Disassortative Network Structure Improves the Synchronization between Neurons in the Suprachiasmatic Nucleus

Abstract

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.

Cocaine experience induces functional adaptations in astrocytes: Implications for synaptic plasticity in the nucleus accumbens shell

“…In neuronal networks, disassortativity has been proposed to improve network stability 72 which may indicate reduced sensitivity to weak stimuli 73 . Interestingly, disassortative neural networks have also been linked to increased synchronisation of activity 74 . Network level relationships have not been previously explored among astrocytes in situ and it is too early to say whether changes that we observed here warrant similar conclusions with regard to stimulus sensitivity, synchronisability, etc.…”

Network Structure of the Master Clock Is Important for Its Primary Function

“…It was found that the incoming degree is similar for most SCN neurons but the outgoing degree varies between SCN neurons and the nodes with large outgoing degree are located in the core (McBride and Petzold, 2018). We found that the SCN network for each slice partially is a scale-free network (Gu et al, 2019a). A power-law distribution of the node degrees was found, as well as a short averaged path length (around 2.5).…”

“…In order to explore the directed network structure of the SCN, two articles performed an analysis on the data from Ref. (Abel et al, 2017) using the method of information theory based technique (McBride and Petzold, 2018) and the method of the transfer entropy (Gu et al, 2019a). It was found that the incoming degree is similar for most SCN neurons but the outgoing degree varies between SCN neurons and the nodes with large outgoing degree are located in the core (McBride and Petzold, 2018).…”

“…Additionally, we examined whether the network structure is disassortative or assortative (Gu et al, 2019a). In the disassortative network, the nodes preferably connect to other nodes with a different node degree, while in the assortative network, the nodes preferably connect to nodes with a similar node degree.…”

“…Several typical types of the network structures (or topologies) have been widely studied in a lot of fields, including all-to-all connectivity networks, regularly connected networks, Newman-Watts (NW) small-world networks and scale-free networks (Vasalou et al, 2009;Hafner et al, 2012;Gu et al, 2019a). For simplicity, we will first consider undirected networks, where the connections between nodes always go both ways.…”