Dynamic causal modelling of evoked responses: The role of intrinsic connections
Dynamic causal modelling of evoked responses: The role of intrinsic connections
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DOI：
10.1016/j.neuroimage.2007.02.046
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发表时间：
2007-06-01
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：
Kiebel, Stefan J.;Garrido, Marta I.;Friston, Karl J.
动态因果模型是一种表征诱发反应的方法，通过磁/脑电图（M/EEG）测量。动态因果模型（DCM）是一种时空生成的事件相关场/反应（ERP/ERF）数据网络模型。使用贝叶斯模型反演，可以计算DCM的生理参数的后验分布及其边际似然用于模型比较。模型比较可用于测试关于如何产生电生理数据的机制假设。在这项工作中，我们期待在内在的（源）和外在的（源之间）连接在产生失配响应的变化的相对重要性。简而言之，我们将内在连通性的调制引入DCM框架。这对于检验神经元对局部影响的反应适应性的假设是有用的，与来自其他来源的长距离外部连接（向前、向后和侧向）介导的影响有关。我们说明了这种扩展使用的合成数据和经验数据从一个古怪的ERP实验。(c)2007年爱思唯尔公司All rights reserved.
Dynamic causal modelling is an approach to characterising evoked responses as measured by magneto/electroencephalography (M/EEG). A dynamic causal model (DCM) is a spatiotemporal, generative network model for event-related fields/responses (ERP/ERF) data. Using Bayesian model inversion, one can compute the posterior distributions of the DCM's physiological parameters and its marginal likelihood for model comparison. Model comparison can be used to test mechanistic hypotheses about how electrophysiological data were generated. In this work, we look at the relative importance of changes in intrinsic (within source) and extrinsic (between sources) connections in generating mismatch responses. In short, we introduce the modulation of intrinsic connectivity to the DCM framework. This is useful for testing hypotheses about adaptation of neuronal responses to local influences, in relation to influences that are mediated by long-range extrinsic connections (forward, backward, and lateral) from other sources. We illustrate this extension using synthetic data and empirical data from an oddball ERP experiment. (c) 2007 Elsevier Inc. All rights reserved.