Predictive Modeling of Longitudinal Data for Alzheimer’s Disease Diagnosis Using RNNs

In this paper, we study the application of Recurrent Neural Networks (RNNs) to discriminate Alzheimer’s disease patients from healthy control individuals using longitudinal neuroimaging data. Distinctions between Alzheimer’s Disease (AD), Mild Cognitive Impairment (MCI), and healthy subjects in a multi-modal heterogeneous longitudinal dataset is a challenging problem due to high similarity between brain patterns, high portions of missing data from different modalities and time points, and inconsistent number of test intervals between different subjects. Due to these challenges, to distinguish AD patients from healthy subjects, conventionally researchers use cross-sectional data when applying deep learning methods in neuroimaging applications. Whereas we propose a method based on RNNS to analyze the longitudinal data. After carefully preprocessing the data to alleviate the inconsistency due to different data sources and various protocols of capturing modalities, we arrange the data and feed it into variations of RNNs, i.e., vanilla Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). The accuracy, F-score, sensitivity, and specificity of our models are reported and are compared with the most immediate baseline method, multi-layer perceptron (MLP).