So sánh phương pháp

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