Distinguishing Healthy Ageing from Dementia: a Biomechanical Simulation of Brain Atrophy using Deep Networks
This work addresses the challenge of distinguishing dementia from normal aging for medical researchers, though it appears incremental as it applies existing biomechanical modeling with deep networks to a specific domain.
The researchers tackled the problem of simulating brain atrophy in healthy aging versus Alzheimer's Disease by developing a deep learning framework for hyper-elastic strain modeling, which successfully differentiated between the two patterns using ADNI cohort data.
Biomechanical modeling of tissue deformation can be used to simulate different scenarios of longitudinal brain evolution. In this work,we present a deep learning framework for hyper-elastic strain modelling of brain atrophy, during healthy ageing and in Alzheimer's Disease. The framework directly models the effects of age, disease status, and scan interval to regress regional patterns of atrophy, from which a strain-based model estimates deformations. This model is trained and validated using 3D structural magnetic resonance imaging data from the ADNI cohort. Results show that the framework can estimate realistic deformations, following the known course of Alzheimer's disease, that clearly differentiate between healthy and demented patterns of ageing. This suggests the framework has potential to be incorporated into explainable models of disease, for the exploration of interventions and counterfactual examples.