Event Abstract

Towards well-defined node-based topologies of functionally directional neuronal circuits on multielectrode arrays

  • 1 ETH Zürich, Department for Biomedical Engineering, Switzerland

While the field of connectomics is advancing the knowledge of network mapping and structural wiring of the brain, there is a limited toolbox for determining how the structural motif of a neuronal circuit can affect function at a network level. The formation of neuronal circuits and functional activity groups is strongly influenced not only by genetic directive but also by experience-dependent learning [1]. Because the spatio-temporal properties of networks are continuously changing, knowing the static connectivity of the brain alone is not enough to understand functional dynamics. Networks, or individual cells within a network, are in complex activity states dependent on many uncontrollable factors [2]. Thus, it remains difficult to target and research the underlying, universal mechanisms of neuronal circuits, the processing of information, and the decoding of well-defined activity patterns. The key to understanding how information is transmitted and processed is to move away from defining a single neuron as a functional unit and toward studying how circuit ensembles generate functionality [3]. In vivo research and theoretical network models suggest that information processing circuits are highly organized in modular, node-based structures that exhibit adaptive intra-network and inter-network functional connectivity [4]. To complement this work, in vitro bottom-up neuroscience aims to minimize the confounding variables present in vivo by building neuronal networks of different topologies and content based on experimental hypothesis. A particularly useful tool to probe in vitro networks has been substrate-integrated microelectrode arrays (MEA) [5]. MEA recordings of neuronal cultures have already revealed many spatio-temporal properties of network development, connectivity, and spontaneous activity. However, a major disadvantage of these randomly connected networks is that there is a lack of control over circuit development and architecture, resulting in high variation between samples. There is thus an initiative to engineer in vitro patterned circuits that are more physiologically relevant and controlled [6]. By directing the structural motif and connectivity of a network, there is also a greater probability to achieve a high fidelity of signal transmission and reproducibility of evoked network responses. Existing methods to create modular, oriented networks fall into the broad categories of either a) surface modifications or b) 3D structural confinement. While surface patterning can be effective in guiding adhesion and neurite outgrowth, long-term structural integrity of the network depends on stability of surface molecule adsorption, often resulting in overgrown networks over time. Methods that use structural confinements eliminate the need for long-term non-fouling regions and strongly promote neurite guidance via interactions with 3D physical barriers. The adaptation of compartmentalized microfluidic devices coupled to MEAs has been particularly significant towards engineering modular networks [7]. Most devices are designed to have wells or chambers for initial cell placement with connecting microchannels of dimensions suitable only for neurite outgrowth. The most common material used for device fabrication is polydimethylsiloxane (PDMS), a transparent elastomer easily molded into versatile geometric structures at nano-scale resolution. Despite the introduction of patterning methods, as of yet, there is no robust technique to test how different functional motifs arise based on the geometrical and topological properties of a node-based network. While some reports have utilized PDMS structures with more than two chambers/nodes or demonstrated signal transmission mostly in one direction, none of the techniques have achieved small-scale, multi-node networks with stable, long-term connections and high yield of directionality. Here, we executed a screening of ten different microchannel architectures within polydimethylsiloxane (PDMS) devices to test which could induce the highest probability of directional neurite guidance via microchannel confinement. Of the structures tested, the most successful architecture had a probability of 92% of achieving fully unidirectional connections between nodes. Neurons connected bidirectionally within control structures. Networks cultured on 60 electrode MEAs were recorded on days in vitro (DIV) 9,12,15 and 18 to investigate neuronal spiking activity. Transfer entropy (TE) between subsequent nodes showed significant (p < 0.05) directional flow of information versus the control. Additionally, directed networks encoded a greater amount of information, reinforcing the importance of directional connections in the brain being critical for reliable communication. Recordings of electrically stimulated activity of a well-defined circuit additionally proved functional connectivity with directional signal propagation. 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.

References

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Keywords: neuronal network, unidirectional circuits, network theory, multielectrode array, structural axon guidance, functional connectivity

Conference: MEA Meeting 2018 | 11th International Meeting on Substrate Integrated Microelectrode Arrays, Reutlingen, Germany, 4 Jul - 6 Jul, 2018.

Presentation Type: Poster Presentation

Topic: Microphysiological systems

Citation: Thompson-Steckel G, Weaver S, Weydert S, Forro C, Ihle SJ and Voros J (2019). Towards well-defined node-based topologies of functionally directional neuronal circuits on multielectrode arrays. Conference Abstract: MEA Meeting 2018 | 11th International Meeting on Substrate Integrated Microelectrode Arrays. doi: 10.3389/conf.fncel.2018.38.00116

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Received: 18 Mar 2018; Published Online: 17 Jan 2019.

* Correspondence: Ms. Greta Thompson-Steckel, ETH Zürich, Department for Biomedical Engineering, Zurich, Switzerland, Thompson@biomed.ee.ethz.ch