Comparer des méthodes

	Analyse des réseaux cérébraux par graphes	Modélisation par équations structurelles
Domaine	Neuro-imagerie	Statistiques de recherche
Famille	Process / pipeline	Process / pipeline
Année d'origine	2009	1921
Auteur d'origine	Ed Bullmore	Sewall Wright
Type	Brain network graph analysis pipeline	Method
Source fondatrice	Bullmore, E., & Sporns, O. (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10(3), 186–198.	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	graph theory, brain network analysis, network neuroscience	SEM, path analysis, latent variable modeling, causal modeling
Apparentées	3	3
Résumé	Graph Theoretical Brain Network Analysis applies network science to understand brain organization, treating the brain as a complex network of interconnected nodes (regions) and edges (connections). Formalized by Bullmore and Sporns in 2009, graph analysis reveals fundamental organizational principles—modularity, efficiency, resilience—that characterize healthy and diseased brains.	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.