Lee Hyungsub, Yi Gwan-Su, Nam Yoonkey
Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141 Republic of Korea.
KAIST Institute for Health Science and Technology, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141 Republic of Korea.
Biomed Eng Lett. 2023 Jun 3;13(4):659-670. doi: 10.1007/s13534-023-00289-5. eCollection 2023 Nov.
Modularity is one of the important structural properties that affect information processing and other functionalities of neuronal networks. Researchers have developed in-vitro clustered network models for reproducing the modularity, but it is still challenging to control the segregation and integration of several sub-populations of them. We cultured clustered networks with alginate patterning and collected the electrophysiological signals to investigate the changes in functional properties during the development. We built inter-connected neuronal clusters using alginate micro-patterning with a circular shape on the surface of the micro-electrode array. The neuronal clusters were enabled to be connected at 3 or 10 days-in-vitro (DIV) by removing the barrier. The neuronal signals from different types of networks were collected from 16 to 34 DIV, and functional characteristics were examined. Connectivity and burst motif analysis were carried out to find out the relation between the structure and function of the networks. Neuronal networks with clustered structure showed different activity properties from the random networks along the development. The clustered networks had more short-range connections compared to the random networks. In the network burst motif analysis, the clustered networks showed more various patterns and a slower propagation of the activation patterns. In this study, we successfully cultured neuronal networks with clustered structure, and the structure affected the functional properties. The network model suggested in this study will be a good solution for observing the effect of structure on function during their development.
The online version contains supplementary material available at 10.1007/s13534-023-00289-5.
模块化是影响神经网络信息处理和其他功能的重要结构特性之一。研究人员已经开发出体外集群网络模型来重现模块化，但控制其中几个亚群的分离和整合仍然具有挑战性。我们利用藻酸盐图案化培养集群网络并收集电生理信号，以研究其发育过程中功能特性的变化。我们在微电极阵列表面使用圆形藻酸盐微图案构建相互连接的神经元集群。通过去除屏障，使神经元集群在体外3天或10天（DIV）时能够连接。在16至34 DIV期间收集来自不同类型网络的神经元信号，并检查功能特性。进行连通性和爆发基序分析以找出网络结构与功能之间的关系。具有集群结构的神经网络在发育过程中表现出与随机网络不同的活动特性。与随机网络相比，集群网络具有更多的短程连接。在网络爆发基序分析中，集群网络表现出更多样化的模式和激活模式的较慢传播。在本研究中，我们成功培养了具有集群结构的神经网络，并且该结构影响了功能特性。本研究中提出的网络模型将是观察其发育过程中结构对功能影响的一个很好的解决方案。
在线版本包含可在10.1007/s13534-023-00289-5获取的补充材料。
