10.1371/journal.pone.0124681
Long Qian
Long
Qian
Yi Zhang
Yi
Zhang
Li Zheng
Li
Zheng
Yuqing Shang
Yuqing
Shang
Jia-Hong Gao
Jia-Hong
Gao
Yijun Liu
Yijun
Liu
Frequency Dependent Topological Patterns of Resting-State Brain Networks
Public Library of Science
2015
CEEMD
brain BOLD signals
fMRI data analysis
emd
oscillation rhythms
mode decomposition method
frequency division method
frequency bands
frequency Dependent Topological Patterns
brain networks
topological patterns
2015-04-30 03:32:30
Dataset
https://plos.figshare.com/articles/dataset/_Frequency_Dependent_Topological_Patterns_of_Resting_State_Brain_Networks_/1399754
<div><p>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.</p></div>