Learning to synthesise the ageing brain without longitudinal data

Brain ageing is a continuous process that is affected by many factors including neurodegenerative diseases. Understanding this process is of great value for both neuroscience research and clinical applications. However, revealing underlying mechanisms is challenging due to the lack of longitudinal data. In this paper, we propose a deep learning-based method that learns to simulate subject-specific brain ageing trajectories without relying on longitudinal data. Our method synthesises aged images using a network conditioned on two clinical variables: age as a continuous variable, and health state, i.e. status of Alzheimer's Disease (AD) for this work, as an ordinal variable. We adopt an adversarial loss to learn the joint distribution of brain appearance and clinical variables and define reconstruction losses that help preserve subject identity. To demonstrate our model, we compare with several approaches using two widely used datasets: Cam-CAN and ADNI. We use ground-truth longitudinal data from ADNI to evaluate the quality of synthesised images. A pre-trained age predictor, which estimates the apparent age of a brain image, is used to assess age accuracy. In addition, we show that we can train the model on Cam-CAN data and evaluate on the longitudinal data from ADNI, indicating the generalisation power of our approach. Both qualitative and quantitative results show that our method can progressively simulate the ageing process by synthesising realistic brain images. The code will be made publicly available at: https://github.com/xiat0616/BrainAgeing.