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：本研究提出了一种新颖的框架，用于对多被试时间序列数据进行基于动态因子模型的群体水平分析，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特性，识别并刻画两个预设群体之间的被试间相似性与差异性。此外，通过对每个被试采用不同的模型配置，该框架能够识别同一被试随时间变化的内部相似性与差异性。在方法论上，该框架结合了一种基于主角度的新颖秩选择算法与一种非迭代的整合分析框架。受同步成分分析的启发，该方法还能重构具有灵活协方差结构的可识别潜在因子序列。GRIDY模型的性能通过在多种场景下进行的仿真实验进行评估。本文还展示了一项应用，用于比较从自闭症谱系障碍组和对照组多个被试采集的静息态功能磁共振成像数据。