Comparar métodos

	Modelado Causal Dinámico	Modelado de Ecuaciones Estructurales
Campo	Neuroimagen	Estadística para la investigación
Familia	Process / pipeline	Process / pipeline
Año de origen	2003	1921
Autor original	Karl J. Friston	Sewall Wright
Tipo	Causal modeling pipeline for neuroimaging	Method
Fuente seminal	Friston, K. J., Harrison, L., & Penny, W. (2003). Dynamic causal modelling. NeuroImage, 19(4), 1273–1302.	Jöreskog, K. G., & Sörbom, D. (1973). LISREL: A general computer program for estimating a linear structural equation system. Research Bulletin 73-5. University of Stockholm.
Alias	DCM, Dynamic Causal Model	SEM, path analysis, latent variable modeling, causal modeling
Relacionados	2	3
Resumen	Dynamic Causal Modeling (DCM) is a Bayesian framework for specifying and inverting generative models of brain connectivity from neuroimaging data. Introduced by Karl Friston and colleagues in 2003, DCM treats brain regions as dynamical systems and estimates effective connectivity by fitting observed fMRI time series to a biophysically plausible model of neuronal interactions.	Structural equation modeling (SEM) is a comprehensive statistical framework combining path analysis (Sewall Wright, 1921) and confirmatory factor analysis to test complex causal models linking observed and latent variables. Formalized by Jöreskog (1973) with LISREL software, SEM enables simultaneous estimation of measurement relationships (how variables measure latent constructs) and structural relationships (how constructs influence outcomes), making it powerful for theory testing in psychology, epidemiology, organizational research, and health sciences where complex mediation, moderation, and latent processes require integrated analysis.