Modular microstructure design to build neuronal networks of defined functional connectivity
Modular microstructure design to build neuronal networks of defined functional connectivity
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DOI：
10.1016/j.bios.2018.08.075
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发表时间：
2018-12-30
影响因子：
12.6
通讯作者：
Voros, Janos
中科院分区：
文献类型：
--
作者：
Forro, Csaba;Thompson-Steckel, Greta;Voros, Janos
理论和体内神经科学研究表明，神经网络内的功能信息传递受到电路结构的影响。由于大脑的动态复杂性，测试一个已定义网络的结构和功能之间的相关性仍然是一个挑战。工程控制的体外神经网络提供了一种测试结构基序的方法；然而，没有一种方法能够实现具有稳定单向连接的小型多节点网络。在这里，我们在聚二甲基硅氧烷（PDMS）装置中筛选了十种不同的微通道架构，以测试它们在轴突引导方面的潜力。最成功的设计在节点之间实现严格单向连接的概率为92%。通过这种设计构建的网络在多电极阵列上培养，并在离体9、12、15和18天进行记录，以研究自发和诱发的爆发活动。与对照组相比，后续节点之间的传递熵显示出高达100倍的定向信息流。此外，定向网络产生了更多的信息流，强化了大脑中定向连接的重要性，这对可靠的交流至关重要。通过控制网络形成的参数，我们最小化了响应的可变性，并实现了功能性的定向网络。该技术为我们探索不同网络母题的时空效应提供了一种工具。
Theoretical and in vivo neuroscience research suggests that functional information transfer within neuronal networks is influenced by circuit architecture. Due to the dynamic complexities of the brain, it remains a challenge to test the correlation between structure and function of a defined network. Engineering controlled neuronal networks in vitro offers a way to test structural motifs; however, no method has achieved small, multi-node networks with stable, unidirectional connections. Here, we screened ten different microchannel architectures within polydimethylsiloxane (PDMS) devices to test their potential for axonal guidance. The most successful design had a 92% probability of achieving strictly unidirectional connections between nodes. Networks built from this design were cultured on multielectrode arrays and recorded on days in vitro 9, 12, 15 and 18 to investigate spontaneous and evoked bursting activity. Transfer entropy between subsequent nodes showed up to 100 times more directional flow of information compared to the control. Additionally, directed networks produced a greater amount of information flow, reinforcing the importance of directional connections in the brain being critical for reliable communication. By controlling the parameters of network formation, we minimized response variability and achieved functional, directional networks. The technique provides us with a tool to probe the spatio-temporal effects of different network motifs.