MMGK: Multimodality Multiview Graph Representations and Knowledge Embedding for Mild Cognitive Impairment Diagnosis

The diagnosis of mild cognitive impairment (MCI), which is an early stage of Alzheimer's disease (AD), has great clinical significance. Medical imaging and gene sequencing technologies have provided sufficient multimodality data for MCI diagnostic studies. However, how to effectively extract the rich representations from multimodality data remains a challenging task. To address this challenging task, we propose a new multimodality multiview graph representations and knowledge embedding (MMGK) framework to diagnose MCI. First, to obtain rich information from multimodality data, we extract multiview feature representations from magnetic resonance imaging (MRI) and genetic data. Afterward, considering the correlations between subjects, all subjects are constructed into a graph based on the different single-view feature representations, respectively. To further enrich the correlations between subjects, demographic data are utilized through knowledge embedding. Finally, to perform MCI diagnosis on multiview graphs, graph convolutional networks (GCNs) are utilized. In addition, to further improve the performance of MCI diagnosis, a two-step ensemble learning method is proposed. The proposed framework is evaluated on 188 subjects from the AD Neuroimaging Initiative (ADNI). Experimental results show that our proposed framework achieves good performance with accuracy reaching 0.888, and outperforms some state-of-the-art (SOTA) methods. In addition, the proposed framework is applied to Parkinson's disease (PD) diagnosis and achieves 0.856 accuracy. Overall, our proposed method has potential for clinical application in MCI diagnosis and other diseases via integrating MRI, genetic data, and demographic data.