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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

Information

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.

References

Cairo, T.A., Woodward, T.S., Ngan, E.T. (2006). Decreased encoding efficiency in schizophrenia. Biological Psychiatry, 59, 740–746CrossRefGoogle ScholarPubMed
Chen, G., Glen, D.R., Saad, Z.S., Hamilton, J.P., Thomason, M.E., Gotlib, I.H., Cox, R.W. (2011). Vector autoregression, structural equation modeling, and their synthesis in neuroimaging data analysis. Computers in Biology and Medicine, 41, 1142–1155CrossRefGoogle ScholarPubMed
deLeeuw, J., Young, F.W., Takane, Y. (1976). Additive structure in qualitative data: An alternating least squares method with optimal scaling features. Psychometrika, 41, 471–503CrossRefGoogle Scholar
Efron, B. (1982). The jackknife, the bootstrap and other resampling plans, Philadelphia: SIAMCrossRefGoogle Scholar
Friston, K.J. (1994). Functional and effective connectivity in neuroimaging: A synthesis. Human Brain Mapping, 2, 56–78CrossRefGoogle Scholar
Gates, K.M., Molenaar, P.C., Hillary, F.G., Slobounov, S. (2011). Extended unified SEM approach for modeling event-related fMRI data. NeuroImage, 54, 1151–1158CrossRefGoogle ScholarPubMed
Greve, D.N., Brown, G.G., Mueller, B.A., Glover, G., Liu, T.T. (2013). A survey of the sources of noise in fMRI. Psychometrika, 78, 396–416CrossRefGoogle ScholarPubMed
Hwang, H., Takane, Y. (2004). Generalized structured component analysis. Psychometrika, 69, 81–99CrossRefGoogle Scholar
Hwang, H., Takane, Y. (2014). Generalized structured component analysis: A component-based approach to structural equation modeling, Boca Raton, FL: Chapman & Hall/CRC Press. (forthcoming)CrossRefGoogle Scholar
Hwang, H., Ho, R.M., Lee, J. (2010). Generalized structured component analysis with latent interactions. Psychometrika, 75, 228–242CrossRefGoogle Scholar
Jung, K., Takane, Y., Hwang, H., Woodward, T.S. (2012). Dynamic GSCA (Generalized Structured Component Analysis) with applications to the analysis of effective connectivity in functional neuroimaging data. Psychometrika, 77, 827–848CrossRefGoogle Scholar
Kim, J., Zhu, W., Chang, L., Bentler, P.M., Ernst, T. (2007). Unified structural equation modeling approach for the analysis of multisubject, multivariate functional MRI data. Human Brain Mapping, 28, 85–93CrossRefGoogle ScholarPubMed
Snijders, T., Bosker, R. (1999). Multilevel analysis: An introduction to basic and advanced multilevel modeling, London: Sage PublicationGoogle Scholar
Takane, Y. (2013). Constrained principal component analysis and related techniques, Boca Raton, FL: Chapman and Hall/CRC PressGoogle Scholar
Takane, Y., Hunter, M.A. (2001). Constrained principal component analysis: A comprehensive theory. Applicable Algebra in Engineering, Communication and Computing, 12, 391–419CrossRefGoogle Scholar
Takane, Y., Shibayama, T. (1991). Principal component analysis with external information on both subjects and variables. Psychometrika, 56, 97–120CrossRefGoogle Scholar
Van Den Heuvel, M.P., Hulshoff Pol, H.E. (2010). Exploring the brain network: A review on resting-state fMRI functional connectivity. European Neuropsychopharmacology, 20, 519–534CrossRefGoogle ScholarPubMed
Woodward, T.S., Cairo, T.A., Ruff, C.C., Takane, Y., Hunter, M.A., Ngan, E.T.C. (2006). Functional connectivity reveals load dependent neural systems underlying encoding and maintenance in verbal working memory. Neuroscience, 139, 317–325CrossRefGoogle ScholarPubMed