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

TL;DR

This paper presents GRIDY, a novel dynamic factor model framework for analyzing group-level and intra-subject brain connectivity in multi-subject fMRI data, capturing inter- and intra-group differences.

Contribution

Introduces GRIDY, a new integrative dynamic factor model combining principal angle-based rank selection and flexible covariance structures for multi-subject brain data analysis.

Findings

Effective in identifying inter-group differences in autism and control groups

Accurately captures intra-subject temporal dynamics

Demonstrates robustness through simulation studies

Abstract

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.