方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 图脑网络分析× | 动态因果建模× | |
|---|---|---|
| 领域 | 神经影像 | 神经影像 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 2009 | 2003 |
| 提出者≠ | Ed Bullmore | Karl J. Friston |
| 类型≠ | Brain network graph analysis pipeline | Causal modeling pipeline for neuroimaging |
| 开创性文献≠ | Bullmore, E., & Sporns, O. (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10(3), 186–198. DOI ↗ | Friston, K. J., Harrison, L., & Penny, W. (2003). Dynamic causal modelling. NeuroImage, 19(4), 1273–1302. DOI ↗ |
| 别名≠ | graph theory, brain network analysis, network neuroscience | DCM, Dynamic Causal Model |
| 相关≠ | 3 | 2 |
| 摘要≠ | 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. |
| ScholarGate数据集 ↗ |
|
|