A closer look at the apparent correlation of structural and functional connectivity in excitable neural networks
A closer look at the apparent correlation of structural and functional connectivity in excitable neural networks
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DOI：
10.1038/srep07870
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发表时间：
2015-01-19
影响因子：
4.6
通讯作者：
Hilgetag, Claus C.
中科院分区：
文献类型：
--
作者：
Messe, Arnaud;Huett, Marc-Thorsten;Hilgetag, Claus C.
神经系统的结构连接性（SC）和功能连接性（FC）之间的关系是脑网络科学的一个核心问题。然而，这是一个悬而未决的问题，如何强烈的SC-FC关系取决于特定的拓扑特征的大脑网络或用于描述兴奋动力学的模型。使用一个基本模型的离散兴奋单位，遵循一个易感-兴奋不应动态周期（SER模型），我们在这里分析功能连接是如何形成的神经网络的拓扑特征，特别是它的模块化。我们比较了SER模型与另一种成熟的动力学机制Fitzhugh-Nagumo模型的相应模拟结果，以探索SC-FC关系的一般特征。我们发现，这两个模型产生的结果之间的明显差异可以通过调整共激活的整合时间窗口来解决，从中获得FC，从而更清楚地区分共激活和顺序激活。因此，网络模块化出现作为一个重要因素塑造FC-SC的关系在不同的动态模型。
The relationship between the structural connectivity (SC) and functional connectivity (FC) of neural systems is a central focus in brain network science. It is an open question, however, how strongly the SC-FC relationship depends on specific topological features of brain networks or the models used for describing excitable dynamics. Using a basic model of discrete excitable units that follow a susceptible -excited refractory dynamic cycle (SER model), we here analyze how functional connectivity is shaped by the topological features of a neural network, in particular its modularity. We compared the results obtained by the SER model with corresponding simulations by another well established dynamic mechanism, the Fitzhugh-Nagumo model, in order to explore general features of the SC-FC relationship. We showed that apparent discrepancies between the results produced by the two models can be resolved by adjusting the time window of integration of co-activations from which the FC is derived, providing a clearer distinction between co-activations and sequential activations. Thus, network modularity appears as an important factor shaping the FC-SC relationship across different dynamic models.