关键词: hypergraph neural network, similarity network fusion, Parkinson's disease subtypes, multi-modal data

Abstract: Parkinson's disease (PD) exhibits significant phenotypic heterogeneity, which complicates clinical management and underscores the need for precise subtyping. Existing subtyping approaches often rely on a single modality, such as clinical assessments, failing to capture the complex, multi-faceted nature of the disease. This paper proposes a novel computational framework that integrates multi-modal data, specifically preprocessed functional MRI, DNA methylation, and clinical behavioral assessments, for PD subtyping. The methodology involves constructing individual hypergraphs for each modality using $K$-nearest neighbors (KNN), followed by the integration of these hypergraphs into a unified, multi-modal hypergraph using similarity network fusion (SNF). This consolidated hypergraph is then processed via a hypergraph neural network (HGNN) utilizing hyperedge convolution to cluster patients into distinct subtypes. Our experimental results demonstrate that this approach effectively identifies PD subtypes with significant clinical and biological relevance. We provide a comprehensive analysis of the model's performance and further validate the reliability of the identified subtypes through post-hoc statistical tests. This study highlights the potential of graph-based machine learning in disentangling disease heterogeneity, paving the way for personalized therapeutic strategies and improved patient outcomes.

Key words: hypergraph neural network, similarity network fusion, Parkinson's disease subtypes, multi-modal data