Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging
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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.
“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]
The In Vivo Estimation of Fiber Orientations section reports multiple orientations in about 90% of white-matter voxels using CSD and complex configurations in 63% using bedpostx, supporting the claim that a majority have complex fiber organization. The paper discusses bending and fanning, but these measurements do not establish how many voxels have each listed configuration, including kissing fibers, or that the configurations are extensive. (LLM, conf=0.84)
Experimental — accuracy not yet measuredEven in regions with crossing fibers, many observed differences or changes in FA, AD and/or RD are likely to reflect actual anatomical and biophysical phenomena, as long as other pitfalls in acquisition and analysis have been avoided.
“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
In “Implications for DTI” (page not provided), the paper says that, if known analysis pitfalls are avoided, an observed FA change is highly likely to reflect a true underlying biophysical phenomenon—even though its meaning is ambiguous in crossing-fiber regions. It discusses axial and radial diffusivity as similarly difficult to interpret, but does not explicitly extend that likelihood statement to changes in AD or RD. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe behaviour of DTI indices in the human brain, which is characterized by a high density of crossing fibre that influence DTI measures, is only partially understood and remains speculative.
“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
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.
“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
The proportion of cerebral white matter voxels containing complex fiber geometries and/or multiple fiber bundles in different orientations is more likely between 60 and 90%.
“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
The majority of cerebral white matter voxels contain multiple fiber populations with different trajectories.
“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
Many cerebral white matter regions contain as many as 3 or more intersecting fiber bundles with different trajectories.
“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
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.
“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
Up to 90% of WM voxels in a DTI map contain complex fiber architecture and cannot be modeled accurately using a single tensor model.
“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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“…crossing fibers, kissing fibers, etc.) is now thought to be at least 33% (Behrens et al., 2007), and is more likely somewhere between 60 and 90% (Jeurissen et al., 2013) – with many regions thought to contain as many as 3 or more intersecting fiber bundles with different trajectories ( Figure 1 )…”
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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.”
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- 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
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