השוואת שיטות

	מודלים סיבתיים דינמיים	ניתוח רשתות מוחיות מבוסס גרפים
תחום	הדמיה עצבית	הדמיה עצבית
משפחה	Process / pipeline	Process / pipeline
שנת המקור	2003	2009
הוגה השיטה	Karl J. Friston	Ed Bullmore
סוג	Causal modeling pipeline for neuroimaging	Brain network graph analysis pipeline
מקור מכונן	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.
כינויים	DCM, Dynamic Causal Model	graph theory, brain network analysis, network neuroscience
קשורות	2	3
תקציר	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.