Probabilistic diffusion tractography with multiple fibre orientations: What can we gain?
Open accessAbstract
We present a direct extension of probabilistic diffusion tractography to the case of multiple fibre orientations. Using automatic relevance determination, we are able to perform online selection of the number of fibre orientations supported by the data at each voxel, simplifying the problem of tracking in a multi-orientation field. We then apply the identical probabilistic algorithm to tractography in the multi- and single-fibre cases in a number of example systems which have previously been tracked successfully or unsuccessfully with single-fibre tractography. We show that multi-fibre tractography offers significant advantages in sensitivity when tracking non-dominant fibre populations, but does not dramatically change tractography results for the dominant pathways.
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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 Behrens et al., 2007
The Abstract says that multiple fibre populations occur across large regions of white matter. But Results (Local fitting of the complex fibre model; page unavailable) reports that only one third of voxels with fractional anisotropy above 0.1 supported more than one orientation in the 60-direction dataset. This supports the presence of multiple populations, not the claim that they occur in a majority of cerebral white matter voxels. (LLM, conf=0.88)
Experimental — accuracy not yet measuredThe proportion of cerebral white matter voxels containing complex fiber geometries and/or multiple fiber bundles in different orientations is at least 33%.
“the proportion of cerebral white matter voxels containing complex fiber geometries and/or multiple fiber bundles in different orientations (i.e., crossing fibers, kissing fibers, etc.) is now thought to be at least 33% (Behrens et al., 2007)”
From Potential Pitfalls of Using Fractional Anisotropy, Axial Diffusivity, and Radial Diffusivity as Biomarkers of Cerebral White Matter Microstructure (2022) · cites this paper as Behrens et al., 2007
The Results (Local fitting of the complex fibre model) and Discussion report detectable complex fibre architecture in approximately one third of voxels with fractional anisotropy greater than 0.1 using a 60-direction dataset; page numbers are not supplied. This supports the approximate proportion, but not a lower bound of at least 33% for all cerebral white matter voxels. (LLM, conf=0.88)
Experimental — accuracy not yet measuredTractography results can vary across the large set of available diffusion models and tractography methods.
“On one hand, tractography results may vary across the large set of available diffusion models (Afzali et al., 2020; Alexander, 2006; Panagiotaki et al., 2012; Yablonskiy and Sukstanskii, 2010) and tractography methods”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Behrens et al., 2007
The authors recommend probabilistic tractography for examining specific white matter tracts because it is more robust to crossing and kissing fibers.
“Probabilistic tractography is recommended as it has greatly improved robustness to the many kissing and crossing white matter fibers of the brain (Behrens, Berg, Jbabdi, Rushworth, & Woolrich, 2007).”
From Substance abuse and white matter: Findings, limitations, and future of diffusion tensor imaging research (2019) · cites this paper as Behrens, Berg, Jbabdi, Rushworth, & Woolrich, 2007
Recent advances in modeling of local diffusion properties allow estimation of more than one fiber direction at each voxel, enabling tracing of crossing fibers.
“Recent advances in modeling of local diffusion properties allow estimation of more than 1 fiber direction at each voxel, enabling tracing of crossing fibers (Behrens et al. 2007)”
From Anatomical connectivity of the subgenual cingulate region targeted with deep brain stimulation for treatment-resistant depression (2008) · cites this paper as Behrens et al. 2007
Use of a multi-fiber tractography algorithm increased the chances of tracking through regions of fiber complexity or crossing, but the approach is potentially susceptible to false negatives.
“Although use of a multi-fiber tractography algorithm will have increased our chances of tracking through regions of fiber complexity or crossing (Behrens et al. 2007), the approach is nevertheless potentially susceptible to false negatives.”
From Anatomical connectivity of the subgenual cingulate region targeted with deep brain stimulation for treatment-resistant depression (2008) · cites this paper as Behrens et al. 2007
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“…fiber geometries and/or multiple fiber bundles in different orientations (i.e., 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…”
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Ball and rackets: inferring fiber fanning from diffusion-weighted MRI
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Self-citationInsular cortex mediates increased pain tolerance in yoga practitioners
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A DTI-Derived Measure of Cortico-Cortical Connectivity
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- Authors
- Behrens, T. E. J., Berg, H. J., Jbabdi, S., Rushworth, M. F. S., & Woolrich, M. W.
- Affiliations
- University of Oxford (GB)
- Corresponding author
- Behrens, T.E.J.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2007-01
- Volume
- 34
- Issue
- 1
- Pages
- 144-155
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC7116582
- OpenAlex
- W2000133863
- Cited by
- 3,471
- References
- 40
- Citations by year
- 2026: 104 · 2025: 160 · 2024: 157 · 2023: 156 · 2022: 224 · 2021: 237 · 2020: 219 · 2019: 250 · 2018: 222 · 2017: 264 · 2016: 214 · 2015: 240 · 2014: 201 · 2013: 222 · 2012: 164
- FWCI
- 48.42
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Wellcome Trust (078204)
- Research Councils UK
- Medical Research Council
- Engineering and Physical Sciences 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
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- automatic relevance determination, online model selection, white matter tractography, diffusion MRI
- MeSH terms
- Brain, Diffusion, Humans, Neural Pathways, Models, Statistical
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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