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Resiliency of EEG-Based Brain Functional Networks.
1School of Electrical and Computer Engineering, RMIT University, Melbourne, Australia.
Plos One
|August 22, 2015
Summary
Brain functional networks derived from electroencephalography (EEG) are less resilient to failures than random networks. This suggests that real brain networks are more vulnerable to node removal and cascading failures.
Area of Science:
- Neuroscience
- Network Science
- Graph Theory
Background:
- Brain functional networks are increasingly studied using network science and graph theory.
- Electroencephalography (EEG) time series are commonly used to extract these networks in health and disease.
Purpose of the Study:
- To investigate the failure resiliency of EEG-based brain functional networks.
- To compare the vulnerability of real brain networks to that of randomized networks.
Main Methods:
- EEG time series from 30 healthy subjects (resting state, eyes-closed) were analyzed.
- Network structures were extracted from EEG data.
- Resiliency metrics were measured and compared to corresponding random networks.
Main Results:
- EEG-based brain networks exhibited significantly lower resiliency compared to random networks (P < 0.05).
- Brain networks showed higher vulnerability, with global efficiency being more affected by node removal.
- Cascading failures in brain networks were more severe, resulting in fewer surviving nodes than in randomized networks (P < 0.05).
Conclusions:
- Real EEG-based brain networks are less resilient to failures than expected.
- These findings suggest that brain networks may not have evolved optimal resiliency mechanisms against disruptions.
