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