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Parkinson's Disease (PD) is one of the most prevalent neurodegenerativediseases that affects tens of millions of Americans. PD is highly progressiveand heterogeneous. Quite a few studies have been conducted in recent years onpredictive or disease progression modeling of PD using clinical and biomarkersdata. Neuroimaging, as another important information source forneurodegenerative disease, has also arisen considerable interests from the PDcommunity. In this paper, we propose a deep learning method based on GraphConvolutional Networks (GCN) for fusing multiple modalities of brain images inrelationship prediction which is useful for distinguishing PD cases fromcontrols. On Parkinson's Progression Markers Initiative (PPMI) cohort, ourapproach achieved $0.9537\pm 0.0587$ AUC, compared with $0.6443\pm 0.0223$ AUCachieved by traditional approaches such as PCA.

Multi-View Graph Convolutional Network and Its Applications on Neuroimage Analysis for Parkinson's Disease