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.
翻译:Parkinson病(PD)是影响数千万美国人的最流行的神经退化疾病之一。PD是高度进步和多样化的。近年来,对利用临床和生物标志数据对PD进行预测或疾病进展模型模型化进行了相当多的研究。神经成像作为神经退化性疾病的另一个重要信息来源,也引起了PD界的极大兴趣。在本文中,我们提议了一种深思熟虑的方法,以图变网络为基础,在关系预测中使用多种模式的脑图象,这有助于区分PD病例和控制情况。关于Parkinson进步标记倡议(PPMI)的组群,我们的方法达到了0.937\pm 0.0587澳元,相比之下,通过五氯苯甲醚等传统方法实现了0.443 pm 0.022美元。