Predicting conversion of mild cognitive impairment to Alzheimer's disease

TL;DR

This paper introduces a novel self-supervised learning framework that uses routine MRI to predict the progression from mild cognitive impairment to Alzheimer's disease, outperforming existing benchmarks.

Contribution

It develops a contrastive learning approach to generate brain networks from anatomical MRI guided by diffusion MRI, enabling better prediction of disease conversion.

Findings

Proposed method outperforms benchmarks in prediction accuracy.

Model visualizations reveal abnormal white matter changes.

Framework effectively models healthy brain aging trajectories.

Abstract

Alzheimer's disease (AD) is the most common age-related dementia. Mild cognitive impairment (MCI) is the early stage of cognitive decline before AD. It is crucial to predict the MCI-to-AD conversion for precise management, which remains challenging due to the diversity of patients. Previous evidence shows that the brain network generated from diffusion MRI promises to classify dementia using deep learning. However, the limited availability of diffusion MRI challenges the model training. In this study, we develop a self-supervised contrastive learning approach to generate structural brain networks from routine anatomical MRI under the guidance of diffusion MRI. The generated brain networks are applied to train a learning framework for predicting the MCI-to-AD conversion. Instead of directly modelling the AD brain networks, we train a graph encoder and a variational autoencoder to model the healthy ageing trajectories from brain networks of healthy controls. To predict the MCI-to-AD conversion, we further design a recurrent neural networks based approach to model the longitudinal deviation of patients' brain networks from the healthy ageing trajectory. Numerical results show that the proposed methods outperform the benchmarks in the prediction task. We also visualize the model interpretation to explain the prediction and identify abnormal changes of white matter tracts.