In mammals, an endogenous clock located in the suprachiasmatic nucleus (SCN) of the brain regulates the circadian rhythms of physiological and behavioral activities. The SCN is composed of about 20,000 neurons that are autonomous oscillators with nonidentical intrinsic periods ranging from 22 h to 28 h. These neurons are coupled through neurotransmitters and synchronized to form a network, which produces a robust circadian rhythm of a uniform period. The neurons, which are the nodes in the network, are known to be heterogeneous in their characteristics, which is reflected in different phenotypes and different functionality. This heterogeneous nature of the nodes of the network leads to the question as to whether the structure of the SCN network is assortative or disassortative. Thus far, the disassortativity of the SCN network has not been assessed and neither have its effects on the collective behaviors of the SCN neurons. In the present study, we build a directed SCN network composed of hundreds of neurons for a single slice using the method of transfer entropy, based on the experimental data. Then, we measured the synchronization degree as well as the disassortativity coefficient of the network structure (calculated by either the out-degrees or the in-degrees of the nodes) and found that the network of the SCN is a disassortative network. Furthermore, a positive relationship is observed between the synchronization degree and disassortativity of the network, which is confirmed by simulations of our modeling. Our finding suggests that the disassortativity of the network structure plays a role in the synchronization between SCN neurons; that is, the synchronization degree increases with the increase of the disassortativity, which implies that a more heterogeneous coupling in the network of the SCN is important for proper function of the SCN.

中文翻译：

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分解网络结构改善了视交叉上神经元之间的神经元同步。

在哺乳动物中，位于大脑上裂眼上核（SCN）的内源性时钟调节生理和行为活动的昼夜节律。SCN由大约20,000个神经元组成，它们是自主振荡器，其固有周期范围从22 h至28 h不相同。这些神经元通过神经递质耦合并同步形成一个网络，该网络产生均匀周期的稳健的昼夜节律。已知神经元是网络中的节点，其特征是异质的，这反映在不同的表型和不同的功能上。网络节点的这种异质性导致了关于SCN网络的结构是分类还是分类的问题。迄今，SCN网络的分散性尚未评估，也未对SCN神经元的集体行为产生影响。在本研究中，我们基于实验数据，使用转移熵的方法，为单个切片建立了由数百个神经元组成的定向SCN网络。然后，我们测量了网络结构的同步度以及分解度系数（通过节点的出度或入度计算），发现SCN的网络是分解性网络。此外，在网络的同步度和可分解性之间观察到正相关，这通过我们的模型仿真得到了证实。我们的发现表明，网络结构的分解性在SCN神经元之间的同步中起着作用。也就是说，同步度随着解离度的增加而增加，这意味着SCN网络中更异构的耦合对于SCN的正常功能很重要。