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. 2019 Sep;6(9):1595-1605.
doi: 10.1002/acn3.50836. Epub 2019 Aug 13.

Probing axons using multi-compartmental diffusion in multiple sclerosis

Francesca Bagnato  1   2 , Giulia Franco  1   3 , Hua Li  4 , Enrico Kaden  5 , Fei Ye  6 , Run Fan  6 , Amalie Chen  1 , Daniel C Alexander  5 , Seth A Smith  4 , Richard Dortch  4   7 , Junzhong Xu  4
Affiliations

Probing axons using multi-compartmental diffusion in multiple sclerosis

Francesca Bagnato et al. Ann Clin Transl Neurol. 2019 Sep.

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.

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Conflict of interest statement

None declared.

Figures

Figure 1
Figure 1
Coregistered anatomical MRI and parametric maps. Top row shows T1‐w TSE (A), T2‐w TSE (B), and T2‐w FLAIR (C); bottom row shows AD (D), V ax (E), and D ax (F) maps. Note the cBH visibility on T1‐w SE image and V ax map (white arrow).
Figure 2
Figure 2
Schematic representation of lesions delineation. Lesion delineation is illustrated. Each cBH (yellow region‐of‐interest (ROI) in A and B) was anatomically matched with a correspondent T2‐lesion (green ROI in A and B); each T2‐lesion (green ROIs in C) was anatomically matched with contralateral areas of normal appearing white matter (NAWM, blue ROIs in C).
Figure 3
Figure 3
Correlation plots of scan‐1 and scan‐2 values. Correlation plots showing the associations between scan‐1 and scan‐2 measurements of V ax (A), D ax (B), and AD (C).
Figure 4
Figure 4
cBHs and T2‐lesions SMT‐metrics. V ax (A), D ax (B), and AD (C) values in cBHs (patterned columns) and T2‐lesions (white columns). Data are expressed in mean (columns) + 1 standard deviation (bars).
Figure 5
Figure 5
T2‐lesions and NAWM SMT‐metrics. V ax (A), D ax (B), and AD (C) values in T2‐lesions (patterned columns) and NAWM (white columns). Data are expressed in mean (columns) + 1 standard deviation (bars).
Figure 6
Figure 6
NAWM and NWM SMT‐metrics. V ax (A), D ax (B), and AD (C) values in NAWM (patterned columns) and NWM (white columns). Data are expressed in mean (columns) + 1 standard deviation (bars).
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