Modelling and predicting the progression of neurodegenerative diseases
Speaker: Stanley Durrlem
Bio
Dr. Stanley Durrleman is senior researcher at Inria, head of the ARAMIS Lab at the Paris Brain Institute (ICM) on the campus of the Pitié-Salpêtrière hospital. He is fellow of the Paris AI research institute (PRAIRIE). He holds a PhD in applied mathematics from the university of Nice (2010) and a habilitation from Sorbonne University in Paris (2018). His research interests lie in the field of mathematical modeling and statistical learning applied to imaging and medical data. S. Durrleman has received several awards including the second Gilles Kahn award for the best dissertation in computer science in 2010, a starting grant from the European research council (ERC) in 2015 and was the first laureate of a Sanofi iDEA award outside of the USA in 2019. In 2020, he received the Inria – Académie des Sciences young researcher award.
Abstract
In this talk, we will review disease course mapping, a statistical technique aiming to chart the range of trajectories of a series of imaging biomarkers and clinical endpoints changing during disease progression. The technique relies on differential geometric principles and may be used for any data that can be represented on Riemannian manifolds. It uniquely decompose variations due differences in the dynamics of the progression from differences due to the presentation of the disease.
We will show that this technique can forecast the values of the biomarkers and clinical endpoints with smaller errors than state-of-the-art methods. Such predictions can be used, in turn, to design clinical trials with better statistical power by selecting patients with homogeneous progression profiles.
We will illustrate these methods on three therapeutic areas: Alzheimer, Parkinson and Huntington disease.