Compartment models of the diffusion MR signal in brain white matter: A taxonomy and comparison
Abstract
This paper aims to identify the minimum requirements for an accurate model of the diffusion MR signal in white matter of the brain. We construct a taxonomy of multi-compartment models of white matter from combinations of simple models for the intra- and the extra-axonal spaces. We devise a new diffusion MRI protocol that provides measurements with a wide range of imaging parameters for diffusion sensitization both parallel and perpendicular to white matter fibres. We use the protocol to acquire data from two fixed rat brains, which allows us to fit, study and compare the different models. The study examines a total of 47 analytic models, including several well-used models from the literature, which we place within the taxonomy. The results show that models that incorporate intra-axonal restriction, such as ball and stick or CHARMED, generally explain the data better than those that do not, such as the DT or the biexponential models. However, three-compartment models which account for restriction parallel to the axons and incorporate pore size explain the measurements most accurately. The best fit comes from combining a full diffusion tensor (DT) model of the extra-axonal space with a cylindrical intra-axonal component of single radius and a third spherical compartment of non-zero radius. We also measure the stability of the non-zero radius intra-axonal models and find that single radius intra-axonal models are more stable than gamma distributed radii models with similar fitting performance.
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The parameters for simulation of the four-compartment diffusion-weighted signal were chosen to obtain representative diffusion properties and image contrasts.
“The parameters for simulation of the four-compartment diffusion-weighted signal were chosen to obtain representative diffusion properties and image contrasts (compare75 for details on the models).”
From Tractography-based connectomes are dominated by false-positive connections (2016) · cites this paper as [75]
The maximum AD and RD values in chronic MS lesions within highly coherent fiber tracts indicate some degree of diffusion restriction and residual anisotropy even in severely damaged white matter, attributed to lesional gliosis and axonal tortuosity.
“We previously reported that in chronic MS lesions situated within highly coherent fiber tracts the maximum AD and RD values do not exceed 2.5 μm2/ms and 1.7 μm2/ms respectively [24], indicating some degree of diffusion restriction and residual anisotropy even in severely damaged white matter, which was attributed to lesional gliosis and axonal tortuosity [8–10, 25].”
From Differentiating axonal loss and demyelination in chronic MS lesions: A novel approach using single streamline diffusivity analysis (2021) · cites this paper as [10]
Tractography results are sensitive to the choice of the underlying fiber tracking algorithm, which can affect subsequent quantitative analyses using the tractography data.
“It is important to note that tractography is known to be sensitive to the choice of the underlying fiber tracking algorithms, which can further affect any subsequent quantitative analyses using the tractography data.”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Panagiotaki et al., 2012
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“While more complex models [7–9] may help to better explain the observed experimental or clinical data [10], they require very high quality acquisition and measurements that may not be feasible in a clinical setting.”
Background
- Authors
- Panagiotaki, E., Schneider, T., Siow, B., Hall, M. G., Lythgoe, M. F., & Alexander, D. C.
- Affiliations
- University College London (GB)
- UCL Queen Square Institute of Neurology (GB)
- Corresponding author
- Panagiotaki, Eleftheria
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2012-02
- Volume
- 59
- Issue
- 3
- Pages
- 2241-2254
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2011015491
- Cited by
- 422
- References
- 50
- Citations by year
- 2026: 8 · 2025: 26 · 2024: 23 · 2023: 14 · 2022: 22 · 2021: 45 · 2020: 41 · 2019: 36 · 2018: 42 · 2017: 43 · 2016: 35 · 2015: 35 · 2014: 22 · 2013: 22 · 2012: 8
- FWCI
- 12.69
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- EPSRC
- 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
- Physical Sciences › Physics and Astronomy › Nuclear and High Energy Physics › NMR spectroscopy and applications
- Keywords
- diffusion MRI, white matter, multi-compartment model, model taxonomy, diffusion tensor model, ball-and-stick model, pore size, model stability, rat brain
- MeSH terms
- Algorithms, Animals, Axons, Bayes Theorem, Brain, Brain Chemistry, Classification, Image Processing, Computer-Assisted, Male, Models, Anatomic, Terminology as Topic, Water, Models, Statistical, Tissue Fixation, Rats, Sprague-Dawley, Rats, Diffusion Tensor Imaging
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