This work introduces a novel framework for dynamic factor model-based data integration of multiple subjects, called GRoup Integrative DYnamic factor models (GRIDY). The framework facilitates the determination of inter-subject differences between two pre-labeled groups by considering a combination of group spatial information and individual temporal dependence. Furthermore, it enables the identification of intra-subject differences over time by employing different model configurations for each subject. Methodologically, the framework combines a novel principal angle-based rank selection algorithm and a non-iterative integrative analysis framework. Inspired by simultaneous component analysis, this approach also reconstructs identifiable latent factor series with flexible covariance structures. The performance of the framework is evaluated through simulations conducted under various scenarios and the analysis of resting-state functional MRI data collected from multiple subjects in both the Autism Spectrum Disorder group and the control group.

翻译：本文提出了一种新颖的基于动态因子模型的多受试者数据整合框架，称为群组整合性动态因子模型（GRIDY）。该框架通过结合群组空间信息与个体时间依赖性，能够确定两个预标记群组间的个体间差异。此外，通过为每个受试者采用不同的模型配置，该框架还能识别个体内随时间变化的差异。在方法论上，该框架结合了基于主角度的新型秩选择算法与非迭代式整合分析框架。受同时成分分析的启发，该方法还重构了具有灵活协方差结构的可识别潜在因子序列。通过对多种情景下的模拟实验以及对自闭症谱系障碍组与对照组多受试者静息态功能磁共振成像数据的分析，评估了该框架的性能。