Although multiple sclerosis (MS) has been recognized as an inflammatory white matter disease, widespread microstructural changes throughout the brain including cortical and subcortical gray matter have also been reported. With substantial individual variation seen in MS, biomarkers that can characterize disease subtypes and progression, particularly in relation with clinical outcome and cognitive function, are currently limited. Measures of microstructural changes in gray and white matter as well as specific brain regions could offer a promising source of such biomarkers [1]. In this study, we measured non-Gaussian and Gaussian diffusion parameters using diffusional kurtosis imaging (DKI) and DTI to characterize region and tissue-type specific microstructural changes in three MS subtypes, relapsing-remitting (RRMS), secondary-progressive (SPMS), primary-progressive MS (PPMS). Their association with cognitive function and clinical status has also been investigated.Three subtypes (RRMS, SPMS, PPMS) of patients with MS (18-65 years old, n=20 per group) and their closely age- and sex-matched healthy controls (CTL) were studied. All MR scans were performed using a 3 T Siemens Skyra system. DKI/DTI data were acquired using a spin-echo EPI sequence with 3 b-values (b = 0, 1000, and 2000 s/mm²) and 30 directions. Diffusion MRI parameters were calculated on a pixel by pixel basis using the DKE software package [2]. Calculated diffusion parameters include fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (DA), radial diffusivity (DR), mean kurtosis (MK), axial kurtosis (KA) and radial kurtosis (KR). Anatomical MR images were also acquired for defining regions of interest (ROI) and MS lesion delineation, which included MPRAGE and T2-weighted MRI. Nonlinear co-registration of DTI/DKI data to T1-weighted MRI was performed using FSL (FMRIB, Oxford University). Regional values of DKI/DTI parameters were calculated from the ROIs obtained from brain tissue segmentation in SPM (University College London) and parcellation in FreeSurfer (MGH) (Fig. 1). A battery of cognitive tests were performed within 3 days of MR scans, including verbal and visual memory and executive planning, and information processing speed. Clinical measures include Expanded Disability Severity Score (EDSS) and Fatigue Severity Scale. ANOVA and Spearman rank correlation analyses were performed for group comparisons and for association between clinical/cognitive measures and DTI/DKI parameters, respectively.Calculated diffusion parametric maps are shown in Fig. 1. In general, DTI parameters (FA, MD, DA DR) were more sensitive in distinguishing subtypes of MS than DKI parameters (MK, KA, and KR). MD, DA, and DR in the corpus stratum (caudate, putamen, and globus pallidus) were the most sensitive measures differentiating degenerative phases of MS (SPMS, PPMS) from RRMS or controls (Fig. 3). MD, DA and DR in cortical gray matter, and FA, MD, DR, MK, KA, and KR in the corpus callosum could differentiate patients with MS from controls. Correlation analysis between DKI/DTI parameters and clinical/cognitive measures showed that MD in the corpus striatum was the most correlated with EDSS (r=0.54, p<0.001), and DA was correlated with EDSS in the most brain regions including cortical gray matter (r=0.38, p<0.01), the corpus callosum (r=0.35, p<0.02), and thalamus (r=0.32, p<0.05). Particularly, MD in the corpus callosum was correlated with the duration of MS (r=33, p<0.02). Strongest correlations were observed between MD, DA, and DR in various brain regions and cognitive function (information processing speed and memory). FA in the corpus callosum and cortical white matter, MD in the corpus striatum and brain stem, MK in the corpus callosum, and DA in the thalamus showed correlation with executive planning.A variety of DKI/DTI parameters could detect microstructure changes that are related to pathologic features of MS in a region specific manner. The observed correlation between these parameters and cognitive function and clinical status promises the role of DKI/DTI parameters as sensitive biomarkers of MS pathophysiology.