Modeling dynamic functional neuroimaging data using structural equation modeling

Larry R. Price, Angela R. Laird, Peter T. Fox, Roger J. Ingham

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


The aims of this study were to present a method for developing a path analytic network model using data acquired from positron emission tomography. Regions of interest within the human brain were identified through quantitative activation likelihood estimation meta-analysis. Using this information, a true or population path model was then developed using Bayesian structural equation modeling. To evaluate the impact of sample size on parameter estimation bias, proportion of parameter replication coverage, and statistical power, a 2 group (clinical/control) 6 (sample size: N = 10, N = 15, N = 20, N = 25, N = 50, N = 100) Markov chain Monte Carlo study was conducted. Results indicate that using a sample size of less than N = 15 per group will produce parameter estimates exhibiting bias greater than 5% and statistical power below.80.

Original languageEnglish (US)
Pages (from-to)147-162
Number of pages16
JournalStructural Equation Modeling
Issue number1
StatePublished - Jan 2009

ASJC Scopus subject areas

  • General Decision Sciences
  • General Economics, Econometrics and Finance
  • Sociology and Political Science
  • Modeling and Simulation


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