Prediction of functional neural circuits in caenorhabditis elegans based on overlapping community detection.

Abstract

The identification of functional neural circuits is crucial for understanding brain functions. However, experimental methods are often labor-intensive and resource-intensive. In this study, we modified the BIGCLAM algorithm to detect overlapping communities in directed and weighted networks and applied it to the neural networks of hermaphrodite and male Caenorhabditis elegans (C. elegans). Given the high similarity in connotation between network communities and functional neural circuits, we can predict functional neural circuits by detecting communities within the neural networks, thereby reducing the complexity of experimental research. In hermaphrodites, we predicted functional neural circuits related to various behaviors, including egg-laying, pharyngeal regulation, stress-induced sleep, tail sensation, and mechanosensation. In males, we identified functional neural circuits involved in sex-specific behaviors, such as mating and mate-searching, as well as those related to mechanosensation and food representation. These findings provide new insights into the neural mechanisms underlying behaviors and sexual dimorphism in C. elegans. The modified algorithm also has potential applications in analyzing other complex systems.