Background: The brain networks can provide us an effective way to analyze brain function and brain disease detection. In brain networks, there exist some import neural unit modules, which contain meaningful biological insights.

Objective: Therefore, we need to find the optimal neural unit modules effectively and efficiently.

Method: In this study, we propose a novel algorithm to find community modules of brain networks by combining Neighbor Index and Discrete Particle Swarm Optimization (DPSO) with dynamic crossover, abbreviated as NIDPSO. The differences between this study and the existing ones lie in that NIDPSO is proposed first to find community modules of brain networks, and dose not need to predefine and preestimate the number of communities in advance.

Results: We generate a neighbor index table to alleviate and eliminate ineffective searches and design a novel coding by which we can determine the community without computing the distances amongst vertices in brain networks. Furthermore, dynamic crossover and mutation operators are designed to modify NIDPSO so as to alleviate the drawback of premature convergence in DPSO.

Conclusion: The numerical results performing on several resting-state functional MRI brain networks demonstrate that NIDPSO outperforms or is comparable with other competing methods in terms of modularity, coverage and conductance metrics.

背景：大脑网络可以为我们提供分析大脑功能和脑部疾病检测的有效方法。在脑网络中，存在一些重要的神经单元模块，其中包含有意义的生物学见解。

目的：因此，我们需要有效，高效地找到最佳的神经单位模块。

方法：在这项研究中，我们提出了一种新算法，该算法通过将邻居指数和离散粒子群优化（DPSO）与动态交叉相结合来找到脑网络的社区模块，简称NIDPSO。这项研究与现有研究之间的差异在于，首先提出了NIDPSO来寻找大脑网络的社区模块，而无需预先确定和估计社区的数量。

结果：我们生成了一个邻居索引表来缓解和消除无效搜索，并设计了一种新颖的编码，通过该编码，我们可以确定社区，而无需计算脑网络中各个顶点之间的距离。此外，设计了动态交叉和变异算子来修改NIDPSO，以减轻DPSO中过早收敛的缺点。

结论：在几个静止状态功能性MRI脑网络上进行的数值结果表明，NIDPSO在模块性，覆盖率和电导率指标方面优于或与其他竞争方法相当。