Living Neural Networks: Dynamic Network Analysis of Developing Neural Progenitor Cells

The architecture of the mammalian brain has been characterized through decades of innovation in the field of network neuroscience. However, the assembly of the brain from progenitor cells is an immensely complex process, and a quantitative understanding of how neural progenitor cells (NPCs) form neural networks has proven elusive. Here, we introduce a method that integrates graph-theory with long-term imaging of differentiating human NPCs to characterize the evolution of spatial and functional network features in NPCs during the formation of neural networks in vitro. We find that the rise and fall in spatial network efficiency is a characteristic feature of the transition from immature NPC networks to mature neural networks. Furthermore, networks at intermediate stages of differentiation that display high spatial network efficiency also show high levels of network-wide spontaneous electrical activity. These results support the view that network-wide signaling in immature progenitor cells gives way to a hierarchical form of communication in mature neural networks. We also leverage graph theory to study the spatial features of individual cell types in developing cultures, uncovering spatial features of polarized neuroepithelium. Finally, we employ our method to uncover aberrant network features in a neurodevelopmental disorder using induced pluripotent stem cell (iPSC) models. The “Living Neural Networks” method bridges the gap between developmental neurobiology and network neuroscience, and offers insight into the relationship between developing and mature neural networks.