Connectivity and motif analysis of in-vitro clustered neuronal network models using microelectrode arrays

Abstract

The study of the relationship between structure and function in neuronal networks is an important area of neuroscience. Neuronal networks are known to have structural features at different shapes and scales, which have a significant impact on their function. In particular, modularity has received much attention as a structural feature that affects the robustness to stimulation, survivability, and functional diversity of neuronal networks. In addition, alginate hydrogel can be erased in the middle of the culture period with minimal damage to the neuronal networks, allowing the structure of the networks to be manipulated. In this study, I aimed to create neuronal network models with clustered structures using alginate to reproduce the modularity of neural networks, and I modulated their connections and developed a functional analysis system. For the functional analysis of neuronal networks, I first developed a cross-correlation-based connectivity analysis method and improved network burst motif finding method. I also implemented an automatic analysis system to apply them. The clustered networks were then cultured on microelectrode array using alginate hydrogel patterning, and their functional properties were analyzed. They showed differences in connectivity and motif properties compared to unstructured neuronal networks. The combination of chemical patterning and clustered networks was used to modulate network connectivity. The clustered networks created through linear and modular patterns showed not only structural connections but also functional connections based on the patterns.