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.

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