There are a huge amount of neural units in brain networks. Some of the neural units have tight connection and form neural unit modules. These unit modules are helpful to the disease detection and target therapy. A good method can find neural unit modules accurately and effectively. The study proposes a new algorithm to analyze a brain network and obtain its neural unit modules. The proposed algorithm combines the uniform design and the fruit fly optimization algorithm (FOA); therefore, we called it as UFOA. It makes the utmost of their respective merits of the uniform design and the FOA, so as to acquire the feasible solutions scattered uniformly over the vector domain and find the optimal solution as quickly as possible. When compared with other existing methods, FOA and the uniform design are integrated first, and UFOA is first utilized to find unit modules from brain networks. 37 TD resting-state functional MRI brain networks are used to testify the performance of UFOA. The obtained experimental results manifest that UFOA is clearly superior to the other five methods in terms of modularity, and is comparable with the other five methods in terms of conductance. Additionally, the comparative analysis of UFOA and FOA also demonstrates that the uniform design brings benefit to the improvement of UFOA.

中文翻译：

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结合均匀设计和果蝇优化算法找到用于脑网络的社区模块。

脑网络中有大量的神经单元。一些神经单元紧密连接并形成神经单元模块。这些单元模块有助于疾病检测和目标治疗。一种好的方法可以准确有效地找到神经单元模块。该研究提出了一种新的算法来分析大脑网络并获得其神经单位模块。该算法结合了统一设计和果蝇优化算法（FOA）。因此，我们将其称为UFOA。它充分利用了统一设计和FOA各自的优点，以便获得均匀分散在矢量域上的可行解，并尽快找到最佳解。与其他现有方法相比，FOA和统一设计首先集成在一起，UFOA首先用于从大脑网络中查找单元模块。37 TD静止状态功能性MRI脑网络用于证明UFOA的性能。获得的实验结果表明，UFOA在模块性方面明显优于其他五种方法，并且在电导率方面可与其他五种方法相媲美。此外，UFOA和FOA的对比分析还表明，统一的设计为UFOA的改进带来了好处。