Learning Latent Structure over Deep Fusion Model of Mild Cognitive Impairment

Many computational models have been developed to understand Alzheimer's disease (AD) and its precursor - mild cognitive impairment (MCI) using non-invasive neural imaging techniques, i.e. magnetic resonance imaging (MRI) based imaging modalities. Most existing methods focused on identification of imaging biomarkers, classification/prediction of different clinical stages, regression of cognitive scores, or their combination as multi-task learning. Given the widely existed individual variability, however, it is still challenging to consider different learning tasks simultaneously even they share a similar goal: exploring the intrinsic alteration patterns in AD/MCI patients. Moreover, AD is a progressive neurodegenerative disorder with a long preclinical period. Besides conducting simple classification, brain changes should be considered within the entire AD/MCI progression process. Here, we introduced a novel deep fusion model for MCI using functional MRI data. We integrated autoencoder, multi-class classification and structure learning into a single deep model. During the modeling, different clinical groups including normal controls, early MCI and late MCI are considered simultaneously. With the learned discriminative representations, we not only can achieve a satisfied classification performance, but also construct a tree structure of MCI progressions.