Bingham–NODDI: Mapping anisotropic orientation dispersion of neurites using diffusion MRI
Open accessAbstract
This paper presents Bingham-NODDI, a clinically-feasible technique for estimating the anisotropic orientation dispersion of neurites. Direct quantification of neurite morphology on clinical scanners was recently realised by a diffusion MRI technique known as neurite orientation dispersion and density imaging (NODDI). However in its current form NODDI cannot estimate anisotropic orientation dispersion, which is widespread in the brain due to common fanning and bending of neurites. This work proposes Bingham-NODDI that extends the NODDI formalism to address this limitation. Bingham-NODDI characterises anisotropic orientation dispersion by utilising the Bingham distribution to model neurite orientation distribution. The new model estimates the extent of dispersion about the dominant orientation, separately along the primary and secondary dispersion orientations. These estimates are subsequently used to estimate the overall dispersion about the dominant orientation and the dispersion anisotropy. We systematically evaluate the ability of the new model to recover these key parameters of anisotropic orientation dispersion with standard NODDI protocol, both in silico and in vivo. The results demonstrate that the parameters of the proposed model can be estimated without additional acquisition requirements over the standard NODDI protocol. Thus anisotropic dispersion can be determined and has the potential to be used as a marker for normal brain development and ageing or in pathology.…
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The NODDI model assumes the neurite orientation distribution using a single Watson distribution without accounting for fiber crossings.
“In addition, NODDI model assumes the neurite orientation distribution using a single Watson distribution without accounting for fiber crossings, the current results may be improved with multiple Bingham distributions or spherical mean technique (SMT) (Kaden et al. 2016; Tariq et al. 2016).”
From Neurite orientation dispersion and density imaging of mouse brain microstructure (2019) · cites this paper as Tariq et al. 2016
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Wang, W., Wen, X., Zhang, Y., Wang, H., Hu, L., Tan, C., & Liu, Z. (2026) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2026.1924031
- Authors
- Tariq, M., Schneider, T., Alexander, D. C., Gandini Wheeler-Kingshott, C. A., & Zhang, H.
- Affiliations
- University College London (GB)
- UCL Queen Square Institute of Neurology (GB)
- Corresponding author
- Tariq, Maira
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2016-06
- Volume
- 133
- Pages
- 207-223
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2283761130
- Cited by
- 169
- References
- 5
- Citations by year
- 2026: 7 · 2025: 14 · 2024: 13 · 2023: 24 · 2022: 13 · 2021: 19 · 2020: 23 · 2019: 24 · 2018: 16 · 2017: 14 · 2016: 2
- FWCI
- 16.19
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- NeuroImage (publishedVersion, cc-by)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- Engineering and Physical Sciences Research Council (P/I027084/01, EP/K502959/1)
- Medical Research Council (MR/L011530/1)
- Royal Society
- NIHR Imperial Biomedical Research Centre (R&D03/10/RAG0449)
- Department of Health and Social Care
- Royal Academy of Engineering
- National Institute for Health Research
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- NODDI, neurite orientation dispersion, Bingham distribution, neurite morphology, white matter microstructure
- MeSH terms
- Adult, Algorithms, Brain, Brain Mapping, Computer Simulation, Feasibility Studies, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Male, Models, Neurological, Sensitivity and Specificity, Software, Reproducibility of Results, Models, Statistical, Neurites, Anisotropy, Diffusion Tensor Imaging
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