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This work introduces a novel framework for dynamic factor model-based dataintegration of multiple subjects time series data, called GRoup IntegrativeDYnamic factor (GRIDY) models. The framework identifies and characterizesinter-subject differences between two pre-labeled groups by considering acombination of group spatial information and individual temporal dependence.Furthermore, it enables the identification of intra-subject differences overtime by employing different model configurations for each subject.Methodologically, the framework combines a novel principal angle-based rankselection algorithm and a non-iterative integrative analysis framework.Inspired by simultaneous component analysis, this approach also reconstructsidentifiable latent factor series with flexible covariance structures. Theperformance of the GRIDY models is evaluated through simulations conductedunder various scenarios. An application is also presented to compareresting-state functional MRI data collected from multiple subjects in theAutism Spectrum Disorder and control groups.

Group integrative dynamic factor models with application to multiple subject brain connectivity