Directed network discovery with dynamic network modelling.
Directed network discovery with dynamic network modelling.
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DOI：
10.1016/j.neuropsychologia.2017.02.006
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发表时间：
2017-05
期刊：
影响因子：
2.6
通讯作者：
Saxe R
中科院分区：
文献类型：
--
作者：
Anzellotti S;Kliemann D;Jacoby N;Saxe R
认知任务需要多个大脑区域参与。了解这些区域如何相互影响（网络结构）是表征认知过程的神经基础的重要一步。通常，有限的证据可用于限制先验假设的范围，并且需要有效筛选大量可能的网络结构的技术（网络发现）。本文介绍了一种新的网络发现建模技术（动态网络建模或DNM），它建立在格兰杰因果关系和动态因果模型的思想基础上，引入了三个关键的变化：1)有效的网络发现是通过对参与者之间模型参数一致性的统计测试来实现的，2)测试考虑了每种影响的大小和符号，以及3)独立数据中解释的方差被用作网络模型质量的绝对（而不是相对）度量。在这篇文章中，我们概述了DNM的功能，我们验证了DNM在模拟数据中已知的基础真相，我们报告了一个应用于情绪识别过程中区域之间影响调查的例子。揭示当参与者被要求在报告情绪效价和面部年龄之间切换时，编码抽象情绪表征的大脑区域（内侧前额叶皮层和颞上沟）对参与面部表情感知分析的区域（枕面区和梭状回面区）自上而下的影响。
Cognitive tasks recruit multiple brain regions. Understanding how these regions influence each other (the network structure) is an important step to characterize the neural basis of cognitive processes. Often, limited evidence is available to restrict the range of hypotheses a priori, and techniques that sift efficiently through a large number of possible network structures are needed (network discovery). This article introduces a novel modeling technique for network discovery (Dynamic Network Modeling or DNM) that builds on ideas from Granger Causality and Dynamic Causal Modeling introducing three key changes: 1) efficient network discovery is implemented with statistical tests on the consistency of model parameters across participants, 2) the tests take into account the magnitude and sign of each influence, and 3) variance explained in independent data is used as an absolute (rather than relative) measure of the quality of the network model. In this article, we outline the functioning of DNM, we validate DNM in simulated data for which the ground truth is known, and we report an example of its application to the investigation of influences between regions during emotion recognition, revealing top-down influences from brain regions encoding abstract representations of emotions (medial prefrontal cortex and superior temporal sulcus) onto regions engaged in the perceptual analysis of facial expressions (occipital face area and fusiform face area) when participants are asked to switch between reporting the emotional valence and the age of a face.