Acupuncture is a mild therapy in rehabilitation practice of peripheral nerve injury. Previous studies confirmed the deep participation of brain plasticity in the process of functional restoration. The therapeutic effect of acupuncture is also believed to be closely associated with brain plasticity, especially in the hypothalamus and limbic system. But the fuzzy neural mechanism somehow limits the application or improvement of this therapy. There is little information about the effect of acupuncture on topological properties of brain networks. Instead of functional segregation approach, we utilized graph theory method to analyze the large-scale and distributed properties of information processing. We first established rat model of sciatic nerve injury and performed rehabilitation therapy of electroacupuncture for 120 days. Meanwhile, we used independent component analysis to extract seven sub-networks from the whole brain. Then measurements of graph theory were calculated in each sub-network as well as the whole brain network. We found no significant difference of any measurement in whole brain network among intervention group, model group and normal group. But the assortativity, hierarchy, small-world properties of sub-network displayed significant differences among three groups. It induces changes of neural plasticity in several sub-networks instead of whole brain network. We attributed the changes to the enhancement of the short-term compensatory adaptation and the reduction of the long-term overacting regional information transmission. The present study may shed light on the vague distinction of large-scale property of brain networks after electroacupuncture, which leads to a better understanding of this ancient traditional Chinese therapy.

在周围神经损伤的康复实践中，针灸是一种温和的疗法。先前的研究证实了大脑可塑性在功能恢复过程中的深入参与。人们还认为，针灸的治疗作用与大脑可塑性密切相关，尤其是在下丘脑和边缘系统中。但是模糊神经机制在某种程度上限制了该疗法的应用或改进。关于针灸对大脑网络拓扑特性影响的信息很少。代替功能隔离方法，我们使用图论方法来分析信息处理的大规模和分布式特性。我们首先建立了坐骨神经损伤的大鼠模型，并进行了120天的电针康复治疗。与此同时，我们使用独立成分分析从整个大脑中提取了七个子网。然后，在每个子网以及整个大脑网络中计算图论的度量。我们发现干预组，模型组和正常组在全脑网络中的任何测量均无显着差异。但是，子网的分类性，层次结构和小世界属性在三组之间显示出显着差异。它在几个子网而不是整个大脑网络中引起神经可塑性的变化。我们将这些变化归因于短期补偿适应的增强和长期过度反应的区域信息传递的减少。本研究可能会揭示电针后脑网络的大规模特性的模糊区分，