Bagnato, F;
Franco, G;
Li, H;
Kaden, E;
Ye, F;
Fan, R;
Chen, A;
... Xu, J; + view all
(2019)
Probing axons using multi-compartmental diffusion in multiple sclerosis.
Annals of Clinical and Translational Neurology
, 6
(9)
pp. 1595-1605.
10.1002/acn3.50836.
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Abstract
OBJECTS: The diffusion-based spherical mean technique (SMT) provides a novel model to relate multi-b-value diffusion magnetic resonance imaging (MRI) data to features of tissue microstructure. We propose the first clinical application of SMT to image the brain of patients with multiple sclerosis (MS) and investigate clinical feasibility and translation. METHODS: Eighteen MS patients and nine age- and sex-matched healthy controls (HCs) underwent a 3.0 Tesla scan inclusive of clinical sequences and SMT images (isotropic resolution of 2 mm). Axial diffusivity (AD), apparent axonal volume fraction (Vax ), and effective neural diffusivity (Dax ) parametric maps were fitted. Differences in AD, Vax , and Dax between anatomically matched regions reflecting different tissues types were estimated using generalized linear mixed models for binary outcomes. RESULTS: Differences were seen in all SMT-derived parameters between chronic black holes (cBHs) and T2-lesions (P ≤ 0.0016), in Vax and AD between T2-lesions and normal appearing white matter (NAWM) (P < 0.0001), but not between the NAWM and normal WM in HCs. Inverse correlations were seen between Vax and AD in cBHs (r = -0.750, P = 0.02); in T2-lesions Dax values were associated with Vax (r = 0.824, P < 0.0001) and AD (r = 0.570, P = 0.014). INTERPRETATIONS: SMT-derived metrics are sensitive to pathological changes and hold potential for clinical application in MS patients.
| Type: | Article |
|---|---|
| Title: | Probing axons using multi-compartmental diffusion in multiple sclerosis |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1002/acn3.50836 |
| Publisher version: | https://doi.org/10.1002/acn3.50836 |
| Language: | English |
| Additional information: | © 2019 The Authors. This is an open access article under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10080127 |
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