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

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