Abstract #1933

Evaluating the sensitivity of univariate and multivariate techniques on diffusion-derived metrics in classification of early Parkinson’s disease patients

Virendra R Mishra¹, Zhengshi Yang¹, Karthik Sreenivasan¹, Xiaowei Zhuang¹, and Dietmar Cordes¹

¹Imaging, Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, United States

In this study, we utilized the diffusion MRI (dMRI) data of early Parkinson’s disease (PD) patients and healthy controls (HC) from the Parkinson’s Progressive Markers Initiative (PPMI) database and performed a plethora of multivariate and univariate statistical tests ranging from voxelwise measures, skeleton-wise measures from both TBSS and DTI-TK, and region of interest (ROI) analysis of major white matter tracts from JHU atlas at various smoothing levels. Our study revealed only voxelwise measures could classify HC from PD patients if a minimum smoothing level has reached, and skeleton-wise and ROI analysis (both univariate and multivariate) were associated with the disease.

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