Distinguishing Healthy Ageing from Dementia: a Biomechanical Simulation of Brain Atrophy using Deep Networks

TL;DR

This paper introduces a deep learning framework that models brain tissue deformation to distinguish healthy aging from Alzheimer's disease, providing realistic deformation estimates from MRI data.

Contribution

It presents a novel deep learning approach for biomechanical simulation of brain atrophy, differentiating healthy aging from dementia using MRI data.

Findings

Framework accurately estimates brain deformations.

Differentiates healthy aging from Alzheimer's patterns.

Potential for explainable disease modeling.

Abstract

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.