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Alzheimer's disease is characterized by alterations of the brain's structuraland functional connectivity during its progressive degenerative processes.Existing auxiliary diagnostic methods have accomplished the classificationtask, but few of them can accurately evaluate the changing characteristics ofbrain connectivity. In this work, a prior guided adversarial representationlearning and hypergraph perceptual network (PGARL-HPN) is proposed to predictabnormal brain connections using triple-modality medical images. Concretely, aprior distribution from the anatomical knowledge is estimated to guidemultimodal representation learning using an adversarial strategy. Also, thepairwise collaborative discriminator structure is further utilized to narrowthe difference of representation distribution. Moreover, the hypergraphperceptual network is developed to effectively fuse the learned representationswhile establishing high-order relations within and between multimodal images.Experimental results demonstrate that the proposed model outperforms otherrelated methods in analyzing and predicting Alzheimer's disease progression.More importantly, the identified abnormal connections are partly consistentwith the previous neuroscience discoveries. The proposed model can evaluatecharacteristics of abnormal brain connections at different stages ofAlzheimer's disease, which is helpful for cognitive disease study and earlytreatment.

A Prior Guided Adversarial Representation Learning and Hypergraph Perceptual Network for Predicting Abnormal Connections of Alzheimer's Disease