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Simulating Alzheimer's disease progression with personalised digital brain models

Abstract : Simulating the effects of Alzheimer's disease on the brain is essential to better understand, predict and control how the disease progresses in patients. Our limited understanding of how disease mechanisms lead to visible changes in brain images and clinical examination hampers the development of biophysical simulations. Instead, we propose a statistical learning approach, where the repeated observations of several patients over time are used to synthesise personalised digital brain models. They provide spatiotemporal views of structural and functional brain alterations and associated scenarios of cognitive decline at the individual level. We show that the personalisation of the models to unseen subjects reconstructs their progression with errors of the same order as the uncertainty of the measurements. Simulation of synthetic patients generalise the distributions of the data in the training cohort. The analysis of factors modulating disease progression evidences a prominent sexual dimorphism and probable compensatory mechanisms in APEO-$\varepsilon$4 carriers. This first-of-its-kind simulator offers an unparalleled way to explore the heterogeneity of the disease's manifestation on the brain, and to predict its progression in each patient.
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Contributor : Stanley Durrleman <>
Submitted on : Sunday, December 23, 2018 - 8:55:53 PM
Last modification on : Friday, September 18, 2020 - 2:35:01 PM


  • HAL Id : hal-01964821, version 1


Igor Koval, Alexandre Bône, Maxime Louis, Simona Bottani, Arnaud Marcoux, et al.. Simulating Alzheimer's disease progression with personalised digital brain models. 2018. ⟨hal-01964821⟩