Sun Chumin, Lin K C, Yeung C Y, Ching Emily S C, Huang Yu-Ting, Lai Pik-Yin, Chan C K
Institute of Theoretical Physics and Department of Physics, The Chinese University of Hong Kong, Shatin, Hong Kong.
Department of Physics and Center for Complex Systems, National Central University, Chungli, Taiwan 320, ROC.
Phys Rev E. 2022 Apr;105(4-1):044406. doi: 10.1103/PhysRevE.105.044406.
In the study of biological networks, one of the major challenges is to understand the relationships between network structure and dynamics. In this paper, we model in vitro cortical neuronal cultures as stochastic dynamical systems and apply a method that reconstructs directed networks from dynamics [Ching and Tam, Phys. Rev. E 95, 010301(R) (2017)2470-004510.1103/PhysRevE.95.010301] to reveal directed effective connectivity, namely, the directed links and synaptic weights, of the neuronal cultures from voltage measurements recorded by a multielectrode array. The effective connectivity so obtained reproduces several features of cortical regions in rats and monkeys and has similar network properties as the synaptic network of the nematode Caenorhabditis elegans, whose entire nervous system has been mapped out. The distribution of the incoming degree is bimodal and the distributions of the average incoming and outgoing synaptic strength are non-Gaussian with long tails. The effective connectivity captures different information from the commonly studied functional connectivity, estimated using statistical correlation between spiking activities. The average synaptic strengths of excitatory incoming and outgoing links are found to increase with the spiking activity in the estimated effective connectivity but not in the functional connectivity estimated using the same sets of voltage measurements. These results thus demonstrate that the reconstructed effective connectivity can capture the general properties of synaptic connections and better reveal relationships between network structure and dynamics.
在生物网络研究中，一个主要挑战是理解网络结构与动力学之间的关系。在本文中，我们将体外皮层神经元培养物建模为随机动力系统，并应用一种从动力学重建有向网络的方法[Ching和Tam，《物理评论E》95，010301(R)(2017)2470 - 004510.1103/PhysRevE.95.010301]，从多电极阵列记录的电压测量值中揭示神经元培养物的有向有效连接性，即有向链接和突触权重。如此获得的有效连接性再现了大鼠和猴子皮层区域的若干特征，并且具有与线虫秀丽隐杆线虫的突触网络相似的网络特性，其整个神经系统已被绘制出来。入度分布是双峰的，平均入向和出向突触强度的分布是非高斯的且具有长尾。有效连接性从通常研究的功能连接性中捕获了不同信息，功能连接性是使用尖峰活动之间的统计相关性来估计的。发现在估计的有效连接性中，兴奋性入向和出向链接的平均突触强度随尖峰活动增加，但在使用相同电压测量集估计的功能连接性中并非如此。因此，这些结果表明，重建的有效连接性可以捕获突触连接的一般特性，并更好地揭示网络结构与动力学之间的关系。
