By Kuzey Sozat, Trenton Jones, Natalia Bojorquez, Cia Varelas, Sanjana Kalagara

Duke University

https://doi.org/10.55894/dv4.14

Abstract

The brain consists of complex sets of neural networks that support sensory integration, computation, and behavior. Olfactory circuits in the D. melanogaster brain serve as a model for understanding how the distinct structure and connectivity of these neural circuits mediate diverse behaviors. Currently, the network- level differences between pheromone-detecting olfactory circuits (DA1, VA1v, VA1d) for attraction and odor- detecting circuits (DA2, DC4) for repulsion remain unclear. Conducting a comparative analysis on neural data from the FlyWire connectome of a female adult D. melanogaster brain, we found that pheromone-detecting glomeruli are laterally located, while repulsive odor-detecting glomeruli are positioned medially in the antennal lobe (AL) and lateral horn (LH). The DA1 olfactory circuit, characterized by high specificity to its ligand cVA, exhibits few local interneurons (LNs) and minimal lateral inhibition, supporting selective attraction behaviors. In contrast, DC4, associated with acidic compound detection, shows extensive LN connectivity, facilitating broad sensory integration. In higher-order pathways to neuromodulatory neurons, pheromone circuits (DA1, VA1v, VA1d) more directly connect to dopaminergic neurons (DSK, PAM), supporting sexual behavior and associative learning. Furthermore, inputs from olfactory circuits to insulin-producing cells connect through dopaminergic DSK neurons, suggesting a metabolic influence on sexual attraction behaviors. Finally, clock neurons regulating circadian rhythm receive attractive and repulsive olfactory inputs primarily through the lateral horn, which may align innate olfactory-driven behaviors with circadian activity patterns. These findings provide new insights into the structural and functional organization of D. melanogaster olfactory circuits, revealing how sensory information is spatially segregated and modulates neural networks for survival, learning, and attraction.