, Volume 81, Issue 2, pp 565–581 | Cite as

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

  • Kwanghee JungEmail author
  • Yoshio Takane
  • Heungsun Hwang
  • Todd S. Woodward


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.


generalized structured component analysis multilevel analysis structural equation modeling alternating least squares (ALS) algorithm brain connectivity analysis time series data functional neuroimaging multi-subject data 


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Copyright information

© The Psychometric Society 2015

Authors and Affiliations

  • Kwanghee Jung
    • 1
    Email author
  • Yoshio Takane
    • 2
  • Heungsun Hwang
    • 3
  • Todd S. Woodward
    • 4
  1. 1.Department of Pediatrics, Children’s Learning InstituteThe University of Texas Health Science Center at HoustonHoustonUSA
  2. 2.University of VictoriaVictoriaCanada
  3. 3.McGill UniversityMontrealCanada
  4. 4.University of British ColumbiaVancouverCanada

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