Department of Clinical Psychology and Neuropsychology, Institute of Psychology, Faculty of Philosophy and Social Sciences, Nicolaus Copernicus University in Torun, Gagarina 39 Street, 87-100, Torun, Poland.
Faculty of Physics, University of Warsaw, Pasteur 5 Street, 02-093, Warsaw, Poland.
Behav Brain Funct. 2023 Oct 5;19(1):18. doi: 10.1186/s12993-023-00218-7.
The study aimed to determine how the resting-state EEG (rsEEG) complexity changes both over time and space (channels). The complexity of rsEEG and its sex/gender differences were examined using the multivariate Multiscale Entropy (mMSE) in 95 healthy adults. Following the probability maps (Giacometti et al. in J Neurosci Methods 229:84-96, 2014), channel sets have been identified that correspond to the functional networks. For each channel set the area under curve (AUC), which represents the total complexity, MaxSlope-the maximum complexity change of the EEG signal at thefine scales (1:4 timescales), and AvgEnt-to the average entropy level at coarse-grained scales (9:12 timescales), respectively, were extracted. To check dynamic changes between the entropy level at the fine and coarse-grained scales, the difference in mMSE between the #9 and #4 timescale (DiffEnt) was also calculated.
We found the highest AUC for the channel sets corresponding to the somatomotor (SMN), dorsolateral network (DAN) and default mode (DMN) whereas the visual network (VN), limbic (LN), and frontoparietal (FPN) network showed the lowest AUC. The largest MaxSlope were in the SMN, DMN, ventral attention network (VAN), LN and FPN, and the smallest in the VN. The SMN and DAN were characterized by the highest and the LN, FPN, and VN by the lowest AvgEnt. The most stable entropy were for the DAN and VN while the LN showed the greatest drop of entropy at the coarse scales. Women, compared to men, showed higher MaxSlope and DiffEnt but lower AvgEnt in all channel sets.
Novel results of the present study are: (1) an identification of the mMSE features that capture entropy at the fine and coarse timescales in the channel sets corresponding to the main resting-state networks; (2) the sex/gender differences in these features.
本研究旨在确定静息态 EEG（rsEEG）的复杂性如何随时间和空间（通道）而变化。我们使用多元多尺度熵（mMSE）检查了 95 名健康成年人的 rsEEG 复杂性及其性别差异。根据概率图谱（Giacometti 等人，《神经科学方法》，229:84-96, 2014），确定了与功能网络相对应的通道集。对于每个通道集，提取了代表总复杂性的曲线下面积（AUC）、最大斜率（粗粒化尺度（9:12 倍频程）上 EEG 信号的最大复杂性变化）和平均熵（细粒化尺度（1:4 倍频程）上的平均熵水平）。为了检查粗粒化和细粒化尺度上熵水平之间的动态变化，还计算了#9 和#4 倍频程之间的 mMSE 差异（DiffEnt）。
我们发现与躯体运动（SMN）、背外侧网络（DAN）和默认模式（DMN）相对应的通道集具有最高的 AUC，而视觉网络（VN）、边缘网络（LN）和额顶网络（FPN）则具有最低的 AUC。最大斜率最大的是 SMN、DMN、腹侧注意网络（VAN）、LN 和 FPN，最小的是 VN。SMN 和 DAN 的平均熵最高，LN、FPN 和 VN 的平均熵最低。DAN 和 VN 的熵最稳定，而 LN 在粗粒化尺度上的熵下降最大。与男性相比，女性在所有通道集中的 MaxSlope 和 DiffEnt 更高，但 AvgEnt 更低。
本研究的新结果是：（1）确定了在与主要静息态网络相对应的通道集中捕获细粒化和粗粒化时间尺度上熵的 mMSE 特征；（2）这些特征中的性别差异。
