Axonal injury is a key contributor to physical disability in persons with multiple sclerosis (pwMS). Yet, assessing axonal damage in vivo is challenged by the lack of pathologically and topographically specific imaging methods.
We use the spherical mean technique (SMT) and neurite orientation density and dispersion index (NODDI) combined with high-resolution probabilistic tractography and propose an improved assessment of the degree of regional axonal injury and its association with measures of disability in pwMS.
Eighteen pwMS and nine age-sex matched heathy controls underwent a brain MRI inclusive of clinical scans, SMT and NODDI. Parametric maps of the apparent axonal volume fraction (Vax), intrinsic diffusivity (Dax), neurite density index (ndi), orientation dispersion index (odi), and isotropic volume fraction (isovf) were estimated. Tract-specific values were measured in transcallosal (TC) and corticospinal (CS) white matter tracts implicated with motor functions. This included the TC bundles from the paracentral lobules, and both the TC and the CS fibers from the ventral premotor areas, dorsal premotor areas, presupplementary motor areas, supplementary motor areas, and primary motor cortex, all of which were reconstructed by probabilistic tractography. Unpaired t-tests assessed group-differences in tract-specific SMT and NODDI-derived metrics between healthy controls and pwMS, and Spearman rank correlations analyses assessed associations between SMT and NODDI metrics and physical disability metrics.
Differences (p<=0.018) were seen only for the isovf of the TC bundles from the paracentral lobules, the presupplementary motor areas and supplementary motor areas, and both the TC and the CS fibers from the ventral premotor areas. However, associations were seen between several NODDI derived metrics and clinical scores of motor impairment (p<=0.054).
Our preliminary findings show that NODDI-derived isovf has a higher radiological discriminatory capacity compared to SMT and NODDI-derived measures, but several NODDI and SMT indices measured in topographically specific regions explain motor disability variations in pwMS.