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Multilevel Dynamic Generalized Structured Component Analysis for Brain Connectivity Analysis in Functional Neuroimaging Data

Published online by Cambridge University Press:  01 January 2025

Kwanghee Jung ,
Yoshio Takane ,
Heungsun Hwang  and
Todd S. Woodward
Kwanghee Jung*
Affiliation:
The University of Texas Health Science Center at Houston
Yoshio Takane
Affiliation:
University of Victoria
Heungsun Hwang
Affiliation:
McGill University
Todd S. Woodward
Affiliation:
University of British Columbia
*
Correspondence should be made to Kwanghee Jung, Department of Pediatrics, Children’s Learning Institute, The University of Texas Health Science Center at Houston, 7000 Fannin UCT 2373J, Houston, TX 77030, USA. Email: kwanghee.jung@uth.tmc.edu
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Abstract

We extend dynamic generalized structured component analysis (GSCA) to enhance its data-analytic capability in structural equation modeling of multi-subject time series data. Time series data of multiple subjects are typically hierarchically structured, where time points are nested within subjects who are in turn nested within a group. The proposed approach, named multilevel dynamic GSCA, accommodates the nested structure in time series data. Explicitly taking the nested structure into account, the proposed method allows investigating subject-wise variability of the loadings and path coefficients by looking at the variance estimates of the corresponding random effects, as well as fixed loadings between observed and latent variables and fixed path coefficients between latent variables. We demonstrate the effectiveness of the proposed approach by applying the method to the multi-subject functional neuroimaging data for brain connectivity analysis, where time series data-level measurements are nested within subjects.

Keywords

generalized structured component analysismultilevel analysisstructural equation modelingalternating least squares (ALS) algorithmbrain connectivity analysistime series datafunctional neuroimagingmulti-subject data
Type
Article
Information
Psychometrika , Volume 81 , Issue 2 , June 2016 , pp. 565 - 581
Copyright
Copyright © 2015 The Psychometric Society

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Footnotes

The online version of the original article can be found under doi:10.1007/s11336-015-9440-6.

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