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| 表征相似性分析× | 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≠ | 2008 | 2003 |
| 提出者≠ | Nikolaus Kriegeskorte | Karl J. Friston |
| 类型≠ | fMRI similarity structure comparison | Causal modeling pipeline for neuroimaging |
| 开创性文献≠ | Kriegeskorte, N., Mur, M., & Bandettini, P. A. (2008). Representational similarity analysis—connecting the branches of systems neuroscience. Frontiers in Systems Neuroscience, 2, 4. DOI ↗ | Friston, K. J., Harrison, L., & Penny, W. (2003). Dynamic causal modelling. NeuroImage, 19(4), 1273–1302. DOI ↗ |
| 别名≠ | RSA, representational geometry, similarity structure analysis | DCM, Dynamic Causal Model |
| 相关≠ | 3 | 2 |
| 摘要≠ | Representational Similarity Analysis (RSA) is a framework for comparing representational geometry across brain regions, computational models, and behavioral measures. Introduced by Kriegeskorte and colleagues in 2008, RSA measures how similarly a brain region represents different stimuli or concepts by examining pairwise similarity structure rather than absolute activity patterns. | 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. |
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