Dynamic GSCA (Generalized Structured Component Analysis) with Applications to the Analysis of Effective Connectivity in Functional Neuroimaging Data

Kwanghee Jung, Yoshio Takane, Heungsun Hwang, Todd S. Woodward

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

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.

Original languageEnglish
Pages (from-to)827-848
Number of pages22
JournalPsychometrika
Volume77
Issue number4
DOIs
StatePublished - Oct 2012

Keywords

  • a modified moving block bootstrap method
  • alternating least squares (ALS) algorithm
  • effective connectivity
  • functional neuroimaging
  • generalized structured component analysis (GSCA)
  • longitudinal and time series data
  • structural equation modeling (SEM)

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