Predicting Cognitive Decline with Deep Learning of Brain Metabolism and Amyloid Imaging

TL;DR

This paper presents a deep learning framework that predicts cognitive decline in MCI patients using baseline PET scans, outperforming traditional methods and demonstrating strong correlation with cognitive changes.

Contribution

A novel CNN-based method that predicts Alzheimer’s progression from baseline PET scans without complex preprocessing, outperforming conventional quantification techniques.

Findings

Prediction accuracy of 84.2% for AD conversion.

CNN approach significantly outperforms traditional methods (p < 0.05).

Network scores correlate with longitudinal cognitive changes.

Abstract

For effective treatment of Alzheimer disease (AD), it is important to identify subjects who are most likely to exhibit rapid cognitive decline. Herein, we developed a novel framework based on a deep convolutional neural network which can predict future cognitive decline in mild cognitive impairment (MCI) patients using flurodeoxyglucose and florbetapir positron emission tomography (PET). The architecture of the network only relies on baseline PET studies of AD and normal subjects as the training dataset. Feature extraction and complicated image preprocessing including nonlinear warping are unnecessary for our approach. Accuracy of prediction (84.2%) for conversion to AD in MCI patients outperformed conventional feature-based quantification approaches. ROC analyses revealed that performance of CNN-based approach was significantly higher than that of the conventional quantification methods (p < 0.05). Output scores of the network were strongly correlated with the longitudinal change in cognitive measurements. These results show the feasibility of deep learning as a tool for predicting disease outcome using brain images.