Network neuroscience has relied on a node-centric network model in which cells, populations and regions are linked to one another via anatomical or functional connections. This model cannot account for interactions of edges with one another. In this study, we developed an edge-centric network model that generates constructs ‘edge time series’ and ‘edge functional connectivity’ (eFC). Using network analysis, we show that, at rest, eFC is consistent across datasets and reproducible within the same individual over multiple scan sessions. We demonstrate that clustering eFC yields communities of edges that naturally divide the brain into overlapping clusters, with regions in sensorimotor and attentional networks exhibiting the greatest levels of overlap. We show that eFC is systematically modulated by variation in sensory input. In future work, the edge-centric approach could be useful for identifying novel biomarkers of disease, characterizing individual variation and mapping the architecture of highly resolved neural circuits.

网络神经科学依赖于以节点为中心的网络模型，在该模型中，细胞，种群和区域通过解剖或功能连接相互链接。这种模型无法解释边之间的相互作用。在这项研究中，我们开发了一个以边缘为中心的网络模型，该模型生成构造“边缘时间序列”和“边缘功能连接”（eFC）。使用网络分析，我们发现，静止时，eFC在数据集之间是一致的，并且可以在多个扫描会话中的同一个人内重现。我们证明，聚类eFC产生的边缘社区自然将大脑分成重叠的集群，感觉运动和注意力网络中的区域表现出最大程度的重叠。我们表明，eFC通过感官输入的变化被系统地调制。在以后的工作中，