Abstract
As a progressive neurodegenerative disorder, the pathological changes of Alzheimer's disease (AD) might begin as much as two decades before the manifestation of clinical symptoms. Since the nature of the irreversible pathology of AD, early diagnosis provides a more tractable way for disease intervention and treatment. Therefore, numerous approaches have been developed for early diagnostic purposes. Although several important biomarkers have been established, most of the existing methods show limitations in describing the continuum of AD progression. However, understanding this continuous development is essential to understand the intrinsic progression mechanism of AD. In this work, we proposed a supervised deep tree model (SDTree) to integrate AD progression and individual prediction. The proposed SDTree method models the progression of AD as a tree embedded in a latent space using nonlinear reversed graph embedding. In this way, the continuum of AD progression is encoded into the locations on the tree structure. The learned tree structure can not only represent the continuum of AD but make predictions for new subjects. We evaluated our method on the classification task and achieved promising results on Alzheimer's Disease Neuroimaging Initiative dataset.