammadi, S. (2024) · Magnetic Resonance in Medicine · doi:10.1002/mrm.30034 · Cited by 1
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Aguayo-González, J. F., Ehrlich-Lopez, H. P., Concha, L., & Rivera, M. (2024) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2024.1277050 · Cited by 1
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Bizzi, A. (2013) · Springer eBooks · doi:10.1007/978-1-4471-2213-5_20 · Cited by 1
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Arslan, S. (2017) · arXiv (Cornell University) · doi:10.25560/54760 · Cited by 1
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Hashempour, N., Tuulari, J. J., Merisaari, H., Lewis, J. D., Häikiö, T., Scheinin, N. M., Nolvi, S., Korja, R., Karlsson, L., Karlsson, H., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.02.04.578788 · Cited by 1
Granger, S. J., Jobson, S. A., Ravichandran, C. T., Devignes, Q., Akman, E., Blackford, J. U., May, V., Hammack, S. E., Carlezon, W. A., Ressler, K. J., et al. (2025) · medRxiv · doi:10.1101/2025.06.05.25329085 · Cited by 1
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Leserri, S., Rockland, K. S., & Aydogan, D. B. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.27.728127 · Cited by 1
Tournier, J., & Mori, S. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-397025-1.00294-3
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Saunavaara, V. (2014) · UTUPub (University of Turku)
Tax, C. M. W., & Leemans, A. (2014) · Neuropraxis · doi:10.1007/s12474-014-0050-3
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Giot, R., Descoteaux, M., & Charrier, C. (2013) · HAL
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Bracht, T. (2015) · ORCA Online Research @Cardiff (Cardiff University)
MRI Investigation of Optic Radiations in Human Brain: New Findings and Technical Considerations
Arrigo, A., Calamuneri, A., & Mormina, E. (2016) · Radiology - Open Journal · doi:10.17140/roj-1-e002
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Zucchelli, M., Brusini, L., Méndez, C. A., & Menegaz, G. (2016) · Mathematics and visualization · doi:10.1007/978-3-319-28588-7_6
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Scheck, S. M. (2016) · The University of Queensland · doi:10.14264/uql.2016.526
Algorithms for magnetic resonance imaging in radiotherapy
Sjölund, J. (2018) · Linköping studies in science and technology. Dissertations · doi:10.3384/diss.diva-144351
Acuff, H. E. (2018) · D-Scholarship@Pitt (University of Pittsburgh)
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Bonthrone, A. F. (2018) · UCL Discovery (University College London)
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Georgiopoulos, C. (2019) · Linköping University medical dissertations · doi:10.3384/diss.diva-154919
Tur, C., Grussu, F., Prados, F., Collorone, S., Wheeler‐Kingshott, C. A. M. G., & Ciccarelli, O. (2019) · Mathematics and visualization · doi:10.1007/978-3-030-05831-9_21
Meissner, T. W., Genç, E., Mädler, B., & Weigelt, S. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/662809
Progress in brain fiber voxel microstructure estimation algorithms
Feng, Y., He, J., Zhou, S. K., & Yongqiang, L. I. (2018) · Scientia Sinica Informationis · doi:10.1360/n112017-00273
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Giot, R., Charrier, C., & Descoteaux, M. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1207.0677
Dall’Acqua, P. (2018) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-165194
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Piebenga, A. (2020) · University of Canterbury Research Repository (University of Canterbury) · doi:10.26021/7401
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Blommaert, J., & Christiaens, D. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-85280-7.00007-5
Xue, T., Karimi, H. S., Consagra, W., Zhang, F., Cai, W., O’Donnell, L. J., Ning, L., & Yogesh Rathi, A. (2023) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi53787.2023.10230707
Karan, P., Edde, M., Gilbert, G., Baraković, M., Magon, S., & Descoteaux, M. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.05.561088
Cerdá, A. C., Toschi, N., Treaba, C. A., Barletta, V. T., Herranz, E., Mehndiratta, A., Gomez-Sanchez, J. A., Mainero, C., & Santis, S. D. (2023) · eLife · doi:10.7554/elife.79169.sa2
Zhao, L., Zhao, W., Cao, J., & Tu, Y. (2023) · Figshare · doi:10.6084/m9.figshare.22614448
Wigley, I. L. C. M., Mustonen, P., Karlsson, L., Nolvi, S., Scheinin, N. M., Kortesluoma, S., Pastore, M., Tervahartiala, K., Coimbra, B., Rodrigues, A. J., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.06.610927
Models: Signal Representation vs. Microstructural Models
Luca, A. D., Froeling, M., & Leemans, A. (2024) · Springer eBooks · doi:10.1007/978-3-031-70337-9_4
Self-citationDiffusion Tensor Imaging (DTI)
Luca, A. D., & Froeling, M. (2024) · Springer eBooks · doi:10.1007/978-3-031-70337-9_3
Tractography validation part 3: Lessons learned through validation studies
Schilling, K. G., Rheault, F., & Dyrby, T. B. (2025) · Elsevier eBooks · doi:10.1016/b978-0-12-818894-1.00004-5
Newman, B. T., Horn, J. D. V., & Druzgal, T. J. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.03.10.642453
Fixel-based analysis in patients after the first episode in schizophrenia
Slezak, O., Škoch, A., Žižka, J., Bayer, D., Španiel, F., Tintěra, J., Vaňásek, J., Eliáš, P., & Ryška, P. (2022) · Česká radiologie · doi:10.55095/cesradiol2022/013
Kierońska-Siwak, S., Filipiak, P., Jabłońska, M., & Sokal, P. (2025) · Frontiers in Neuroscience · doi:10.3389/fnins.2025.1543032
Yip, S. W., & Konova, A. B. (2025) · Biological Psychiatry Global Open Science · doi:10.1016/j.bpsgos.2025.100532
Sathe, A., Chatterjee, A., Shashikumar, N., Pechman, K. R., Schilling, K. G., Landman, B. A., Bolton, C. J., Zhang, P., Bender, A. R., Hohman, T. J., et al. (2025) · medRxiv · doi:10.1101/2025.05.29.25328528
Metadata
- Authors
- Jeurissen, B., Leemans, A., Tournier, J., Jones, D. K., & Sijbers, J.
- Affiliations
- University of Antwerp (BE)
- University Medical Center Utrecht (NL)
- Florey Institute of Neuroscience and Mental Health (AU)
- Cardiff University (GB)
- Venue
- Human Brain Mapping
- Publisher
- Wiley
- Published
- 2012-05-19
- Volume
- 34
- Issue
- 11
- Pages
- 2747-2766
- ISSN
- 1065-9471 (ISSN-L)
- 1097-0193
- PMC
- PMC6870534
- OpenAlex
- W2137679584
- Cited by
- 1,088
- References
- 53
- Citations by year
- 2026: 70 · 2025: 82 · 2024: 83 · 2023: 93 · 2022: 92 · 2021: 111 · 2020: 81 · 2019: 76 · 2018: 69 · 2017: 80 · 2016: 63 · 2015: 78 · 2014: 40 · 2013: 55 · 2012: 14
- FWCI
- 45.17
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Human Brain Mapping (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Research Bank (Australian Catholic University) (submittedVersion)
- Research Portal (King's College London) (submittedVersion)
- Funders
- Wellcome Trust (096646, 096646/Z/11/Z)
- State Government of Victoria
- National Health and Medical Research Council
- National Medical Research Council
- 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
- crossing fibers, white matter microstructure, diffusion MRI, constrained spherical deconvolution, multi-compartment model, fiber orientation distribution, tractography, fractional anisotropy
- MeSH terms
- Adult, Algorithms, Brain, Brain Mapping, Data Interpretation, Statistical, False Positive Reactions, Humans, Image Processing, Computer-Assisted, Nerve Fibers, Reproducibility of Results, Anisotropy, Excitatory Postsynaptic Potentials, Diffusion Magnetic Resonance Imaging, Signal-To-Noise Ratio
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.
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