Abstract
Mapping gray matter (GM) pathology in Parkinson's disease (PD) with conventional MRI is challenging, and the need for more sensitive brain imaging techniques is essential to facilitate early diagnosis and assessment of disease severity. GM microstructure was assessed with GM-based spatial statistics applied to diffusion kurtosis imaging (DKI) and neurite orientation dispersion imaging (NODDI) in 30 participants with PD and 28 age- and gender-matched controls. These were compared with currently used assessment methods such as diffusion tensor imaging (DTI), voxel-based morphometry (VBM), and surface-based cortical thickness analysis. Linear discriminant analysis (LDA) was also used to test whether subject diagnosis could be predicted based on a linear combination of regional diffusion metrics. Significant differences in GM microstructure were observed in the striatum and the frontal, temporal, limbic, and paralimbic areas in PD patients using DKI and NODDI. Significant correlations between motor deficits and GM microstructure were also noted in these areas. Traditional VBM and surface-based cortical thickness analyses failed to detect any GM differences. LDA indicated that mean kurtosis (MK) and intra cellular volume fraction (ICVF) were the most accurate predictors of diagnostic status. In conclusion, DKI and NODDI can detect cerebral GM abnormalities in PD in a more sensitive manner when compared with conventional methods. Hence, these methods may be useful for the diagnosis of PD and assessment of motor deficits. Hum Brain Mapp 38:3704-3722, 2017. © 2017 Wiley Periodicals, Inc.