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

## TL;DR

This paper introduces a deformation-based generative model that jointly captures brain morphological changes due to aging and Alzheimer's disease, providing interpretable biomarkers for disease progression and aging effects.

## Contribution

It presents a novel spatio-temporal framework that models normal aging and Alzheimer's disease progression simultaneously using MRI data, generating biomarkers and morphological trajectories.

## Key findings

- Markers differentiate clinical conditions despite high variability.
- Model reveals aging effects around ventricles and AD effects in temporal lobe and hippocampus.
- Markers indicate accelerated aging in Alzheimer's patients.

## Abstract

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.