Cyclotron Research Centre Attribution Share Alike Yes University of Li├Ęge NITRC High-quality diffusion-weighted imaging of Parkinson's disease Python Christophe Phillips This project contains data and analysis pipelines for a set of 53 subjects in a cross-sectional Parkinson's disease (PD) study. The dataset contains diffusion-weighted images (DWI) of 27 PD patients and 26 age, sex, and education-matched control subjects. The DWIs were acquired with 120 unique gradient directions, b=1000 and b=2500 s/mm2, and isotropic 2.4 mm3 voxels. The acquisition used a twice-refocused spin echo sequence in order to avoid distortions induced by eddy currents. Processing scripts for the paper can be found on Github: 2014-9-23 Demographic data 2014-6-18 Motion-corrected diffusion-weighted images 2014-6-18 Normalized TDI Maps High-quality diffusion-weighted imaging of Parkinson's disease Data, Parkinson Disease, Clinical Neuroinformatics, MR, Attribution Share Alike, English, NiPyPe, Python, NIfTI-1,