2.8 Image segmentation & Structural network analysis
During the culture lifespan, the neurons grow new connections and self-structure themselves into a complex network, whose structural evolution is the objective of the study. Therefore, from the large-scale photographs acquired during the monitoring days, we extracted the connection scheme and mapped it into a graph using an image segmentation algorithm coded in MatLab to identify both the neurons and their connections. The whole procedure is illustrated in Figure 2. Panels A1-3 show the growth evolution of a small part of the whole culture between day 1 and 15. Panels B1-3 show the result of the image segmentation algorithm after detecting neurons (and their aggregates) in red colour while the segmented neurites are marked in green. This information is used to construct a connectivity graph (panels C1-3) where nodes (green circles) are single neurons or aggregates and links (blue connecting lines) are either direct paths between nodes or through a branching path.
Our aim is to compare the evolution of CNNs cultured on a chip to those cultured on Petri dishes as previously reported in Refs.. The network evolution involves the creation of new links connecting nodes over time. We monitor the progress of several commonly studied graph parameters such as the average clustering coefficient (C), which accounts for how densely connected is each local vicinity of the network, and the shortest path length (L), computed in the largest connected component, that quantifies how far are the nodes in the topological sense. These two last parameters are used as an indicator of the balance between the local and long-distance connectivity in the network.