RT Journal Article
SR Electronic
T1 Living Neural Networks: Dynamic Network Analysis of Developing Neural Progenitor Cells
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 055533
DO 10.1101/055533
A1 Arun S. Mahadevan
A1 Nicolas E. Grandel
A1 Jacob T. Robinson
A1 Amina A. Qutub
YR 2017
UL http://biorxiv.org/content/early/2017/08/25/055533.abstract
AB 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 neuronal 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 neuronal 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. The Living Neural Networks method bridges the gap between developmental neurobiology and network neuroscience, and offers insight into the relationship between developing and mature neuronal networks.