# Learning to synthesise the ageing brain without longitudinal data

arXiv: https://arxiv.org/abs/1912.02620 · HTML: https://mlsanity.com/paper/1912.02620/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2019.

## Built on by (most cited first, 30 of 42)

- [Causal Machine Learning for Healthcare and Precision Medicine](https://mlsanity.com/paper/2205.11402/) (2022)
- [Equitable modelling of brain imaging by counterfactual augmentation with morphologically constrained 3D deep generative models](https://mlsanity.com/paper/2111.14923/) (2021)
- [Enhancing Spatiotemporal Disease Progression Models via Latent Diffusion and Prior Knowledge](https://mlsanity.com/paper/2405.03328/) (2024)
- [Benchmarking Counterfactual Image Generation](https://mlsanity.com/paper/2403.20287/) (2024)
- [Brain Latent Progression: Individual-based Spatiotemporal Disease Progression on 3D Brain MRIs via Latent Diffusion](https://mlsanity.com/paper/2502.08560/) (2025)
- [Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging](https://mlsanity.com/paper/2107.09543/) (2021)
- [A survey on GANs for computer vision: Recent research, analysis and taxonomy](https://mlsanity.com/paper/2203.11242/) (2022)
- [CHeart: A Conditional Spatio-Temporal Generative Model for Cardiac Anatomy](https://mlsanity.com/paper/2301.13098/) (2023)
- [Latent Drifting in Diffusion Models for Counterfactual Medical Image Synthesis](https://mlsanity.com/paper/2412.20651/) (2024)
- [Applications of Generative Adversarial Networks in Neuroimaging and Clinical Neuroscience](https://mlsanity.com/paper/2206.07081/) (2022)
- [Deep Structural Causal Shape Models](https://mlsanity.com/paper/2208.10950/) (2022)
- [Synthesizing Individualized Aging Brains in Health and Disease with Generative Models and Parallel Transport](https://mlsanity.com/paper/2502.21049/) (2025)
- [Adversarial Counterfactual Augmentation: Application in Alzheimer's Disease Classification](https://mlsanity.com/paper/2203.07815/) (2022)
- [NODER: Image Sequence Regression Based on Neural Ordinary Differential Equations](https://mlsanity.com/paper/2407.13241/) (2024)
- [Addressing Asynchronicity in Clinical Multimodal Fusion via Individualized Chest X-ray Generation](https://mlsanity.com/paper/2410.17918/) (2024)
- [Causally Steered Diffusion for Automated Video Counterfactual Generation](https://mlsanity.com/paper/2506.14404/) (2025)
- [Generative Autoregressive Transformers for Model-Agnostic Federated MRI Reconstruction](https://mlsanity.com/paper/2502.04521/) (2025)
- [IdenBAT: Disentangled Representation Learning for Identity-Preserved Brain Age Transformation](https://mlsanity.com/paper/2410.16945/) (2024)
- [Temporally-Aware Diffusion Model for Brain Progression Modelling with Bidirectional Temporal Regularisation](https://mlsanity.com/paper/2509.03141/) (2025)
- [Self-Supervised CSF Inpainting with Synthetic Atrophy for Improved Accuracy Validation of Cortical Surface Analyses](https://mlsanity.com/paper/2303.05777/) (2023)
- [Evaluation of 3D Counterfactual Brain MRI Generation](https://mlsanity.com/paper/2508.02880/) (2025)
- [Anatomically Guided Latent Diffusion for Brain MRI Progression Modeling](https://mlsanity.com/paper/2601.14584/) (2026)
- [A Deep Generative Model of Neonatal Cortical Surface Development](https://mlsanity.com/paper/2206.07542/) (2022)
- [Aging prediction using deep generative model toward the development of preventive medicine](https://mlsanity.com/paper/2208.10797/) (2022)
- [A Quantitatively Interpretable Model for Alzheimer's Disease Prediction Using Deep Counterfactuals](https://mlsanity.com/paper/2310.03457/) (2023)
- [Extrapolating Prospective Glaucoma Fundus Images through Diffusion Model in Irregular Longitudinal Sequences](https://mlsanity.com/paper/2410.21130/) (2024)
- [Identity Preserving Latent Diffusion for Brain Aging Modeling](https://mlsanity.com/paper/2503.09634/) (2025)
- [MambaControl: Anatomy Graph-Enhanced Mamba ControlNet with Fourier Refinement for Diffusion-Based Disease Trajectory Prediction](https://mlsanity.com/paper/2505.09965/) (2025)
- [Language-Enhanced Generative Modeling for Amyloid PET Synthesis from MRI and Blood Biomarkers](https://mlsanity.com/paper/2511.02206/) (2025)
- [Forecasting Future Anatomies: Longitudinal Brain Mri-to-Mri Prediction](https://mlsanity.com/paper/2511.02558/) (2025)

## Abstract (arXiv)

How will my face look when I get older? Or, for a more challenging question: How will my brain look when I get older? To answer this question one must devise (and learn from data) a multivariate auto-regressive function which given an image and a desired target age generates an output image. While collecting data for faces may be easier, collecting longitudinal brain data is not trivial. We propose a deep learning-based method that learns to simulate subject-specific brain ageing trajectories without relying on longitudinal data. Our method synthesises images conditioned on two factors: age (a continuous variable), and status of Alzheimer's Disease (AD, an ordinal variable). With an adversarial formulation we learn the joint distribution of brain appearance, age and AD status, and define reconstruction losses to address the challenging problem of preserving subject identity. We compare with several benchmarks using two widely used datasets. We evaluate the quality and realism of synthesised images using ground-truth longitudinal data and a pre-trained age predictor. We show that, despite the use of cross-sectional data, our model learns patterns of gray matter atrophy in the middle temporal gyrus in patients with AD. To demonstrate generalisation ability, we train on one dataset and evaluate predictions on the other. In conclusion, our model shows an ability to separate age, disease influence and anatomy using only 2D cross-sectional data that should be useful in large studies into neurodegenerative disease, that aim to combine several data sources. To facilitate such future studies by the community at large our code is made available at https://github.com/xiat0616/BrainAgeing.