  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.
