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Frequency dependent topological patterns of resting-state brain networks
Long Qian1, Yi Zhang2, Li Zheng1
1Department of Biomedical Engineering, Peking University, Beijing, China.
Plos One
|May 1, 2015
Summary
Complementary ensemble empirical mode decomposition (CEEMD) reveals frequency-specific brain network organization. Ultra-low frequencies (0-0.015 Hz) show the most prominent topological properties in resting-state fMRI data.
Area of Science:
- Neuroscience
- Network Science
- Signal Processing
Background:
- Resting-state fMRI is widely used to study brain network topology.
- Previous studies primarily focused on the 0.01-0.1 Hz frequency band.
- Frequency-specific network properties remain largely unexplored.
Purpose of the Study:
- To investigate frequency-specific topological patterns in brain networks.
- To introduce and validate complementary ensemble empirical mode decomposition (CEEMD) for fMRI analysis.
- To explore intrinsic oscillation rhythms within BOLD signals.
Main Methods:
- Applied CEEMD to decompose BOLD signals into intrinsic frequency bands.
- Analyzed topological properties of resulting temporally correlated networks.
- Compared CEEMD with empirical mode decomposition (EMD) and a rectangular window band-pass filter.
Main Results:
- BOLD signals were divided into five distinct frequency bands using CEEMD.
- Global topological properties were most prominent in ultra-low frequencies (0-0.015 Hz).
- Small-world architecture saliency showed frequency-density dependency.
Conclusions:
- CEEMD effectively separates intrinsic oscillation rhythms from BOLD signals, outperforming EMD.
- Ultra-low frequencies are crucial for understanding brain network topology.
- CEEMD offers a robust method for frequency-specific fMRI analysis.
