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

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

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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.

Original languageEnglish
Pages (from-to)565-581
Number of pages17
JournalPsychometrika
Volume81
Issue number2
DOIs
StatePublished - Jun 1 2016

Keywords

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

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