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

arXiv: https://arxiv.org/abs/2108.08214 · HTML: https://mlsanity.com/paper/2108.08214/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 25 references that are in the map.
- First posted on arXiv 18 Aug 2021.

## Builds on (most cited first, 6 of 6)

- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [Deep Structural Causal Models for Tractable Counterfactual Inference](https://mlsanity.com/paper/2006.06485/) (2020)
- [Visual Feature Attribution using Wasserstein GANs](https://mlsanity.com/paper/1711.08998/) (2017)
- [ICAM: Interpretable Classification via Disentangled Representations and Feature Attribution Mapping](https://mlsanity.com/paper/2006.08287/) (2020)
- [Transfer Learning with intelligent training data selection for prediction of Alzheimer's Disease](https://mlsanity.com/paper/1906.01160/) (2019)
- [Interpretation of Disease Evidence for Medical Images Using Adversarial Deformation Fields](https://mlsanity.com/paper/2007.01975/) (2020)

## Built on by (most cited first, 3 of 3)

- [Synthesizing Individualized Aging Brains in Health and Disease with Generative Models and Parallel Transport](https://mlsanity.com/paper/2502.21049/) (2025)
- [NEUBORN: The Neurodevelopmental Evolution framework Using BiOmechanical RemodelliNg](https://mlsanity.com/paper/2508.09757/) (2025)
- [Integrating Mechanistic and Data-Driven Models for Neurological Disorders through Differentiable Programming](https://mlsanity.com/paper/2606.06094/) (2026)

## Abstract (arXiv)

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.