加权Fast Newman模块化算法在人脑结构网络中的应用

针对二值人脑结构网络的模块化方法不足以反映复杂的人脑生理特征这一问题，提出一种基于Fast Newman二值算法的加权脑网络模块化算法。该算法以凝聚节点的层次聚类思想为基础，以脑网络中单个脑区节点的权重值和脑网络总权重值为主要依据构建加权模块度评价指标，并将其增量作为度量值来确定加权脑网络中节点的合并从而实现模块划分。将该算法应用于60个健康人的组平均数据中的实验结果显示，与二值人脑网络模块化结果相对比，所提算法得到的模块度提高了28%，并且模块内部和模块外部的特征区分更加明显，所得到的人脑模块也更符合已知的人脑生理特性；而与现有的两种加权模块化算法实验对比结果表明，所提算法在合理划分人脑网络模块结构的同时也小幅提高了模块度。

Application of weighted Fast Newman modularization algorithm in human brain structural network

The binary brain network modularization is not enough to describe physiological features of human brain. In order to solve the problem, a modularization algorithm for weighted brain network based on Fast Newman binary algorithm was presented. Using the hierarchical clustering idea of condensed nodes as the base, a weighted modularity indicator was built with the main bases of single node's weight and entire network's weight. Then the modularity increment was taken as the testing index to decide which two nodes should be combined in weighted brain network and realize module partition. The proposed method was applied to detect the modular structure of the group average data of 60 healthy people. The experiment results showed that, compared with the modular structure of the binary brain network, the brain network modularity of the proposed method was increased by 28% and more significant difference between inside and outside of modules could be revealed. Moreover, the modular structure found by the proposed method is more consistent with the physiological characteristics of human brain. Compared with the other two existing weighted modular algorithms, the proposed method can also slightly improve the modularity and guarantee a reasonable identification for human brain modular structure.