Linganisha mbinu

	Uchanganuzi wa Kina wa Sababu	Uchambuzi wa Mtandao wa Ubongo wa Grafu	Uchanganuzi wa Kimuundo wa Milongozo (SEM)
Nyanja	Upigaji Picha wa Ubongo	Upigaji Picha wa Ubongo	Takwimu za Utafiti
Familia	Process / pipeline	Process / pipeline	Process / pipeline
Mwaka wa asili	2003	2009	1921
Mwanzilishi	Karl J. Friston	Ed Bullmore	Sewall Wright
Aina	Causal modeling pipeline for neuroimaging	Brain network graph analysis pipeline	Method
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.	Jöreskog, K. G., & Sörbom, D. (1973). LISREL: A general computer program for estimating a linear structural equation system. Research Bulletin 73-5. University of Stockholm.
Majina mbadala	DCM, Dynamic Causal Model	graph theory, brain network analysis, network neuroscience	SEM, path analysis, latent variable modeling, causal modeling
Zinazohusiana	2	3	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.	Structural equation modeling (SEM) is a comprehensive statistical framework combining path analysis (Sewall Wright, 1921) and confirmatory factor analysis to test complex causal models linking observed and latent variables. Formalized by Jöreskog (1973) with LISREL software, SEM enables simultaneous estimation of measurement relationships (how variables measure latent constructs) and structural relationships (how constructs influence outcomes), making it powerful for theory testing in psychology, epidemiology, organizational research, and health sciences where complex mediation, moderation, and latent processes require integrated analysis.