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Hierarchical communities in the larval Drosophila connectome: Links to cellular annotations and network topology
Proceedings of the National Academy of Sciences of the United States of America ( IF 9.4 ) Pub Date : 2024-09-13 , DOI: 10.1073/pnas.2320177121
Richard Betzel 1, 2, 3, 4 , Maria Grazia Puxeddu 1 , Caio Seguin 1
Proceedings of the National Academy of Sciences of the United States of America ( IF 9.4 ) Pub Date : 2024-09-13 , DOI: 10.1073/pnas.2320177121
Richard Betzel 1, 2, 3, 4 , Maria Grazia Puxeddu 1 , Caio Seguin 1
Affiliation
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.
中文翻译:
果蝇幼虫连接组中的分层群落:细胞注释和网络拓扑的链接
网络神经科学的长期目标之一是将连接组的拓扑特性(即仅从连接性定义的特征)与生物体的神经生物学联系起来。这样做的一种方法是将连接组属性与注释图进行比较。这种类型的分析在中观/宏观尺度上很流行,但由于缺乏神经元级连接组数据,在纳米尺度上不太常见。然而,最近的方法学进展使得以单神经元分辨率重建一组选定的生物体的全脑连接体成为可能。其中包括果蝇、黑腹果蝇及其发育中的幼虫。除了连接性的精细描述之外,这些数据集还附有丰富的注释。在这里,我们使用随机模块模型的变体来检测幼虫果蝇连接组中的多级群落。我们发现群落根据功能和细胞类型来划分神经元,并且大多数群落相互作用,反映了功能分离的原则。然而,少数群落以非配对方式相互作用,形成中间神经元的“丰富俱乐部”,接收感觉/上升输入并沿下降路径传递输出。接下来,我们研究社区结构在塑造沟通模式中的作用。我们发现多突触信号传导遵循跨模块层次结构的特定轨迹,中间神经元在调节模块和层次尺度之间的通信路径中发挥着关键作用。我们的工作表明系统级架构与单个神经元的生物功能和分类之间的关系。 我们认为我们的研究是弥合复杂系统与脑科学研究神经生物学研究路线之间差距的重要一步。
更新日期:2024-09-13
中文翻译:
果蝇幼虫连接组中的分层群落:细胞注释和网络拓扑的链接
网络神经科学的长期目标之一是将连接组的拓扑特性(即仅从连接性定义的特征)与生物体的神经生物学联系起来。这样做的一种方法是将连接组属性与注释图进行比较。这种类型的分析在中观/宏观尺度上很流行,但由于缺乏神经元级连接组数据,在纳米尺度上不太常见。然而,最近的方法学进展使得以单神经元分辨率重建一组选定的生物体的全脑连接体成为可能。其中包括果蝇、黑腹果蝇及其发育中的幼虫。除了连接性的精细描述之外,这些数据集还附有丰富的注释。在这里,我们使用随机模块模型的变体来检测幼虫果蝇连接组中的多级群落。我们发现群落根据功能和细胞类型来划分神经元,并且大多数群落相互作用,反映了功能分离的原则。然而,少数群落以非配对方式相互作用,形成中间神经元的“丰富俱乐部”,接收感觉/上升输入并沿下降路径传递输出。接下来,我们研究社区结构在塑造沟通模式中的作用。我们发现多突触信号传导遵循跨模块层次结构的特定轨迹,中间神经元在调节模块和层次尺度之间的通信路径中发挥着关键作用。我们的工作表明系统级架构与单个神经元的生物功能和分类之间的关系。 我们认为我们的研究是弥合复杂系统与脑科学研究神经生物学研究路线之间差距的重要一步。