Comparer des méthodes

	Modélisation Causale Dynamique	Analyse des réseaux cérébraux par graphes
Domaine	Neuro-imagerie	Neuro-imagerie
Famille	Process / pipeline	Process / pipeline
Année d'origine	2003	2009
Auteur d'origine	Karl J. Friston	Ed Bullmore
Type	Causal modeling pipeline for neuroimaging	Brain network graph analysis pipeline
Source fondatrice	Friston, K. J., Harrison, L., & Penny, W. (2003). Dynamic causal modelling. NeuroImage, 19(4), 1273–1302.	Bullmore, E., & Sporns, O. (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10(3), 186–198.
Alias	DCM, Dynamic Causal Model	graph theory, brain network analysis, network neuroscience
Apparentées	2	3
Résumé	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.	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.