Noninvasive quantification of axon radii using diffusion MRI

Veraart, Jelle; Nunes, Daniel; Rudrapatna, Umesh; Fieremans, Els; Jones, Derek K.; Novikov, Dmitry S.; Shemesh, Noam

doi:10.7554/elife.49855

Cited by 158 publications

(363 citation statements)

References 99 publications

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“…From in vivo human data, Veraart et al . [21] estimated a radial diffusivity of ~ 0.01 μm 2 /ms across the white matter. Here, using simulated data we found that tensor-like (rather than stick-like) fibres with a radial diffusivity of ~ 0.01 μm 2 /ms led to the over estimation of axial diffusivity, from d ‖ = 2.2 μm 2 /ms to d ‖ = 2.68 μm 2 /ms (Figure 3b).…”

Section: Discussionmentioning

confidence: 99%

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Estimating axial diffusivity in the NODDI model

Howard

Lange

Mollink

et al. 2020

Preprint

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By analysing the diffusion MRI signal, we can infer information about the microscopic structure of the brain. Two parameters of interest - the intra-axonal axial diffusivity and fibre orientation dispersion - are potential biomarkers for very different aspects of the white matter microstructure, yet they are difficult to disentangle. The parameters covary such that, if one is not accurately accounted for, the other will be biased. In this work we use high b-value data to isolate the signal from the intra-axonal compartment and resolve any degeneracies with the extra-axonal compartment. In the high b-value regime, we then use a model of dispersed sticks to estimate the intra-axonal axial diffusivity and fibre orientation distribution on a voxelwise basis. Our results in in vivo, human data show an intra-axonal axial diffusivity of ~ 2.3 – 3 μm2/ms, where 3 μm2/ms is the diffusivity of free water at 37°C. The intra-axonal axial diffusivity is seen to vary considerably across the white matter. For example, in the corpus callosum we find high values in the genu and splenium, and lower values in the midbody. Furthermore, the axial diffusivity and orientation dispersion appear negatively correlated, behaviour which we show is consistent with the presence of fibre undulations but not consistent with a degeneracy between fanning fibres and axial diffusivity. Finally, we demonstrate that the parameter maps output from Neurite Orientation Dispersion and Density Imaging (NODDI) change substantially when the assumed axial diffusivity was increased from 1.7 to 2.5 or 3 μm2/ms.

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Section: Discussionmentioning

confidence: 99%

“…To solve for all parameters simultaneously and without parameter degeneracy, the dispersed stick model requires data with three b-values or shells, each with negligible contributions from the extra-axonal compartment i.e. b > 6, 000ms/μm 2 [21].…”

Section: Theorymentioning

confidence: 99%

Estimating axial diffusivity in the NODDI model

Howard

Lange

Mollink

et al. 2020

Preprint

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“…Diffusion-weighted MRI (dMRI) is a particularly relevant neuroimaging modality to probe cellular features, far below the resolution of the imaging experiment (Assaf et al, 2008;Alexander et al, 2010;Romascano et al, 2020;McNab et al, 2013;Fan et al, 2020;Sepehrband et al, 2016b;Veraart et al, 2020;Huang et al, 2020). Indeed, dMRI is sensitive to a wide range of tissue microstructural parameters because the signal is sensitized to the micrometer length scale of the diffusion of water molecules (Tanner, 1979).…”

Section: Introductionmentioning

confidence: 99%

The variability of MR axon radii estimates in the human brain

Veraart

et al. 2020

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The noninvasive quantification of axonal morphology provides an exciting avenue to gain understanding of the function and structure of the central nervous system. Accurate non-invasive mapping of micron-sized axon radii using commonly applied neuroimaging techniques, i.e., diffusion-weighted MRI, has been bolstered by recent hardware developments. In this work, we present the whole brain characterization of the effective MR axon radius and evaluate the inter- and intra-scanner test-retest repeatability and reproducibility to promote the further development of the effective MR axon radius as a neuroimaging biomarker. We observe a coefficient-of-variability of approximately 10% in the voxelwise estimation of the effective MR radius in the test-retest analysis, but demonstrate that the performance can be improved fourfold using a customized along-tract analyses.

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“…Using both myelin and axonal diameter data would allow one to characterize more thoroughly brain pathologies such as multiple sclerosis, Parkinson's or Alzheimer's disease, more thoroughly. However, axonal diameter can be accurately measured only with high gradients (300 mT/m) (Veraart et al, 2020) and is therefore not a measure that one can have on a clinical scanner yet.…”

Section: Discussionmentioning

confidence: 99%

The R1-weighted connectome: complementing brain networks with a myelin-sensitive measure

Boshkovski

Kocarev

Cohen‐Adad

et al. 2020

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Myelin plays a crucial role in how well information travels between brain regions. Many neurological diseases affect the myelin in the white matter, making myelin-sensitive metrics derived from quantitative MRI of potential interest for early detection and prognosis of those conditions. Complementing the structural connectome, obtained with diffusion MRI tractography, with a myelin sensitive measure could result in a more complete model of structural brain connectivity and give better insight into how the myeloarchitecture relates to brain function. In this work we weight the connectome by the longitudinal relaxation rate (R1) as a measure sensitive to myelin, and then we assess its added value by comparing it with connectomes weighted by the number of streamlines (NOS). Our analysis reveals differences between the two connectomes both in the distribution of their weights and the modular organization. Additionally, the rank-based analysis shows that R1 is able to separate different classes (unimodal and transmodal), following a functional gradient. Overall, the R1-weighted connectome provides a different perspective on structural connectivity taking into account white matter myeloarchitecture.Author summaryIn the present work, we integrate a myelin sensitive MRI metric into the connectome and compare it with a connectome weighted with a standard diffusion-derived metric, number of streamlines (NOS). Our analysis shows that the R1-weighted connectome complements the NOS-weighted connectome. We show that the R1-weighted average distribution does not follow the same trend as the NOS strength distribution, and the two connectomes exhibit different modular organization. We also show that unimodal cortical regions tend to be connected by more streamlines, but the connections exhibit a lower R1-weighted average, while the transmodal regions tend to have a higher R1-weighted average but fewer streamlines. In terms of network communication, this could imply that the unimodal regions require more connections with lower myelination, whereas the transmodal regions take more myelinated, but fewer, connections for a reliable transfer of information.

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Noninvasive quantification of axon radii using diffusion MRI

Cited by 158 publications

References 99 publications

Estimating axial diffusivity in the NODDI model

Estimating axial diffusivity in the NODDI model

The variability of MR axon radii estimates in the human brain

The R1-weighted connectome: complementing brain networks with a myelin-sensitive measure

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