Dynamic causal modeling of evoked responses in EEG and MEG
Dynamic causal modeling of evoked responses in EEG and MEG
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DOI：
10.1016/j.neuroimage.2005.10.045
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发表时间：
2006-05-01
期刊：
影响因子：
5.7
通讯作者：
Friston, Karl J.
中科院分区：
文献类型：
--
作者：
David, Olivier;Kiebel, Stefan J.;Friston, Karl J.
神经元上合理的生成模型或前向模型对于理解事件相关场 (ERF) 和电位 (ERP) 的生成方式至关重要。在本文中，我们提出了一种对使用脑电图或脑磁图测量的事件相关反应进行建模的新方法。这种方法使用生物学信息模型来推断产生反应的底层神经元网络。该方法可以被视为神经生物学约束的源重建方案，其中重建的参数具有明确的神经元解释。具体来说，这些参数尤其编码源之间的耦合以及该耦合如何依赖于刺激属性或实验背景。基本思想是用神经元动力学如何生成源活动的模型来补充源如何在测量空间中表达的传统电磁正向模型。这种扩展的正向模型的单次反演可以推断源的空间部署和生成它们的底层神经元架构。重要的是，这一推论涵盖了明确定义的神经元亚群之间的远程连接。在之前的一篇论文中，我们使用分层神经质量模型模拟了 ERP，该模型体现了远程区域之间自下而上、自上而下和横向的连接。在本文中，我们描述了使用经验数据估计该模型参数的贝叶斯过程。我们通过表征皮质-皮质耦合变化在 ERP 发生中的作用来证明这一过程。在第一个实验中，在面部和房屋感知过程中记录的 ERP 被建模为腹侧视觉通路中不同的皮质源。类别选择性，如面部选择性 N170 所索引，可以通过从感觉到腹侧流中更高区域的前向连接的类别特异性差异来解释。我们能够使用连通性的条件估计来量化并推断这些影响。这使我们能够确定在处理流中，类别选择性出现的位置。在第二个实验中，我们使用了一种奇怪的听觉范式来表明，不匹配的负性可以通过连接性的变化来解释。具体来说，使用贝叶斯模型选择，我们评估了后向连接的变化，高于前向连接的变化。根据理论预测，有强有力的证据表明前向和后向耦合中与学习相关的变化。这些例子表明，可以在机械的、生物动机的推理框架内明确评估皮质区域之间的类别或上下文特定的耦合。 (c) 2005 Elsevier Inc. 保留所有权利。
Neuronally plausible, generative or forward models are essential for understanding how event-related fields (ERFs) and potentials (ERPs) are generated. In this paper, we present a new approach to modeling event-related responses measured with EEG or MEG. This approach uses a biologically informed model to make inferences about the underlying neuronal networks generating responses. The approach can be regarded as a neurobiologically constrained source reconstruction scheme, in which the parameters of the reconstruction have an explicit neuronal interpretation. Specifically, these parameters encode, among other things, the coupling among sources and how that coupling depends upon stimulus attributes or experimental context. The basic idea is to supplement conventional electromagnetic forward models, of how sources are expressed in measurement space, with a model of how source activity is generated by neuronal dynamics. A single inversion of this extended forward model enables inference about both the spatial deployment of sources and the underlying neuronal architecture generating them. Critically, this inference covers long-range connections among well-defined neuronal subpopulations.In a previous paper, we simulated ERPs using a hierarchical neural-mass model that embodied bottom-up, top-down and lateral connections among remote regions. In this paper, we describe a Bayesian procedure to estimate the parameters of this model using empirical data. We demonstrate this procedure by characterizing the role of changes in cortico-cortical coupling, in the genesis of ERPs. In the first experiment, ERPs recorded during the perception of faces and houses were modeled as distinct cortical sources in the ventral visual pathway. Category-selectivity, as indexed by the face-selective N170, could be explained by category-specific differences in forward connections from sensory to higher areas in the ventral stream. We were able to quantify and make inferences about these effects using conditional estimates of connectivity. This allowed us to identify where, in the processing stream, category-selectivity emerged.In the second experiment, we used an auditory oddball paradigm to show that the mismatch negativity can be explained by changes in connectivity. Specifically, using Bayesian model selection, we assessed changes in backward connections, above and beyond changes in forward connections. In accord with theoretical predictions, there was strong evidence for learning-related changes in both forward and backward coupling. These examples show that category- or context-specific coupling among cortical regions can be assessed explicitly, within a mechanistic, biologically motivated inference framework. (c) 2005 Elsevier Inc. All rights reserved.