Open AccessProbing axons using multi‐compartmental diffusion in multiple sclerosis
Author(s) -
Bagnato Francesca,
Franco Giulia,
Li Hua,
Kaden Enrico,
Ye Fei,
Fan Run,
Chen Amalie,
Alexander Daniel C.,
Smith Seth A.,
Dortch Richard,
Xu Junzhong
Publication year - 2019
Publication title -
annals of clinical and translational neurology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.824
H-Index - 42
ISSN - 2328-9503
DOI - 10.1002/acn3.50836
Subject(s) - medicine , multiple sclerosis , diffusion , diffusion mri , neuroscience , computational biology , physical medicine and rehabilitation , immunology , magnetic resonance imaging , radiology , physics , biology , thermodynamics
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 ( HC s) 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 ( V ax ), and effective neural diffusivity ( D ax ) parametric maps were fitted. Differences in AD , V ax , and D ax 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 ( cBH s) and T2‐lesions ( P ≤ 0.0016), in V ax and AD between T2‐lesions and normal appearing white matter ( NAWM ) ( P < 0.0001), but not between the NAWM and normal WM in HC s. Inverse correlations were seen between V ax and AD in cBH s ( r = −0.750, P = 0.02); in T2‐lesions D ax values were associated with V ax ( 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.