Abstract #1514

Data-driven model of Parkinson’s disease progression performs precision staging with magnetic resonance imaging biomarkers

Neil P Oxtoby¹, Leon M Aksman², Louise-Ann Leyland³, Rimona S Weil³, and Daniel C Alexander¹

¹Department of Computer Science, University College London, London, United Kingdom, ²Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom, ³Department of Neurodegenerative Diseases, University College London, London, United Kingdom

We estimate a data-driven signature of de novo Parkinson's disease progression as a sequence of disease events. We show that clinical decline in classic markers precedes grey-matter and white-matter neurodegeneration estimated from T1-weighted MRI and diffusion-weighted MRI. Using only cross-sectional data from the PPMI data set, we show model utility for fine-grained staging/stratification of patients, which holds promise for future clinical applications.

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