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Diffusion tensor imaging (DTI) has been used to study the effects ofneurodegenerative diseases on neural pathways, which may lead to more reliableand early diagnosis of these diseases as well as a better understanding of howthey affect the brain. We introduce an intelligent visual analytics system forstudying patient groups based on their labeled DTI fiber tract data andcorresponding statistics. The system's AI-augmented interface guides the userthrough an organized and holistic analysis space, including the statisticalfeature space, the physical space, and the space of patients over differentgroups. We use a custom machine learning pipeline to help narrow down thislarge analysis space, and then explore it pragmatically through a range oflinked visualizations. We conduct several case studies using real data from theresearch database of Parkinson's Progression Markers Initiative.

A Predictive Visual Analytics System for Studying Neurodegenerative Disease based on DTI Fiber Tracts