Developmental patterns of the C. elegans neural circuits using community detection

C. elegans serves as a prime model for dissecting the developmental trajectories of nervous systems, yet, few studies have probed the functional development of its neural connectome. In this work, we utilized an enhanced version of BIGCLAM, a community detection algorithm tailored for weighted and directed networks that explicitly captures overlapping modules, to delineate neural circuits across C. elegans' developmental stages (from L1 larvae to adults) based on developmental neural connectome datasets. Our findings reveal that neural circuits are initially small and simple during early development, whereas they undergo marked expansion, increased structural complexity and functional diversification in later phases. Notably, neural circuits emerge progressively: neurons integrate sequentially into functional assemblies, enabling a transition from basic survival-centric behaviors to complex behavioral repertoires. In contrast to conventional experiment-based approaches, this topological analysis framework facilitates quasi-precise detection of neural circuits with minimal resource consumption.