Learning to synthesise the ageing brain without longitudinal data

12/04/2019
by   Tian Xia, et al.
19

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

page 6

page 7

page 8

page 9

research
11/26/2020

Bidirectional Modeling and Analysis of Brain Aging with Normalizing Flows

Brain aging is a widely studied longitudinal process throughout which th...
research
09/14/2022

Continuous longitudinal fetus brain atlas construction via implicit neural representation

Longitudinal fetal brain atlas is a powerful tool for understanding and ...
research
08/28/2022

Generative Modelling of the Ageing Heart with Cross-Sectional Imaging and Clinical Data

Cardiovascular disease, the leading cause of death globally, is an age-r...
research
05/27/2023

Explainable Brain Age Prediction using coVariance Neural Networks

In computational neuroscience, there has been an increased interest in d...
research
06/28/2021

A Diffeomorphic Aging Model for Adult Human Brain from Cross-Sectional Data

Normative aging trends of the brain can serve as an important reference ...
research
05/07/2021

Interpretable machine learning for high-dimensional trajectories of aging health

We have built a computational model for individual aging trajectories of...
research
03/27/2018

Disease-Atlas: Navigating Disease Trajectories with Deep Learning

Joint models for longitudinal and time-to-event data are commonly used i...

Please sign up or login with your details

Forgot password? Click here to reset