  Alzheimer's disease is characterized by alterations of the brain's structural
and functional connectivity during its progressive degenerative processes.
Existing auxiliary diagnostic methods have accomplished the classification
task, but few of them can accurately evaluate the changing characteristics of
brain connectivity. In this work, a prior guided adversarial representation
learning and hypergraph perceptual network (PGARL-HPN) is proposed to predict
abnormal brain connections using triple-modality medical images. Concretely, a
prior distribution from the anatomical knowledge is estimated to guide
multimodal representation learning using an adversarial strategy. Also, the
pairwise collaborative discriminator structure is further utilized to narrow
the difference of representation distribution. Moreover, the hypergraph
perceptual network is developed to effectively fuse the learned representations
while establishing high-order relations within and between multimodal images.
Experimental results demonstrate that the proposed model outperforms other
related methods in analyzing and predicting Alzheimer's disease progression.
More importantly, the identified abnormal connections are partly consistent
with the previous neuroscience discoveries. The proposed model can evaluate
characteristics of abnormal brain connections at different stages of
Alzheimer's disease, which is helpful for cognitive disease study and early
treatment.
