CAREER: Circuit discovery in the Drosophila connectome

The fruit fly uses its senses, its memory, and its internal states to make decisions about where to lay an egg, what to eat, and whether to pursue or accept a mate. This project seeks to discover the neural pathways and circuits in the fruit fly’s brain that enable it to flexibly make decisions. These parts of the brain are difficult to study with traditional methods. The recent availability of connectome data – full reconstructions of entire brain volumes – now provide researchers with access to the parts of the fly’s brain that ostensibly combine sensory information with memories and internal states to effect an action. This project will analyze the fruit fly connectome using methods that have traditionally been used for studying social networks. Network science tools can identify groups of neurons that interact with regions of known function, thus enabling the discovery of neural circuits that support cognitive-level function in the fruit fly. This project will advance our understanding of how flexible, context-dependent interactions happen in the brain. Current technology cannot autonomously emulate these flexible and context-dependent behaviors at such a small scale. The project’s education plan will broaden participation in scientific research through innovative workshops and courses for middle school, high school and college students that engage the next generation of researchers in learning basic coding and network analyses, and in exploring the connectome. The public availability of connectome data also makes it possible to engage young people in computational neuroscience research at scale. In addition to strengthening the U.S. domestic workforce through student training, this project will increase national competitiveness in science and engineering – specifically in pursuit of elucidating the parts of the fly’s brain that endow it with flexible, context-dependent behavior. The neural circuits that perform context-dependent computations are largely unknown and difficult to study with commonly used methods such as genetic manipulations which require a known target with an associated driver line. Researchers posit that the diffuse neuropils of the superior protocerebrum contain circuits for higher-order processing. The Drosophila connectome is a volumetric reconstruction of an entire brain, including annotated cell types with no currently known function in the diffuse regions of the brain. This project will leverage the connectome to discover the neural circuits that produce cognitive-level computations related to decision-making in oviposition, and other ethologically-relevant behaviors. Community detection methods, together with other statistical network analyses and computational modeling, will be employed to: (1) characterize the circuit structure of the understudied regions of the brain believed to support context-dependent computations; (2) investigate the inputs to the oviposition pre-motor circuit to connect populations of unknown cell types to behaviorally-relevant function; and, (3) determine the utility of community detection methods for detecting microscale circuits for dendritic processing. The project will lead to experimentally testable predictions for the neurons, cell types, and circuits that drive context-dependent behaviors in a model organism that has a wealth of genetic tools available for testing the resulting predictions. Furthermore, these studies will lead to computational principles of Drosophila brain organization and an enhanced understanding of how to use network science tools to reveal and investigate neuronal circuits. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. NSF Award ID: 2543078 | Program: 01002930DB NSF RESEARCH & RELATED ACTIVIT,01002627DB NSF RESEARCH & RELATED ACTIVIT,01003031DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Gabrielle Gutierrez | Institution: Barnard College, NEW YORK, NY | Award Amount: $256,207 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2543078 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2543078.html