قارن الطرق
راجع الطرق التي اخترتها جنبًا إلى جنب؛ الصفوف المختلفة مميَّزة.
| النمذجة السببية الديناميكية× | تحليل شبكات الدماغ البيانية× | |
|---|---|---|
| المجال | التصوير العصبي | التصوير العصبي |
| العائلة | Process / pipeline | Process / pipeline |
| سنة النشأة≠ | 2003 | 2009 |
| صاحب الطريقة≠ | Karl J. Friston | Ed Bullmore |
| النوع≠ | Causal modeling pipeline for neuroimaging | Brain network graph analysis pipeline |
| المصدر التأسيسي≠ | Friston, K. J., Harrison, L., & Penny, W. (2003). Dynamic causal modelling. NeuroImage, 19(4), 1273–1302. DOI ↗ | Bullmore, E., & Sporns, O. (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10(3), 186–198. DOI ↗ |
| الأسماء البديلة≠ | DCM, Dynamic Causal Model | graph theory, brain network analysis, network neuroscience |
| ذات صلة≠ | 2 | 3 |
| الملخص≠ | 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. |
| ScholarGateمجموعة البيانات ↗ |
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