Application and evaluation of NODDI in the cervical spinal cord of multiple sclerosis patients.

By S, Xu J, Box BA, Bagnato FR, Smith SA
Neuroimage Clin. 2017 15: 333-342

PMID: 28560158 · PMCID: PMC5443965 · DOI:10.1016/j.nicl.2017.05.010

INTRODUCTION - There 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 (v), isotropic water (v) 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.

METHODS - NODDI 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.

RESULTS - A decrease in NODDI-derived v was observed at the site of the lesion ( < 0.01), whereas a global increase in ODI was seen throughout white matter ( < 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 ( < 0.02), however NODDI provided higher contrast between NAWM and lesion in all MS patients.

CONCLUSION - NODDI provides unique contrast that is not available with DKI or DTI, enabling improved characterization of the spinal cord in MS.

MeSH Terms (11)

Adult Cervical Cord Diffusion Magnetic Resonance Imaging Feasibility Studies Female Humans Image Interpretation, Computer-Assisted Middle Aged Multiple Sclerosis, Relapsing-Remitting Neurites Reproducibility of Results

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