Frequency Dependent Topological Patterns of Resting-State Brain Networks

The topological organization underlying brain networks has been extensively investigated using resting-state fMRI, focusing on the low frequency band from 0.01 to 0.1 Hz. However, the frequency specificities regarding the corresponding brain networks remain largely unclear. In the current study, a data-driven method named complementary ensemble empirical mode decomposition (CEEMD) was introduced to separate the time series of each voxel into several intrinsic oscillation rhythms with distinct frequency bands. Our data indicated that the whole brain BOLD signals could be automatically divided into five specific frequency bands. After applying the CEEMD method, the topological patterns of these five temporally correlated networks were analyzed. The results showed that global topological properties, including the network weighted degree, network efficiency, mean characteristic path length and clustering coefficient, were observed to be most prominent in the ultra-low frequency bands from 0 to 0.015 Hz. Moreover, the saliency of small-world architecture demonstrated frequency-density dependency. Compared to the empirical mode decomposition method (EMD), CEEMD could effectively eliminate the mode-mixing effects. Additionally, the robustness of CEEMD was validated by the similar results derived from a split-half analysis and a conventional frequency division method using the rectangular window band-pass filter. Our findings suggest that CEEMD is a more effective method for extracting the intrinsic oscillation rhythms embedded in the BOLD signals than EMD. The application of CEEMD in fMRI data analysis will provide in-depth insight in investigations of frequency specific topological patterns of the dynamic brain networks.

本研究深入探讨了大脑网络底层的拓扑组织，通过静息态功能磁共振成像(fMRI)技术，主要聚焦于0.01至0.1赫兹的低频带。然而，关于相应大脑网络的频率特异性仍普遍模糊不清。在当前研究中，引入了一种名为互补集经验模态分解(CEEMD)的数据驱动方法，以将每个体素的时间序列分离成具有不同频带的多个固有振荡节奏。我们的数据表明，全脑血氧水平依赖性(BOLD)信号可以被自动划分为五个特定的频带。在应用CEEMD方法之后，分析了这五个时间相关网络的拓扑模式。结果显示，全局拓扑属性，包括网络加权度、网络效率、平均特征路径长度和聚类系数，在0至0.015赫兹的超低频带中表现尤为显著。此外，小世界结构的显著性显示出频率密度依赖性。与经验模态分解(EMD)方法相比，CEEMD能够有效消除模态混叠效应。此外，通过分割一半分析以及使用矩形窗口带通滤波器的传统频分方法得到的相似结果验证了CEEMD的鲁棒性。我们的研究结果表明，CEEMD相较于EMD，是一种更有效的从BOLD信号中提取固有振荡节奏的方法。CEEMD在fMRI数据分析中的应用，将为动态大脑网络频率特定拓扑模式的研究提供深入的见解。