Linganisha mbinu

	Uchanganuzi wa Kina wa Sababu	Uchambuzi wa Mtandao wa Ubongo wa Grafu
Nyanja	Upigaji Picha wa Ubongo	Upigaji Picha wa Ubongo
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	2003	2009
Mwanzilishi	Karl J. Friston	Ed Bullmore
Aina	Causal modeling pipeline for neuroimaging	Brain network graph analysis pipeline
Chanzo asilia	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.
Majina mbadala	DCM, Dynamic Causal Model	graph theory, brain network analysis, network neuroscience
Zinazohusiana	2	3
Muhtasari	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.