This work introduces a novel framework for dynamic factor model-based group-level analysis of multiple subjects time series data, called GRoup Integrative DYnamic factor (GRIDY) models. The framework identifies and characterizes inter-subject similarities and differences between two pre-determined groups by considering a combination of group spatial information and individual temporal dynamics. Furthermore, it enables the identification of intra-subject similarities and 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 GRIDY models is evaluated through simulations conducted under various scenarios. An application is also presented to compare resting-state functional MRI data collected from multiple subjects in autism spectrum disorder and control groups.

翻译：本文提出了一种基于动态因子模型的群体级多受试者时间序列数据分析新框架，称为群体整合动态因子（GRIDY）模型。该框架通过结合群体空间信息与个体时间动态，识别并表征两个预定组之间的受试者间相似性与差异性。此外，通过为每个受试者采用不同的模型配置，框架还能识别受试者内部随时间变化的相似性与差异性。在方法论上，该框架融合了基于主成分角的新型秩选择算法与非迭代式整合分析框架。受同时成分分析启发，该方法可重构具有灵活协方差结构的可识别潜在因子序列。通过多种场景下的模拟实验评估了GRIDY模型的性能，并在自闭症谱系障碍组与对照组的多受试者静息态功能磁共振成像数据比较中展示了实际应用效果。