The resting-state brain networks (RSNs) reflects the functional connectivity patterns between brain modules, providing essential foundations for decoding intrinsic neural information within the brain. It serves as one of the primary tools for describing the spatial dynamics of the brain using various neuroimaging techniques, such as electroencephalography (EEG) and magnetoencephalography (MEG). However, the distribution rules or potential modes of functional connectivity weights in the resting state remain unclear. In this context, we first start from simulation, using forward solving model to generate scalp EEG with four channel densities (19, 32, 64, 128). Subsequently, we construct scalp brain networks using five coupling measures, aiming to explore whether different channel density or coupling measures affect the distribution pattern of functional connectivity weights. Next, we quantify the distribution pattern by calculating the skewness, kurtosis, and Shannon entropy of the functional connectivity network weights. Finally, the results of the simulation were validated in a normative database. We observed that: 1) The functional connection weights exhibit a right-skewed distribution, and are not influenced by channel density or coupling measures; 2) The functional connection weights exhibit a relatively uniform distribution, with the potential for volume conduction to affect the degree of uniformity in the distribution; 3) Networks constructed using coupling measures influenced by volume conduction exhibit significant correlations between the average connection weight and measures of skewness, kurtosis, and Shannon entropy. This study contributes to a deeper understanding of RSNs, providing valuable insights for research in the field of neuroscience, and holds promise for being associated with brain cognition and disease diagnosis.

翻译：静息态脑网络反映了大脑模块间的功能连接模式，为解码大脑内部固有神经信息提供了重要基础。作为利用脑电图、脑磁图等多种神经影像技术描述大脑空间动态特性的主要工具之一，静息态脑网络的功能连接权重在静息状态下的分布规律或潜在模式仍不明确。在此背景下，本研究首先从仿真出发，采用正向求解模型生成四种通道密度（19、32、64、128）的 scalp EEG。随后，我们使用五种耦合度量构建头皮脑网络，旨在探究不同通道密度或耦合度量是否会影响功能连接权重的分布模式。接着，通过计算功能连接网络权重的偏度、峰度和香农熵来量化分布模式。最后，仿真结果在规范数据库中得到了验证。我们观察到：1）功能连接权重呈现右偏分布，且不受通道密度或耦合度量的影响；2）功能连接权重呈现相对均匀的分布，容积传导可能影响分布的均匀程度；3）采用受容积传导影响的耦合度量所构建的网络，其平均连接权重与偏度、峰度及香农熵度量间存在显著相关性。本研究有助于深化对静息态脑网络的理解，为神经科学领域的研究提供有价值的见解，并有望与脑认知及疾病诊断建立关联。