title: Modeling Dynamic Functional Neuroimaging Data Using Structural Equation Modeling.
abstract: 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.
authors.affiliation: Texas State University-San Marcos.
authors.forename: Larry R
authors.initials: LR
authors.lastname: Price
authors.forename: Angela R
authors.initials: AR
authors.lastname: Laird
authors.forename: Peter T
authors.initials: PT
authors.lastname: Fox
authors.forename: Roger J
authors.initials: RJ
authors.lastname: Ingham