So sánh phương pháp

	Phân tích Mạng Não Bộ Dựa trên Đồ thị	Dynamic Causal Modeling
Lĩnh vực	Chẩn đoán hình ảnh thần kinh	Chẩn đoán hình ảnh thần kinh
Họ	Process / pipeline	Process / pipeline
Năm ra đời	2009	2003
Người khởi xướng	Ed Bullmore	Karl J. Friston
Loại	Brain network graph analysis pipeline	Causal modeling pipeline for neuroimaging
Công trình gốc	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.
Tên gọi khác	graph theory, brain network analysis, network neuroscience	DCM, Dynamic Causal Model
Liên quan	3	2
Tóm tắt	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.