Modeling Dynamic Functional Neuroimaging Data Using Structural Equation Modeling |
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| Authors: | Larry R Price Angela R Laird Peter T Fox Roger J Ingham |
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| Institution: | 1. Texas State University , San Marcos;2. University of Texas Health Science Center at San Antonio, Research Imaging Center;3. Department of Speech and Hearing Sciences , University of California , Santa Barbara |
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| 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. |
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