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A New Novel Synchronization Index of Brain Networks in Hyperbolic EEG Dynamics

Year 2022, Volume: 26 Issue: 3, 600 - 607, 30.06.2022
https://doi.org/10.16984/saufenbilder.999015

Abstract

The functional connectivity of brain connectivity changes its pattern over time i.e. dynamics, even in the resting state with an infinite number of degrees of freedom with local couplings. Recently, quantifying the level of synchrony has received considerable attention. We hypothesized that time-varying instantaneous phase synchronization over local couplings are defined in hyperbolic space and different brain regions can identify failures, flexibility, and stability in network dynamics. Our goal is to understand the phase synchronization changes of the beta-gamma band, and in addition, to investigate Shannon entropy based on phase synchronization stability. Whole EEG dynamics from local phase synchronizations was used to detect treatment resistance from both hemispheres in OCD patients. Temporal filtering and Hilbert transforms were performed to infer beta-gamma band phase difference activity from the EEG brain dynamics. Then, the response beta-gamma band phase stability was quantified using a new phase synchronization index (PSI). Results indicated significantly changed phase synchronization of the response and non-response to treatment, patients in OCD patients in F7 electrode. Greater phase fluctuations of beta-gamma synchronizations in treatment resistance OCD is claiming phase deficiencies within neural populations. This study first provides experimental and theoretical support for characterizing cycle structure depends on the non-Euclidian dynamics of neural phase synchrony caused by disturbances of underlying neurotransmitter systems, as reflected in different normal and disease states.

References

  • [1] Freeman, Walter J.. “Origin, structure, and role of background EEG activity. Part 1. Analytic amplitude,” Clinical Neurophysiology 115 (2004): 2077-2088.
  • [2] L Tass, P., Rosenblum, M. G., Weule, J., Kurths, J., Pikovsky, A., Volkmann, J., … Freund, H.-J. (1998). “Detection of n:m Phase Locking from Noisy Data: Application to Magnetoencephalography,” Physical Review Letters, 81(15), 3291–3294.
  • [3] Freeman, W. J., & Rogers, L. J. (2002). “Fine temporal resolution of the analytic phase reveals episodic synchronization by state transitions in gamma EEGs. Journal of Neurophysiology,” 87(2).
  • [4] Kamaradova, D., Hajda, M., Prasko, J., Taborsky, J., Grambal, A., Latalova, K., Hlustik, P. (2016). “Cognitive deficits in patients with obsessive-compulsive disorder -electroencephalography correlates. Neuropsychiatric Disease and Treatment,” 12,1119–25.
  • [5] Chen, Shaolin and David Kalaj. “Lipschitz continuity of holomorphic mappings with respect to Bergman metric,” arXiv: Complex Variables (2017): n. pag.
  • [6] Koppleman W and Pincus JD, (1959) “Spectral representations for finite Hilbert transform,” Math Z. vol. 71, pp. 399-407.
  • [7] Tass P, Kurths J, Rosenblum M, Weule J, Pikovsky A, Volkmann J, Schnitzler H, Freund H. In: Uhl C, editor. (1999), “Complex phase synchronization in neurophysiological data. Analysis of neurophysiological brain functioning,” Berlin: Springer. p. 252–73.
  • [8] Freeman, Walter J.. “A pseudo-equilibrium thermodynamic model of information processing in nonlinear brain Dynamics,” Neural networks : the official journal of the International Neural Network Society 21 2-3 (2008): 257-65.
Year 2022, Volume: 26 Issue: 3, 600 - 607, 30.06.2022
https://doi.org/10.16984/saufenbilder.999015

Abstract

References

  • [1] Freeman, Walter J.. “Origin, structure, and role of background EEG activity. Part 1. Analytic amplitude,” Clinical Neurophysiology 115 (2004): 2077-2088.
  • [2] L Tass, P., Rosenblum, M. G., Weule, J., Kurths, J., Pikovsky, A., Volkmann, J., … Freund, H.-J. (1998). “Detection of n:m Phase Locking from Noisy Data: Application to Magnetoencephalography,” Physical Review Letters, 81(15), 3291–3294.
  • [3] Freeman, W. J., & Rogers, L. J. (2002). “Fine temporal resolution of the analytic phase reveals episodic synchronization by state transitions in gamma EEGs. Journal of Neurophysiology,” 87(2).
  • [4] Kamaradova, D., Hajda, M., Prasko, J., Taborsky, J., Grambal, A., Latalova, K., Hlustik, P. (2016). “Cognitive deficits in patients with obsessive-compulsive disorder -electroencephalography correlates. Neuropsychiatric Disease and Treatment,” 12,1119–25.
  • [5] Chen, Shaolin and David Kalaj. “Lipschitz continuity of holomorphic mappings with respect to Bergman metric,” arXiv: Complex Variables (2017): n. pag.
  • [6] Koppleman W and Pincus JD, (1959) “Spectral representations for finite Hilbert transform,” Math Z. vol. 71, pp. 399-407.
  • [7] Tass P, Kurths J, Rosenblum M, Weule J, Pikovsky A, Volkmann J, Schnitzler H, Freund H. In: Uhl C, editor. (1999), “Complex phase synchronization in neurophysiological data. Analysis of neurophysiological brain functioning,” Berlin: Springer. p. 252–73.
  • [8] Freeman, Walter J.. “A pseudo-equilibrium thermodynamic model of information processing in nonlinear brain Dynamics,” Neural networks : the official journal of the International Neural Network Society 21 2-3 (2008): 257-65.
There are 8 citations in total.

Details

Primary Language English
Journal Section Research Articles
Authors

Rüştü Murat Demirer 0000-0002-5508-741X

Publication Date June 30, 2022
Submission Date September 22, 2021
Acceptance Date May 6, 2022
Published in Issue Year 2022 Volume: 26 Issue: 3

Cite

APA Demirer, R. M. (2022). A New Novel Synchronization Index of Brain Networks in Hyperbolic EEG Dynamics. Sakarya University Journal of Science, 26(3), 600-607. https://doi.org/10.16984/saufenbilder.999015
AMA Demirer RM. A New Novel Synchronization Index of Brain Networks in Hyperbolic EEG Dynamics. SAUJS. June 2022;26(3):600-607. doi:10.16984/saufenbilder.999015
Chicago Demirer, Rüştü Murat. “A New Novel Synchronization Index of Brain Networks in Hyperbolic EEG Dynamics”. Sakarya University Journal of Science 26, no. 3 (June 2022): 600-607. https://doi.org/10.16984/saufenbilder.999015.
EndNote Demirer RM (June 1, 2022) A New Novel Synchronization Index of Brain Networks in Hyperbolic EEG Dynamics. Sakarya University Journal of Science 26 3 600–607.
IEEE R. M. Demirer, “A New Novel Synchronization Index of Brain Networks in Hyperbolic EEG Dynamics”, SAUJS, vol. 26, no. 3, pp. 600–607, 2022, doi: 10.16984/saufenbilder.999015.
ISNAD Demirer, Rüştü Murat. “A New Novel Synchronization Index of Brain Networks in Hyperbolic EEG Dynamics”. Sakarya University Journal of Science 26/3 (June 2022), 600-607. https://doi.org/10.16984/saufenbilder.999015.
JAMA Demirer RM. A New Novel Synchronization Index of Brain Networks in Hyperbolic EEG Dynamics. SAUJS. 2022;26:600–607.
MLA Demirer, Rüştü Murat. “A New Novel Synchronization Index of Brain Networks in Hyperbolic EEG Dynamics”. Sakarya University Journal of Science, vol. 26, no. 3, 2022, pp. 600-7, doi:10.16984/saufenbilder.999015.
Vancouver Demirer RM. A New Novel Synchronization Index of Brain Networks in Hyperbolic EEG Dynamics. SAUJS. 2022;26(3):600-7.