带时间约束的Louvain算法在动态脑功能网络模块化中的应用研究#br#

针对在动态脑功能网络的模块化属性研究中,Louvain算法因过度追求模块度值最大化而导致的动态脑功能网络模块辨识度不高的问题,提出了一种带时间约束的Louvain算法。该算法以整个数据采集区间上的模块度值分布为依据构建迭代结束条件,以时间约束来达到模块在规模和数量上的均衡,从而保证模块划分更加合理。将本文算法用于静息态脑功能的模块划分实验时,对比结果显示,与原Louvain算法相比,带时间约束的Louvain算法能够得到更为合理的模块化结果,并可以观测到动态脑功能网络中小规模的模块结构。而采用本文算法用于健康人和自闭症患者的动态脑功能网络模块度对比实验,能够揭示两者在模块化上存在显著差别,从而验证了本文算法的有效性。

Application of time-constrained Louvain algorithm in modularization of dynamic brain function network

In the study of modularization attributes of dynamic brain function network, Louvain algorithm maximizes the modularity value, which leads to low recognition of dynamic brain function network modules. For solving this problem, a time-constrained Louvain algorithm is proposed. In this algorithm, the iterative end condition for time constraint model is constructed based on the distribution of modularity values on the entire data acquisition time period. With this time constraint condition, the ba- lance of both scale and quantity of modules can be achieved, which will be helpful to ensure the reasonability of modules. When this method is used in the module partitioning experiment of resting-state brain function network, the experiment results show that, compared with Louvain algorithm, the time- constrained Louvain algorithm can provide a more reasonable modularity recognition result. Furthermore, the dynamic brain function network modularity comparison experiments with this method between healthy people and autistic patients are carried out, and the results prove that there are significant differences in the modularization features between these two groups, which can verify the effectiveness of this method.