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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Lachaux, J.P., Rodriguez, E., Martinerie, J.: Measuring phase synchrony in brain signals. Hum. Brain Mapp. 8, 194–208 (1999)
Fries, P.: A mechanism for cognitive dynamics: neuronal communication through neuronal coherence. Trends Cogn. Sci. 9, 474–480 (2005)
Thatcher, R.W.: Coherence, phase differences, phase shift, and phase lock in EEG/ERP analyses. Dev. Neuropsychol. 37, 476–496 (2012)
Kitzbichler, M.G., Smith, M.L., Christensen, S.R.: Broadband criticality of human brain network synchronization. PLoS Comput. Biol. 5, e1000314 (2009)
Werner, G.: Metastability, criticality and phase transitions in brain and its models. Biosyst. 90, 496–508 (2007)
Bassett, D.S., Meyer-Lindenberg, A., Achard, S.: Adaptive reconfiguration of fractal small-world human brain functional networks. Proc. Natl. Acad. Sci. 103, 19518–19523 (2006)
Tadel, F., Baillet, S., Mosher, J.C.: Brainstorm: a user-friendly application for MEG/EEG analysis. Comput. Intell. Neurosci. 2011, 8 (2011)
Thatcher, R.W., North, D.M., Biver, C.J.: Self-organized criticality and the development of EEG phase reset. Hum. Brain Mapp. 30, 553–574 (2009)
Clauset, A., Shalizi, C.R., Newman, M.E.J.: Power-law distributions in empirical data. SIAM Rev. 51, 661–703 (2009)
Levina, A., Herrmann, J.M., Geisel, T.: Phase transitions towards criticality in a neural system with adaptive interactions. Phys. Rev. Lett. 102, 118110 (2009)
Levina, A., Herrmann, J.M., Geisel, T.: Dynamical synapses causing self-organized criticality in neural networks. Nat. Phys. 3, 857–860 (2007)
Thatcher, R.W., North, D.M., Neubrander, J.: Autism and EEG phase reset: deficient GABA mediated inhibition in thalamo-cortical circuits. Dev. Neuropsychol. 34, 780–800 (2009)
Zhang, L., Gan, J.Q., Wang, H.: Optimized gamma synchronization enhances functional binding of fronto-parietal cortices in mathematically gifted adolescents during deductive reasoning. Front. Hum. Neurosci. 8, 430 (2014)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, L., Shi, B., Cao, M., Zhang, S., Dai, Y., Zhu, Y. (2018). Estimating Criticality of Resting-State Phase Synchronization Network Based on EEG Source Signals. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11307. Springer, Cham. https://doi.org/10.1007/978-3-030-04239-4_31
Download citation
DOI: https://doi.org/10.1007/978-3-030-04239-4_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04238-7
Online ISBN: 978-3-030-04239-4
eBook Packages: Computer ScienceComputer Science (R0)