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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| Published in | Biometrical journal Vol. 66; no. 8; pp. e202300370 - n/a |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Germany
Wiley - VCH Verlag GmbH & Co. KGaA
01.12.2024
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0323-3847 1521-4036 1521-4036 |
| DOI | 10.1002/bimj.202300370 |
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| Abstract | 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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| AbstractList | 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. 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.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. 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. 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. |
| Author | Kim, Younghoon Pipiras, Vladas Fisher, Zachary F. |
| Author_xml | – sequence: 1 givenname: Younghoon orcidid: 0009-0007-0117-5530 surname: Kim fullname: Kim, Younghoon email: yk748@cornell.edu organization: Cornell University – sequence: 2 givenname: Zachary F. surname: Fisher fullname: Fisher, Zachary F. organization: The Pennsylvania State University – sequence: 3 givenname: Vladas surname: Pipiras fullname: Pipiras, Vladas organization: University of North Carolina at Chapel Hill |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39470131$$D View this record in MEDLINE/PubMed |
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This work introduces a novel framework for dynamic factor model‐based group‐level analysis of multiple subjects time‐series data, called GRoup... This work introduces a novel framework for dynamic factor model‐based group‐level analysis of multiple subjects time‐series data, called GRoup Integrative... This work introduces a novel framework for dynamic factor model-based group-level analysis of multiple subjects time-series data, called GRoup Integrative... |
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| SubjectTerms | 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 |
| Title | Group Integrative Dynamic Factor Models With Application to Multiple Subject Brain Connectivity |
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