Modeling Dynamic Functional Neuroimaging Data Using Structural Equation Modeling

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,...

ver descrição completa

Na minha lista:
Detalhes bibliográficos
Main Authors: Price, Larry R., Laird, Angela R., Fox, Peter T., Ingham, Roger J.
Formato: Artigo
Idioma:Inglês
Publicado em: 2009
Assuntos:
Acesso em linha:https://ncbi.nlm.nih.gov/pmc/articles/PMC2874985/
https://ncbi.nlm.nih.gov/pubmed/20502535
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1080/10705510802561402
Tags: Adicionar Tag
Sem tags, seja o primeiro a adicionar uma tag!
Descrição
Resumo: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.