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| 动态因果建模× | 事件相关电位分析× | |
|---|---|---|
| 领域 | 神经影像 | 神经影像 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 2003 | 1969 |
| 提出者≠ | Karl J. Friston | George Sutherland |
| 类型≠ | Causal modeling pipeline for neuroimaging | Time-locked EEG analysis pipeline |
| 开创性文献≠ | Friston, K. J., Harrison, L., & Penny, W. (2003). Dynamic causal modelling. NeuroImage, 19(4), 1273–1302. DOI ↗ | Luck, S. J. (2005). An Introduction to the Event-Related Potential Technique. MIT Press. link ↗ |
| 别名≠ | DCM, Dynamic Causal Model | ERP, evoked potential, averaged EEG |
| 相关≠ | 2 | 3 |
| 摘要≠ | 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. | Event-Related Potential (ERP) analysis is a method for extracting stereotyped brain electrical responses time-locked to stimulus presentation or behavioral events from EEG recordings. Formalized in the cognitive neuroscience literature by researchers including Sutherland and Picton, ERP analysis enables millisecond-level temporal resolution of neural processing and has become foundational for studying perception, attention, memory, and decision-making. |
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