Early Prediction of Alzheimer's Disease Dementia Based on Baseline Hippocampal MRI and 1-Year Follow-Up Cognitive Measures Using Deep Recurrent Neural Networks

TL;DR

This paper presents a deep recurrent neural network model that integrates baseline hippocampal MRI and longitudinal cognitive data to accurately predict the progression from mild cognitive impairment to Alzheimer's dementia.

Contribution

It introduces a novel deep learning approach that leverages longitudinal data and baseline MRI to improve early prediction of AD dementia progression.

Findings

Deep learning model accurately predicts AD progression.

Longitudinal data enhances prediction performance.

Model outperforms previous classification methods.

Abstract

Multi-modal biological, imaging, and neuropsychological markers have demonstrated promising performance for distinguishing Alzheimer's disease (AD) patients from cognitively normal elders. However, it remains difficult to early predict when and which mild cognitive impairment (MCI) individuals will convert to AD dementia. Informed by pattern classification studies which have demonstrated that pattern classifiers built on longitudinal data could achieve better classification performance than those built on cross-sectional data, we develop a deep learning model based on recurrent neural networks (RNNs) to learn informative representation and temporal dynamics of longitudinal cognitive measures of individual subjects and combine them with baseline hippocampal MRI for building a prognostic model of AD dementia progression. Experimental results on a large cohort of MCI subjects have demonstrated that the deep learning model could learn informative measures from longitudinal data for characterizing the progression of MCI subjects to AD dementia, and the prognostic model could early predict AD progression with high accuracy.