Abstract #1293

Gaussian Process Progression Modelling of structural MRI changes in Huntington’s disease

Peter A. Wijeratne^1,2, Sara Garbarino³, Eileanoir B. Johnson², Sarah Gregory², Rachael I. Scahill², Sarah J. Tabrizi², Marco Lorenzi³, and Daniel C. Alexander¹

¹Department of Computer Science, University College London, London, United Kingdom, ²Department of Neurodegenerative Disease, University College London, London, United Kingdom, ³Université Côte d’Azur, Valbonne, France

Longitudinal measurements of brain atrophy using structural T1-weighted MRI (sMRI) can provide powerful biomarkers for clinical trials in neurodegenerative diseases. Here we use the latest advances in disease progression modelling, specifically the Gaussian Process Progression Model (GPPM), to untangle the effects of inter-subject variability, measurement noise and individual disease stage on longitudinal sMRI measurements in Huntington’s disease (HD). We use GPPM to estimate, for the first time, the relative timescale of sub-cortical atrophy in HD, and identify when sMRI provides additional information to genetics. We conclude that GPPM could increase power over standard imaging biomarkers for clinical trials in HD.

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