Linganisha mbinu

	Uchambuzi wa Mtandao wa Ubongo wa Grafu	Uchanganuzi wa Kina wa Sababu
Nyanja	Upigaji Picha wa Ubongo	Upigaji Picha wa Ubongo
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	2009	2003
Mwanzilishi	Ed Bullmore	Karl J. Friston
Aina	Brain network graph analysis pipeline	Causal modeling pipeline for neuroimaging
Chanzo asilia	Bullmore, E., & Sporns, O. (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10(3), 186–198.	Friston, K. J., Harrison, L., & Penny, W. (2003). Dynamic causal modelling. NeuroImage, 19(4), 1273–1302.
Majina mbadala	graph theory, brain network analysis, network neuroscience	DCM, Dynamic Causal Model
Zinazohusiana	3	2
Muhtasari	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.	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.