12The architecture of the mammalian brain has been characterized through decades of innovation in the 13 field of network neuroscience. However, the assembly of the brain from progenitor cells is an immensely 14 complex process, and a quantitative understanding of how neural progenitor cells (NPCs) form neural 15 networks has proven elusive. Here, we introduce a method that integrates graph-theory with long-term 16 imaging of differentiating human NPCs to characterize the evolution of spatial and functional network 17 features in NPCs during the formation of neural networks in vitro. We find that the rise and fall in spatial 18 network efficiency is a characteristic feature of the transition from immature NPC networks to mature 19 neural networks. Furthermore, networks at intermediate stages of differentiation that display high spatial 20 network efficiency also show high levels of network-wide spontaneous electrical activity. These results 21 support the view that network-wide signaling in immature progenitor cells gives way to a hierarchical 22 form of communication in mature neural networks. We also leverage graph theory to study the spatial 23 features of individual cell types in developing cultures, uncovering spatial features of polarized 24 neuroepithelium. Finally, we employ our method to uncover aberrant network features in a 25 neurodevelopmental disorder using induced pluripotent stem cell (iPSC) models. The "Living Neural 26 Networks" method bridges the gap between developmental neurobiology and network neuroscience, and 27 offers insight into the relationship between developing and mature neural networks. 28 29 42 the fundamental architectural features of neural networks, no models have been available to study the 43 development of human neural networks from progenitor cells in a quantitative manner, nor have there 44 been tools to characterize the spatial and functional dynamics of network formation at the cellular level. 45 46 Cell-cell communication among neural progenitor cells (NPCs) is an essential aspect of human nervous 47 65 features of cortical neurogenesis 19,20 . In addition, iPSC models have been used to study aberrant 66 development in several neurodevelopmental disorders 21,22 . The ubiquity of stem cell differentiation 67 protocols provides a unique opportunity to study the self-assembly of neural networks in a dish. 69In this report, we introduce a method to study network features of developing human neural networks at 70 the global and single-cell levels. We use long-term imaging coupled with automated image analysis to 71 develop network representations of cell spatial topology and assign spatial coordinates to individual cells 72 (Figure 1). We use our method to demonstrate that two independent human NPC cell lines exhibit a 73 similar rise and fall in spatial network efficiency that characterizes the maturation of in vitro neural 74 networks. We demonstrate that high spatial network efficiencies at intermediate stages of neural 75 differentiation are linked with high levels of sp...

Nervous systems are remarkable for supporting stable animal behavior despite dramatic changes to neurons' number and connectivity. An ideal model organism to study this phenomenon would have: 1) dynamic neural architecture 2) transgenic reporters of neural activity, 3) a small, transparent body, and 4) well-defined sensory-motor behaviors. While Hydra vulgaris possesses the first three advantages, it currently lacks well-characterized sensory-motor responses. Here, we show the first quantitative measurements of Hydra's behavioral responses to thermal stimulation and associated neural activity.Specifically, we find that Hydra elongate and then contract when heated. This behavior is accompanied by synchronous, periodic activity of neurons in the animal's peduncle. We find that the frequency of these neural oscillations is nearly the same even if the number of neurons change by a factor of two.These observations suggest that Hydra provides a rich model for studying how animals maintain stable sensory-motor responses within dynamic neural circuit architectures.One sentence summary: Here we show that Hydra have a group of thermally responsive neurons that encodes the absolute temperature of a thermal stimulus and that this encoding is independent of the number of neurons in the animal.

We introduce cytoNet, a method to characterize multicellular topology from microscopy images. 7Accessible over the web, cytoNet quantifies the spatial relationships in cell communities using 8 principles of graph theory, and evaluates the effect of cell-cell interactions on individual cell 9 phenotypes. We demonstrate cytoNet's capabilities in two applications relevant to regenerative 10 medicine: quantifying the morphological response of endothelial cells to neurotrophic factors present 11 in the brain after injury, and characterizing cell cycle dynamics of differentiating neural progenitor cells. 12The framework introduced here can be used to study complex cell communities in a quantitative 13 manner, leading to a deeper understanding of environmental effects on cellular behavior. 14 A cell's place in its environment influences a large part of its behavior. Advances in the field of phenotypic 15 screening have yielded automated image analysis software that provide detailed phenotypic information 16 at the single-cell level (such as morphology, stain texture and stain intensity) from microscopy images in 17 a high-throughput manner 1,2 . However, current image analysis pipelines often do not account for spatial 18 and density-dependent effects on cell phenotype. Various types of cell-cell interactions including 19 juxtacrine and paracrine signaling are an integral part of biological processes that affect the behavior of 20 individual cells. The recent emergence of technologies for multiparametric mapping of protein and RNA 21 expression in individual cells while preserving the spatial structure of the tissue 3 has further highlighted 22 the need to study single-cell behavior in the context of cell communities. 23

One remarkable feature of behavior is an animal's ability to rapidly switch between activity states such as locomotion and quiescence; however, how the brain regulates these spontaneous transitions based on the animal's perceived environment is not well understood. Here we show a C. elegans sleep-like state on a scalable platform that enables simultaneous control of multiple environmental factors including temperature, mechanical stress, and food availability. This brief quiescent state, we refer to as "μSleep," occurs spontaneously in microfluidic chambers, which allows us to track animal movement and perform whole-brain imaging. With these capabilities, we establish that μSleep meets the behavioral requirements of C. elegans sleep and depends on multiple external factors. Specifically, we show that μSleep is regulated by satiety and temperature, which is consistent with prior reports of C. elegans sleep. Additionally, we show for the first time that C. elegans sleep can be induced through mechanosensory pathways. Together, these results establish a rich model system for studying how animals process multiple sensory pathways to regulate behavioral states.