Chen Wan, Cai Yanping, Li Aihua, Jiang Ke, Su Yanzhao
Rocket Force University of Engineering, Xi'an, 710025, China.
Heliyon. 2024 Aug 27;10(17):e36991. doi: 10.1016/j.heliyon.2024.e36991. eCollection 2024 Sep 15.
Existing studies have shown that the brain network of major depression disorder (MDD) has abnormal topologies. However, constructing reliable MDD brain networks is still an open problem.
This paper proposed a reliable MDD brain network construction method. First, seven connectivity methods are used to calculate the correlation between channels and obtain the functional connectivity matrix. Then, the matrix is binarized using four binarization methods to obtain the EEG brain network. Besides, we proposed an improved binarization method based on the criterion of maximizing differences between groups: the adaptive threshold (AT) method. The AT can automatically set the optimal binarization threshold and overcome the artificial influence of traditional methods. After that, several network metrics are extracted from the brain network to analyze inter-group differences. Finally, we used statistical analysis and Fscore values to compare the performance of different methods and establish the most reliable method for brain network construction.
In theta, alpha, and total frequency bands, the clustering coefficient, global efficiency, local efficiency, and degree of the MDD brain network decrease, and the path length of the MDD brain network increases.
The results show that AT outperforms the existing binarization methods. Compared with other methods, the brain network construction method based on phase-locked value (PLV) and AT has better reliability.
MDD has brain dysfunction, particularly in the frontal and temporal lobes.
现有研究表明，重度抑郁症（MDD）的脑网络具有异常拓扑结构。然而，构建可靠的MDD脑网络仍然是一个未解决的问题。
本文提出了一种可靠的MDD脑网络构建方法。首先，使用七种连接性方法计算通道间的相关性，得到功能连接矩阵。然后，使用四种二值化方法对矩阵进行二值化，得到脑电图脑网络。此外，我们基于最大化组间差异的准则提出了一种改进的二值化方法：自适应阈值（AT）方法。AT可以自动设置最优二值化阈值，克服传统方法的人为影响。之后，从脑网络中提取多个网络指标来分析组间差异。最后，我们使用统计分析和F分数值来比较不同方法的性能，建立最可靠的脑网络构建方法。
在θ波、α波和全频段中，MDD脑网络的聚类系数、全局效率、局部效率和度降低，MDD脑网络的路径长度增加。
结果表明，AT优于现有的二值化方法。与其他方法相比，基于锁相值（PLV）和AT的脑网络构建方法具有更好的可靠性。
MDD存在脑功能障碍，尤其是在额叶和颞叶。
