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Dynamic Causal Modeling of the Response to Frequency Deviants

机译:频率偏差响应的动态因果建模

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摘要

This article describes the use of dynamic causal modeling to test hypotheses about the genesis of evoked responses. Specifically, we consider the mismatch negativity (MMN), a well-characterized response to deviant sounds and one of the most widely studied evoked responses. There have been several mechanistic accounts of how the MMN might arise. It has been suggested that the MMN results from a comparison between sensory input and a memory trace of previous input, although others have argued that local adaptation, due to stimulus repetition, is sufficient to explain the MMN. Thus the precise mechanisms underlying the generation of the MMN remain unclear. This study tests some biologically plausible spatiotemporal dipole models that rest on changes in extrinsic top-down connections (that enable comparison) and intrinsic changes (that model adaptation). Dynamic causal modeling suggested that responses to deviants are best explained by changes in effective connectivity both within and between cortical sources in a hierarchical network of distributed sources. Our model comparison suggests that both adaptation and memory comparison operate in concert to produce the early (N1 enhancement) and late (MMN) parts of the response to frequency deviants. We consider these mechanisms in the light of predictive coding and hierarchical inference in the brain.
机译:本文介绍了使用动态因果模型来测试有关诱发反应发生的假设。具体来说,我们考虑失配负性(MMN),对异常声音的充分表征的响应以及最广泛研究的诱发响应之一。关于MMN可能如何产生的机制有好几种。已经提出,MMN是由感觉输入和先前输入的记忆轨迹之间的比较产生的,尽管其他人认为,由于刺激重复,局部适应足以解释MMN。因此,尚不清楚MMN产生的确切机制。这项研究测试了一些生物学上合理的时空偶极子模型,这些模型建立在外部自上而下的连接变化(可以进行比较)和内在变化(模型适应)上。动态因果模型表明,通过在分布式源分层网络中的皮质源之内和之间的有效连通性的变化,可以最好地解释对偏差的响应。我们的模型比较表明,适应和记忆比较可以协同工作,以产生对频率偏差响应的早期(N1增强)和晚期(MMN)部分。我们根据预测编码和大脑中的层次推理来考虑这些机制。

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