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. 2009;16(1):147-162.
doi: 10.1080/10705510802561402.

Modeling Dynamic Functional Neuroimaging Data Using Structural Equation Modeling

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Free PMC article

Modeling Dynamic Functional Neuroimaging Data Using Structural Equation Modeling

Larry R Price et al. Struct Equ Modeling. .
Free PMC article

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.

Figures

FIGURE 1
FIGURE 1
Region of interest path model.

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