IEEE Trans Neural Syst Rehabil Eng. 2021;29:1546-1556. doi: 10.1109/TNSRE.2021.3092140. Epub 2021 Aug 10.
At present, most brain functional studies are based on traditional frequency bands to explore the abnormal functional connections and topological organization of patients with depression. However, they ignore the characteristic relationship of electroencephalogram (EEG) signals in the time domain. Therefore, this paper proposes a network decomposition model based on Improved Empirical Mode Decomposition (EMD), it is suitable for time-frequency analysis of brain functional network. On the one hand, it solves the problem of mode mixing on original EMD method, especially on high-density EEG data. On the other hand, by building brain function networks on different intrinsic mode function (IMF), we can perform time-frequency analysis of brain function connections. It provides a new insight for brain function connectivity analysis of major depressive disorder (MDD). Experimental results found that the IMFs waveform decomposed by Improved EMD was more stable and the difference between IMFs was obvious, it indicated that the mode mixing can be effectively solved. Besides, the analysis of the brain network, we found that the changes in MDD functional connectivity on different IMFs, it may be related to the pathological changes for MDD. More statistical results on three network metrics proved that there were significant differences between MDD and normal controls (NC) group. In addition, the aberrant brain network structure of MDDs was also confirmed in the hubs characteristic. These findings may provide potential biomarkers for the clinical diagnosis of MDD patients.
目前，大多数脑功能研究都是基于传统频段来探索抑郁症患者异常的功能连接和拓扑组织。然而，它们忽略了脑电图（EEG）信号在时域中的特征关系。因此，本文提出了一种基于改进的经验模态分解（EMD）的网络分解模型，它适用于脑功能网络的时频分析。一方面，它解决了原始 EMD 方法中的模态混叠问题，特别是在高密度 EEG 数据上。另一方面，通过在不同的固有模态函数（IMF）上构建脑功能网络，可以对脑功能连接进行时频分析。这为研究重度抑郁症（MDD）的脑功能连接提供了新的视角。实验结果发现，改进的 EMD 分解的 IMF 波形更稳定，IMF 之间的差异也更明显，这表明模态混叠可以得到有效解决。此外，通过对脑网络的分析，我们发现 MDD 在不同的 IMF 上的功能连接发生了变化，这可能与 MDD 的病理变化有关。三个网络指标的更多统计结果证明，MDD 组与正常对照组（NC）之间存在显著差异。此外，MDD 患者的异常脑网络结构也在枢纽特征中得到了证实。这些发现可能为 MDD 患者的临床诊断提供潜在的生物标志物。
