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Estimating Criticality of Resting-State Phase Synchronization Network Based on EEG Source Signals

  • Li Zhang
  • Bo Shi
  • Mingna Cao
  • Sai Zhang
  • Yiming Dai
  • Yanmei Zhu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11307)

Abstract

EEG phase synchrony is an important signature in estimating functional connectivity of brain network, in which criticality of phase-locking state has been viewed as the key factor in facilitating dynamic reorganization of functional network. Based on source trace of resting-state EEG signals recorded from 24 subjects, this study extracted phase-locking intervals (PLIs) between pairwise source signals and constructed PLI sets with size higher than \( 10^{5} \) for each subject, from frontal-parietal, frontal-temporal, and temporal-parietal cortical areas, respectively. Through further data fitting in power-law model, this study finds that θ- (4–8 Hz) and α-band (8–13 Hz) activities have longer phase-locking duration in a broader power-law distribution interval, compared to those in high frequency bands, indicating higher temporal stability of functional coupling between brain areas. In contrast, the probability density of PLIs oscillating in β (13–30 Hz) and γ (30–60 Hz) bands has less data fitting errors and bigger power-law exponent, suggesting higher criticality and flexibility of reorganization of phase synchronization networks. The findings are expected to provide effective neural signatures for comparison and recognition of neural correlations of cognition, emotion, disease etc. in the future.

Keywords

EEG resting-state source networks Phase-locking interval Power-law model Criticality 

Notes

Acknowledgements

This work was supported in part by the Natural Science Foundation of China under Grants 31600862 and 61501115, the Support Program of Excellent Young Talents in Universities of Anhui Province under Grant gxyqZD2017064, the Natural Science Foundation of Jiangsu Province under Grant BK20150633, the China Scholarship Council Fund under Grant 201808340011, the Natural Science Foundation of Bengbu Medical College under Grant BYKY1604ZD and BYKY1638, the Fundamental Research Funds for the Central Universities under Grant CDLS-2018-04, and Key Laboratory of Child Development and Learning Science (Southeast University), Ministry of Education.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Li Zhang
    • 1
    • 2
  • Bo Shi
    • 1
  • Mingna Cao
    • 1
  • Sai Zhang
    • 1
  • Yiming Dai
    • 1
  • Yanmei Zhu
    • 2
  1. 1.School of Medical ImagingBengbu Medical CollegeBengbuChina
  2. 2.Research Center for Learning ScienceSoutheast UniversityNanjingChina

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