# Predicting conversion of mild cognitive impairment to Alzheimer's disease

arXiv: https://arxiv.org/abs/2203.04725 · HTML: https://mlsanity.com/paper/2203.04725/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 28 references that are in the map.
- First posted on arXiv 8 Mar 2022.

## Builds on (most cited first, 5 of 5)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Generating Diffusion MRI scalar maps from T1 weighted images using generative adversarial networks](https://mlsanity.com/paper/1810.02683/) (2018)
- [BrainNetGAN: Data augmentation of brain connectivity using generative adversarial network for dementia classification](https://mlsanity.com/paper/2103.08494/) (2021)
- [TransforMesh: A Transformer Network for Longitudinal modeling of Anatomical Meshes](https://mlsanity.com/paper/2109.00532/) (2021)

## Built on by (most cited first, 1 of 1)

- [Hierarchical Multiscale Structure-Function Coupling for Brain Connectome Integration](https://mlsanity.com/paper/2603.20680/) (2026)

## Abstract (arXiv)

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.