Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA.
Program in Neuroscience, Indiana University, Bloomington, IN, USA.
Nat Neurosci. 2020 Dec;23(12):1644-1654. doi: 10.1038/s41593-020-00719-y. Epub 2020 Oct 19.
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，其中细胞、群体和区域通过解剖学或功能连接相互连接。该模型无法解释边缘之间的相互作用。在这项研究中，我们开发了一种以边缘为中心的网络模型，该模型生成了“边缘时间序列”和“边缘功能连接”(eFC)的构建体。通过网络分析，我们表明，在静息状态下，eFC 在不同数据集之间是一致的，并且在同一个体的多次扫描会话中具有可重复性。我们证明，聚类 eFC 可以产生边缘社区，这些社区自然地将大脑划分为重叠的集群，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 受到感觉输入变化的系统调节。在未来的工作中，边缘中心方法可能有助于识别疾病的新生物标志物、描述个体差异以及绘制高度解析的神经回路的结构。
