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Dynamic GSCA (Generalized Structured Component Analysis) with Applications to the Analysis of Effective Connectivity 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:
McGill UniversityUniversity of British Columbia
Yoshio Takane
Affiliation:
McGill University
Heungsun Hwang
Affiliation:
McGill University
Todd S. Woodward
Affiliation:
University of British Columbia
*
Requests for reprints should be sent to Kwanghee Jung, BC Mental Health and Addictions Research Institute, Department of Psychiatry, University of British Columbia, Child and Family Research Institute Building, Room A3-112, 938 West 28th Avenue, Vancouver, BC, Canada V5Z 4H4. E-mail: kwanghee.jung@ubc.ca
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Abstract

We propose a new method of structural equation modeling (SEM) for longitudinal and time series data, named Dynamic GSCA (Generalized Structured Component Analysis). The proposed method extends the original GSCA by incorporating a multivariate autoregressive model to account for the dynamic nature of data taken over time. Dynamic GSCA also incorporates direct and modulating effects of input variables on specific latent variables and on connections between latent variables, respectively. An alternating least square (ALS) algorithm is developed for parameter estimation. An improved bootstrap method called a modified moving block bootstrap method is used to assess reliability of parameter estimates, which deals with time dependence between consecutive observations effectively. We analyze synthetic and real data to illustrate the feasibility of the proposed method.

Keywords

generalized structured component analysis (GSCA)structural equation modeling (SEM)longitudinal and time series dataalternating least squares (ALS) algorithma modified moving block bootstrap methodfunctional neuroimagingeffective connectivity

Information

Type
Original Paper
Information
Psychometrika , Volume 77 , Issue 4 , October 2012, pp. 827 - 848
Copyright
Copyright © 2012 The Psychometric Society

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