As an emerging research field of brain science, multimodal data fusion analysis has attracted broader attention in the study of complex brain diseases such as Parkinson’s disease (PD). However, current studies primarily lie with detecting the association among different modal data and reducing data attributes. The data mining method after fusion and the overall analysis framework are neglected. In this study, we propose a weighted random forest (WRF) model as the feature screening classifier. The interactions between genes and brain regions are detected as input multimodal fusion features by the correlation analysis method. We implement sample classification and optimal feature selection based on WRF, and construct a multimodal analysis framework for exploring the pathogenic factors of PD. The experimental results in Parkinson’s Progression Markers Initiative (PPMI) database show that WRF performs better compared with some advanced methods, and the brain regions and genes related to PD are detected. The fusion of multi-modal data can improve the classification of PD patients and detect the pathogenic factors more comprehensively, which provides a novel perspective for the diagnosis and research of PD. We also show the great potential of WRF to perform the multimodal data fusion analysis of other brain diseases.

作为脑科学的新兴研究领域，多模式数据融合分析在诸如帕金森氏病（PD）等复杂脑疾病的研究中引起了广泛的关注。但是，当前的研究主要在于检测不同模态数据之间的关联并减少数据属性。融合后的数据挖掘方法和整体分析框架被忽略。在这项研究中，我们提出了加权随机森林（WRF）模型作为特征筛选分类器。通过相关分析法将基因和大脑区域之间的相互作用检测为输入多峰融合特征。我们基于WRF实现样本分类和最佳特征选择，并构建多模式分析框架以探索PD的致病因素。帕金森氏病进展指标计划（PPMI）数据库中的实验结果表明，与某些先进方法相比，WRF的效果更好，并且可以检测到与PD相关的大脑区域和基因。多模式数据的融合可以改善PD患者的分类，更全面地检测致病因素，为PD的诊断和研究提供了新的思路。我们还展示了WRF在执行其他脑部疾病的多模式数据融合分析方面的巨大潜力。为PD的诊断和研究提供了新的视角。我们还展示了WRF在执行其他脑部疾病的多模式数据融合分析方面的巨大潜力。为PD的诊断和研究提供了新的视角。我们还展示了WRF在执行其他脑部疾病的多模式数据融合分析方面的巨大潜力。