विधियों की तुलना करें

	Graph Brain Network Analysis	डायनामिक कारणात्मक मॉडलिंग
क्षेत्र	तंत्रिका प्रतिबिंबन	तंत्रिका प्रतिबिंबन
परिवार	Process / pipeline	Process / pipeline
उद्भव वर्ष	2009	2003
प्रवर्तक	Ed Bullmore	Karl J. Friston
प्रकार	Brain network graph analysis pipeline	Causal modeling pipeline for neuroimaging
मौलिक स्रोत	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.
उपनाम	graph theory, brain network analysis, network neuroscience	DCM, Dynamic Causal Model
संबंधित	3	2
सारांश	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.