IntroductionThere is a need to develop imaging methods sensitive to axonal injury in multiple sclerosis (MS), given the prominent impact of axonal pathology on disability and outcome. Advanced multi-compartmental diffusion models offer novel indices sensitive to white matter microstructure. One such model, neurite orientation dispersion and density imaging (NODDI), is sensitive to neurite morphology, providing indices of apparent volume fractions of axons (vin), isotropic water (viso) and the dispersion of fibers about a central axis (orientation dispersion index, ODI). NODDI has yet to be studied for its sensitivity to spinal cord pathology. Here, we investigate the feasibility and utility of NODDI in the cervical spinal cord of MS patients.MethodsNODDI was applied in the cervical spinal cord in a cohort of 8 controls and 6 MS patients. Statistical analyses were performed to test the sensitivity of NODDI-derived indices to pathology in MS (both lesion and normal appearing white matter NAWM). Diffusion kurtosis imaging (DKI) and diffusion tensor imaging (DTI) analysis were also performed to compare with NODDI.ResultsA decrease in NODDI-derived vin was observed at the site of the lesion (p < 0.01), whereas a global increase in ODI was seen throughout white matter (p < 0.001). DKI-derived mean kurtosis (MK) and radial kurtosis (RK) and DTI-derived fractional anisotropy (FA) and radial diffusivity (RD) were all significantly different in MS patients (p < 0.02), however NODDI provided higher contrast between NAWM and lesion in all MS patients.ConclusionNODDI provides unique contrast that is not available with DKI or DTI, enabling improved characterization of the spinal cord in MS.
The purpose of this work was to evaluate the feasibility and reproducibility of the spherical mean technique (SMT), a multi-compartmental diffusion model, in the spinal cord of healthy controls, and to assess its ability to improve spinal cord characterization in multiple sclerosis (MS) patients at 3 T. SMT was applied in the cervical spinal cord of eight controls and six relapsing-remitting MS patients. SMT provides an elegant framework to model the apparent axonal volume fraction v , intrinsic diffusivity D , and extra-axonal transverse diffusivity D (which is estimated as a function of v and D ) without confounds related to complex fiber orientation distribution that reside in diffusion MRI modeling. SMT's reproducibility was assessed with two different scans within a month, and SMT-derived indices in healthy and MS cohorts were compared. The influence of acquisition scheme on SMT was also evaluated. SMT's v , D , and D measurements all showed high reproducibility. A decrease in v was observed at the site of lesions and normal appearing white matter (p < 0.05), and trends towards a decreased D and increased D were seen. Importantly, a twofold reduction in acquisition yielded similarly high accuracy with SMT. SMT provides a fast, reproducible, and accurate method to improve characterization of the cervical spinal cord, and may have clinical potential for MS patients.
Glutamate-sensitive contrast was significantly increased in the prefrontal cortex of MS patients with accumulated disability ( p < 0.05). In addition, glutamate-sensitive contrast in the prefrontal cortex was significantly correlated with symbol digit modality test ( r = -0.814) and choice reaction time ( r = 0.772) scores in patients ( p < 0.05), suggesting that GluCEST MRI may have utility as a marker for GM pathology and CI.
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