# A model of brain morphological changes related to aging and Alzheimer's disease from cross-sectional assessments

arXiv: https://arxiv.org/abs/1905.09826 · HTML: https://mlsanity.com/paper/1905.09826/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2019.

## Built on by (most cited first, 12 of 12)

- [Equitable modelling of brain imaging by counterfactual augmentation with morphologically constrained 3D deep generative models](https://mlsanity.com/paper/2111.14923/) (2021)
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- [IdenBAT: Disentangled Representation Learning for Identity-Preserved Brain Age Transformation](https://mlsanity.com/paper/2410.16945/) (2024)
- [A deformation-based morphometry framework for disentangling Alzheimer's disease from normal aging using learned normal aging templates](https://mlsanity.com/paper/2311.08176/) (2023)
- [Patch-based Intuitive Multimodal Prototypes Network (PIMPNet) for Alzheimer's Disease classification](https://mlsanity.com/paper/2407.14277/) (2024)
- [Neural Autoregressive Modeling of Brain Aging](https://mlsanity.com/paper/2507.22954/) (2025)
- [XADLiME: eXplainable Alzheimer's Disease Likelihood Map Estimation via Clinically-guided Prototype Learning](https://mlsanity.com/paper/2207.13223/) (2022)
- [Identity Preserving Latent Diffusion for Brain Aging Modeling](https://mlsanity.com/paper/2503.09634/) (2025)
- [Spatio-Temporal Similarity Measure based Multi-Task Learning for Predicting Alzheimer's Disease Progression using MRI Data](https://mlsanity.com/paper/2311.03557/) (2023)
- [BrainNorm: A Foundation Model that knows Normal via Semantic Atlas Pretraining](https://mlsanity.com/paper/2608.17521/) (2026)

## Abstract (arXiv)

In this study we propose a deformation-based framework to jointly model the influence of aging and Alzheimer's disease (AD) on the brain morphological evolution. Our approach combines a spatio-temporal description of both processes into a generative model. A reference morphology is deformed along specific trajectories to match subject specific morphologies. It is used to define two imaging progression markers: 1) a morphological age and 2) a disease score. These markers can be computed locally in any brain region. The approach is evaluated on brain structural magnetic resonance images (MRI) from the ADNI database. The generative model is first estimated on a control population, then, for each subject, the markers are computed for each acquisition. The longitudinal evolution of these markers is then studied in relation with the clinical diagnosis of the subjects and used to generate possible morphological evolution. In the model, the morphological changes associated with normal aging are mainly found around the ventricles, while the Alzheimer's disease specific changes are more located in the temporal lobe and the hippocampal area. The statistical analysis of these markers highlights differences between clinical conditions even though the inter-subject variability is quiet high. In this context, the model can be used to generate plausible morphological trajectories associated with the disease. Our method gives two interpretable scalar imaging biomarkers assessing the effects of aging and disease on brain morphology at the individual and population level. These markers confirm an acceleration of apparent aging for Alzheimer's subjects and can help discriminate clinical conditions even in prodromal stages. More generally, the joint modeling of normal and pathological evolutions shows promising results to describe age-related brain diseases over long time scales.