Hierarchical communities in the larval Drosophila connectome: Links to cellular annotations and network topology

• 成果类型: Article
• 署名作者: Betzel, Richard; Puxeddu, Maria Grazia; Seguin, Caio
• 署名单位: Indiana University System; Indiana University Bloomington; Indiana University System; Indiana University Bloomington; Indiana University System; Indiana University Bloomington; University of Minnesota System; University of Minnesota Twin Cities
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-12554
• DOI: 10.1073/pnas.2320177121
• 发表日期: 2024-09-17
• 关键词: small-world; functional connectivity; stochastic blockmodels; complex networks; rich-club; ORGANIZATION; specificity; cost; architecture; modularity

摘要

One of the longstanding aims of network neuroscience is to link a connectome's topological properties-i.e., features defined from connectivity alone-with an organism's neurobiology. One approach for doing so is to compare connectome properties with annotational maps. This type of analysis is popular at the meso-/macroscale, but is less common at the nano-scale, owing to a paucity of neuron-level connectome data. However, recent methodological advances have made possible the reconstruction of whole-brain connectomes at single-neuron resolution for a select set of organisms. These include the fruit fly, Drosophila melanogaster, and its developing larvae. In addition to fine-scale descriptions of connectivity, these datasets are accompanied by rich annotations. Here, we use a variant of the stochastic blockmodel to detect multilevel communities in the larval Drosophila connectome. We find that communities partition neurons based on function and cell type and that most interact assortatively, reflecting the principle of functional segregation. However, a small number of communities interact nonassortatively, forming form a rich-club of interneurons that receive sensory/ascending inputs and deliver outputs along descending pathways. Next, we investigate the role of community structure in shaping communication patterns. We find that polysynaptic signaling follows specific trajectories across modular hierarchies, with interneurons playing a key role in mediating communication routes between modules and hierarchical scales. Our work suggests a relationship between system- level architecture and the biological function and classification of individual neurons. We envision our study as an important step toward bridging the gap between complex systems and neurobiological lines of investigation in brain sciences.