Group Integrative Dynamic Factor Models With Application to Multiple Subject Brain Connectivity

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 intersubject similarities and differences between two predeter...

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Bibliographic Details
Published inBiometrical journal Vol. 66; no. 8; pp. e202300370 - n/a
Main Authors Kim, Younghoon, Fisher, Zachary F., Pipiras, Vladas
Format Journal Article
LanguageEnglish
Published Germany Wiley - VCH Verlag GmbH & Co. KGaA 01.12.2024
Subjects
Algorithms
Autism
Autism Spectrum Disorder - diagnostic imaging
Autism Spectrum Disorder - physiopathology
Biometry - methods
Brain - diagnostic imaging
dynamic factor model
fMRI
Functional magnetic resonance imaging
Group dynamics
group‐level analysis
high‐dimensional time series
Humans
Magnetic Resonance Imaging
Models, Statistical
multiway analysis
Neural networks
principal angles
Similarity
Spatial data
group‐level analysis
fMRI
high‐dimensional time series
dynamic factor model
multiway analysis
principal angles
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Summary: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 intersubject similarities and differences between two predetermined groups by considering a combination of group spatial information and individual temporal dynamics. Furthermore, it enables the identification of intrasubject 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 noniterative 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.
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ISSN:0323-3847
1521-4036
1521-4036
DOI:10.1002/bimj.202300370