The Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London, WC1N 3AR, UK,
Cogn Neurodyn. 2008 Jun;2(2):121-36. doi: 10.1007/s11571-008-9038-0. Epub 2008 Apr 23.
Dynamic Causal Modelling (DCM) is an approach first introduced for the analysis of functional magnetic resonance imaging (fMRI) to quantify effective connectivity between brain areas. Recently, this framework has been extended and established in the magneto/encephalography (M/EEG) domain. DCM for M/EEG entails the inversion a full spatiotemporal model of evoked responses, over multiple conditions. This model rests on a biophysical and neurobiological generative model for electrophysiological data. A generative model is a prescription of how data are generated. The inversion of a DCM provides conditional densities on the model parameters and, indeed on the model itself. These densities enable one to answer key questions about the underlying system. A DCM comprises two parts; one part describes the dynamics within and among neuronal sources, and the second describes how source dynamics generate data in the sensors, using the lead-field. The parameters of this spatiotemporal model are estimated using a single (iterative) Bayesian procedure. In this paper, we will motivate and describe the current DCM framework. Two examples show how the approach can be applied to M/EEG experiments.
动态因果建模（DCM）是一种最初用于分析功能磁共振成像（fMRI）以量化大脑区域之间有效连接的方法。最近，这个框架已经在磁共振/脑电图（M/EEG）领域得到了扩展和建立。M/EEG 的 DCM 需要对多个条件下的全时空诱发反应模型进行反转。该模型基于电生理数据的生物物理和神经生物学生成模型。生成模型是关于数据如何生成的规定。DCM 的反转提供了模型参数的条件密度，实际上也提供了模型本身的条件密度。这些密度使人们能够回答关于基础系统的关键问题。DCM 由两部分组成；一部分描述神经元源内和源间的动力学，另一部分描述源动力学如何使用导联场在传感器中生成数据。这个时空模型的参数是使用单个（迭代）贝叶斯过程来估计的。在本文中，我们将激发并描述当前的 DCM 框架。两个例子展示了如何将该方法应用于 M/EEG 实验。
