Network Topology
network topology
Reprogramming the nociceptive circuit topology reshapes sexual behavior in C. elegans
In sexually reproducing species, males and females respond to environmental sensory cues and transform the input into sexually dimorphic traits. Yet, how sexually dimorphic behavior is encoded in the nervous system is poorly understood. We characterize the sexually dimorphic nociceptive behavior in C. elegans – hermaphrodites present a lower pain threshold than males in response to aversive stimuli, and study the underlying neuronal circuits, which are composed of the same neurons that are wired differently. By imaging receptor expression, calcium responses and glutamate secretion, we show that sensory transduction is similar in the two sexes, and therefore explore how downstream network topology shapes dimorphic behavior. We generated a computational model that replicates the observed dimorphic behavior, and used this model to predict simple network rewirings that would switch the behavior between the sexes. We then showed experimentally, using genetic manipulations, artificial gap junctions, automated tracking and optogenetics, that these subtle changes to male connectivity result in hermaphrodite-like aversive behavior in-vivo, while hermaphrodite behavior was more robust to perturbations. Strikingly, when presented with aversive cues, rewired males were compromised in finding mating partners, suggesting that the network topology that enables efficient avoidance of noxious cues would have a reproductive "cost". To summarize, we present a deconstruction of a sex-shared neural circuit that affects sexual behavior, and how to reprogram it. More broadly, our results are an example of how common neuronal circuits changed their function during evolution by subtle topological rewirings to account for different environmental and sexual needs.
Network science and network medicine: New strategies for understanding and treating the biological basis of mental ill-health
The last twenty years have witnessed extraordinarily rapid progress in basic neuroscience, including breakthrough technologies such as optogenetics, and the collection of unprecedented amounts of neuroimaging, genetic and other data relevant to neuroscience and mental health. However, the translation of this progress into improved understanding of brain function and dysfunction has been comparatively slow. As a result, the development of therapeutics for mental health has stagnated too. One central challenge has been to extract meaning from these large, complex, multivariate datasets, which requires a shift towards systems-level mathematical and computational approaches. A second challenge has been reconciling different scales of investigation, from genes and molecules to cells, circuits, tissue, whole-brain, and ultimately behaviour. In this talk I will describe several strands of work using mathematical, statistical, and bioinformatic methods to bridge these gaps. Topics will include: using artificial neural networks to link the organization of large-scale brain connectivity to cognitive function; using multivariate statistical methods to link disease-related changes in brain networks to the underlying biological processes; and using network-based approaches to move from genetic insights towards drug discovey. Finally, I will discuss how simple organisms such as C. elegans can serve to inspire, test, and validate new methods and insights in networks neuroscience.
Generative models of the human connectome
The human brain is a complex network of neuronal connections. The precise arrangement of these connections, otherwise known as the topology of the network, is crucial to its functioning. Recent efforts to understand how the complex topology of the brain has emerged have used generative mathematical models, which grow synthetic networks according to specific wiring rules. Evidence suggests that a wiring rule which emulates a trade-off between connection costs and functional benefits can produce networks that capture essential topological properties of brain networks. In this webinar, Professor Alex Fornito and Dr Stuart Oldham will discuss these previous findings, as well as their own efforts in creating more physiologically constrained generative models. Professor Alex Fornito is Head of the Brain Mapping and Modelling Research Program at the Turner Institute for Brain and Mental Health. His research focuses on developing new imaging techniques for mapping human brain connectivity and applying these methods to shed light on brain function in health and disease. Dr Stuart Oldham is a Research Fellow at the Turner Institute for Brain and Mental Health and a Research Officer at the Murdoch Children’s Research Institute. He is interested in characterising the organisation of human brain networks, with particular focus on how this organisation develops, using neuroimaging and computational tools.
Sex-specific network topology of the nociceptive circuit shapes dimorphic behavior in C. elegans
COSYNE 2022
Sex-specific network topology of the nociceptive circuit shapes dimorphic behavior in C. elegans
COSYNE 2022
Exploiting network topology in brain-scale multi-area model simulations
FENS Forum 2024
Human iPSC-derived neurogenin 2 (NGN2) cortical neurons develop functional connectivity and small-world network topology in vitro
FENS Forum 2024