Modeling Dynamic Functional Neuroimaging Data Using Structural Equation Modeling

Autor/inn/en	Price, Larry R.; Laird, Angela R.; Fox, Peter T.; Ingham, Roger J.
Titel	Modeling Dynamic Functional Neuroimaging Data Using Structural Equation Modeling
Quelle	In: Structural Equation Modeling: A Multidisciplinary Journal, 16 (2009) 1, S.147-162 (16 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1070-5511
Schlagwörter	Sample Size; Monte Carlo Methods; Structural Equation Models; Markov Processes; Brain; Computation; Diagnostic Tests; Meta Analysis; Path Analysis + Suchen Sie Ihr Suchwort? Monte-Carlo-Methode; Markowscher Prozess; Gehirn; Diagnostic test; Diagnostischer Test; Meta-analysis; Metaanalyse; Pfadanalyse
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) x 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 0.80. (Contains 5 tables and 1 figure.) (As Provided).
Anmerkungen	Psychology Press. Available from: Taylor & Francis, Ltd. 325 Chestnut Street Suite 800, Philadelphia, PA 19106. Tel: 800-354-1420; Fax: 215-625-2940; Web site: http://www.tandf.co.uk/journals
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10