Parametric Resonance as an Interpretative Framework of Coupled Large-Scale Neural Dynamics

Maria Teresa Caccamo; Katia Aragona; Alessandro Sergi; Salvatore Magazù

Abstract

This paper deals with a theoretical paradigm wherein the ElectroEncephaloGram (EEG) brain activity is interpreted as the macroscopic expression of a nonequilibrium oscillatory system influenced by synchronization and parametric resonance effects. In particular, within the proposed model, neural populations are regarded as interacting groups whose collective dynamics produce coherent rhythms that synchronize themselves across different time scales. On this ground, we investigate a collection of neural spectra recorded during wake-to-sleep and sleep-to-wake transitions and show that, within the theta-alpha frequency range examined here (4–12 Hz) the dominant oscillatory components exhibit approximately harmonic frequency relationship. These observations are consistent with a resonance-based interpretation. Furthermore, the EEG records acquired from an individual experiencing the wake-to-sleep and the sleep-to-wake transitions, analyzed by Fourier transform, suggest that energy transfers take place between different spectral bands, this latter showing pseudo-fine structures.

References

1.
Breakspear, M.; Heitmann, S.; Daffertshofer, A. Generative models of cortical oscillations: Neurobiological implications of the Kuramoto model. Front. Hum. Neurosci. 2010, 4, 190.
2.
Buzsáki, G. Rhythms of the Brain; Oxford University Press: Oxford, UK, 2006.
3.
Niedermeyer, E.; da Silva, F.L. Electroencephalography: Basic Principles, Clinical Applications, and Related Fields; Lippincott Williams & Wilkins: Philadelphia, PA, USA, 2005.
4.
Steriade, M.; McCormick, D.A.; Sejnowski, T.J. Thalamocortical oscillations in the sleeping and aroused brain. Science 1993, 262, 679–685.
5.
Pikovsky, A.; Rosenblum, M.; Kurths, J. Synchronization: A Universal Concept in Nonlinear Sciences; Cambridge University Press: Oxford, UK, 2001.
6.
Uhlhaas, P.J.; Singer, W. Neural synchrony in brain disorders: Relevance for cognitive dysfunctions and pathophysiology. Neuron 2006, 52, 155–168.
7.
Stout, J.J.; George, A.E.; Kim, S.; et al. Using synchronized brain rhythms to bias memory-guided decisions. eLife 2024, 12, RP92033.
8.
Gallego-Carracedo, C.; Perich, M.G.; Chowdhury, R.H.; et al. Local field potentials reflect cortical population dynamics in a region-specific and frequency-dependent manner. eLife 2022, 11, e73155.
9.
Musotto, R.; Wanderlingh, U.; Pioggia, G. Ca²⁺ waves in astrocytes: Computational modeling and experimental data. Front. Cell. Neurosci. 2025, 19, 1536096.
10.
Musotto, R.; Wanderlingh, U.; D’Ascola, A.; et al. Dynamics of astrocytes Ca²⁺ signaling: A low-cost fluorescence customized system for 2D cultures. Front. Cell Dev. Biol. 2024, 12, 1320672.
11.
Musotto, R.; Pioggia, G. Neuromechanical network model. Front. Phys. 2025, 13, 1656677.
12.
Sergi, A.; Messina, A.; Martino, G.; et al. The quantum-classical complexity of consciousness and orchestrated objective reduction. Front. Hum. Neurosci. 2025, 19, 1630906. https://doi.org/10.3389/fnhum.2025.1630906.
13.
Breakspear, M. Dynamic models of large-scale brain activity. Nat. Neurosci. 2017, 20, 340–352. https://doi.org/10.1038/nn.4497.
14.
Deco, G.; Jirsa, V.K.; McIntosh, A.R. Emerging concepts for the dynamical organization of resting-state activity in the brain. Nat. Rev. Neurosci. 2011, 12, 43–56. https://doi.org/10.1038/nrn2961.
15.
Friston, K. The free-energy principle: A unified brain theory? Nat. Rev. Neurosci. 2010, 11, 127–138. https://doi.org/10.1038/nrn2787.
16.
Kuramoto, Y. Chemical Oscillations, Waves, and Turbulence; Springer: Berlin/Heidelberg, Germany, 1984.
17.
Strogatz, S.H. From Kuramoto to Crawford: Exploring the onset of synchronization in populations of coupled oscillators. Phys. D Nonlinear Phenom. 2000, 143, 1–20.
18.
Magazù, S.; Caccamo, M.T. Parametric resonance brain model. Sci. Rep. 2024, 14, 24657.
19.
Klimesch, W. Alpha-band oscillations, attention, and controlled access to stored information. Trends Cogn. Sci. 2012, 16, 606–617.
20.
Fries, P. Rhythms for cognition: Communication through coherence. Neuron 2015, 88, 220–235.
21.
Tononi, G.; Cirelli, C. Sleep function and synaptic homeostasis. Sleep Med. Rev. 2006, 10, 49–62.
22.
Colrain, I.M.; Baker, F.C. Changes in sleep as a function of adolescent development. Neuropsychol. Rev. 2011, 21, 5–21.
23.
Grunwald, M. Theta power in the EEG of humans during ongoing processing in a haptic object recognition task. Cogn. Brain Res. 2001, 11, 33–37.
24.
American Academy of Sleep Medicine. The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications (Version 3.0); American Academy of Sleep Medicine: Darien, IL, USA, 2023.
25.
Wei, X.; Avigdor, T.; Ho, A.; et al. ANPHY-Sleep: An Open Sleep Database from Healthy Adults Using High-Density Scalp Electroencephalogram. Sci. Data 2024, 11, 896. https://doi.org/10.1038/s41597-024-03722-1.
26.
Canolty, R.T.; Edwards, E.; Dalal, S.S.; et al. High gamma power is phase-locked to theta oscillations in human neocortex. Science 2006, 313, 1626–1628.
27.
Jensen, O.; Colgin, L.L. Cross-frequency coupling between neuronal oscillations. Trends Cogn. Sci. 2007, 11, 267–269.
28.
Hyafil, A.; Giraud, A.L.; Fontolan, L.; et al. Neural cross-frequency coupling: Connecting architectures, mechanisms, and functions. Trends Neurosci. 2015, 38, 725–740.
29.
Baysal, V. Weak signal detection capacity of type-II Morris–Lecar neuron system under presynaptic bombardments. Eng. Sci. Technol. Int. J. 2024, 57, 101822.

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