Using correlation analysis to study the potential connection between brain genetics and imaging has become an effective method to understand neurodegenerative diseases. Sparse canonical correlation analysis (SCCA) makes it possible to study high-dimensional genetic information. The traditional SCCA methods can only process single-modal genetic and image data, which to some extent weaken the close connection of the brain’s biological network. In some recently proposed multimodal SCCA methods, due to the limitations of penalty items, the pre-processed data needs to be further filtered to make the dimensions uniform, which may destroy the potential association of data in the same modal. In this research, in order to combine data between different modalities and to ensure that the chain relationship or graph network relationship within the same modality will not be destroyed, the original generalized fused lasso penalty was replaced with the fused pairwise group lasso (FGL) and the graph-guided pairwise group lasso (GGL) based on the method of joint sparse canonical correlation analysis (JSCCA). We used prior knowledge to construct a supervised bivariate learning model and use linear regression to select quantitative traits (QTs) of images that are strongly correlated with the Mini-mental State Examination (MMSE) scores. Compared with FGL-SCCA, the model we constructed obtained a higher gene–ROI correlation coefficient and identified more significant biomarkers, providing a theoretical basis for further understanding the complex pathology of neurodegenerative diseases.

使用相关分析来研究脑遗传学与成像之间的潜在联系已成为了解神经退行性疾病的有效方法。稀疏典型相关分析 (SCCA) 使研究高维遗传信息成为可能。传统的SCCA方法只能处理单模态的遗传和图像数据，这在一定程度上削弱了大脑生物网络的紧密联系。在最近提出的一些多模态SCCA方法中，由于惩罚项的限制，需要对预处理数据进行进一步过滤，使维度统一，这可能会破坏同一模态数据的潜在关联。在这项研究中，为了结合不同模态之间的数据，并确保同一模态内的链关系或图网络关系不会被破坏，将原来的广义融合套索惩罚替换为融合成对组套索（FGL）和图引导基于联合稀疏典型相关分析（JSCCA）方法的成对组套索（GGL）。我们使用先验知识来构建有监督的双变量学习模型，并使用线性回归来选择与简易精神状态检查 (MMSE) 分数密切相关的图像的数量特征 (QT)。与 FGL-SCCA 相比，我们构建的模型获得了更高的基因-ROI 相关系数，并识别了更显着的生物标志物，