Abstract
How systems can be functionally integrated is of particular interest in neurosciences, while explaining the underlying bioelectrical phenomena of consciousness. Recent reviews have proposed the cross frequency phase-amplitude coupling (CfM) as a way by which the local brain activities (at high frequency) are integrated in large functional brain networks (by low frequency). Large brain networks are needed for consciousness and for strong cognitive tasks as well as they may be disrupted in pathological conditions as schizophrenia or emotional dysregulation. We have made a math model of high-frequency wave activities (by 9 dipoles) integrated by low-frequency modulators. In this model, “integration” is intended as phase coherence and activity inter correlation between dipoles. The highest integration was found to be linked to specific combination of modulation waves and dipole frequencies and phase differences. These results lead to the hypothesis that integrating the brain networks is not only necessary a low-frequency modulation in the Delta/Theta band, but also a predisposition of dipoles getting an harmonic resonance with that waves; this interpretation highlights the possibility that brain integration needs the past presence of harmonic resonance between dipoles and modulators, in relation with the personal history of parental functioning and neural development.
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References
Allen, E. A., Liu, J., Kiehl, K. A., Gelernter, J., Pearlson, G. D., Perrone-Bizzozero, N. I., & Calhoun, V. D. (2011). Components of cross-frequency modulation in health and disease. Frontiers in Systems Neuroscience, 5, 59. https://doi.org/10.3389/fnsys.2011.00059.
Axmacher, N., Henseler, M. M., Jensen, O., Weinreich, I., Elger, C. E., & Fell, J. (2010). Cross-frequency coupling supports multi-item working memory in the human hippocampus. Proceedings of the National Academy of Sciences of the United States of America, 107(7), 3228–33. https://doi.org/10.1073/pnas.0911531107.
Bressler, S. L., & Menon, V. (2010). Large-scale brain networks in cognition: Emerging methods and principles. Trends in Cognitive Sciences, 14(6), 277–290.
Hunt, M. J., Kopell, N. J., Traub, R. D., & Whittington, M. A. (2017). Aberrant network activity in schizophrenia. Trends in Neurosciences, 40(6), 371–382. https://doi.org/10.1016/j.tins.2017.04.003.
Klimesch, W., Schack, B., Schabus, M., Doppelmayr, M., Gruber, W., & Sauseng, P. (2004). Phase-locked alpha and theta oscillations generate the P1-N1 complex and are related to memory performance. Brain Research. Cognitive Brain Research, 19(3), 302–16. https://doi.org/10.1016/j.cogbrainres.2003.11.016.
Lisman, J. E., & Jensen, O. (2013). The θ-γ neural code. Neuron, 77(6), 1002–16. https://doi.org/10.1016/j.neuron.2013.03.007
Marconi, P. L., Pessa, E., Penna, M. P., & Tambelli, R. (2016). The phase amplitude coupling to assess brain network system integration (pp. 1–6). https://doi.org/10.1109/MeMeA.2016.7533783.
Marconi, P. L., Pessa, E., & Penna, M. P. (2018). Comparing different methods of measurement in psychiatry and clinical psychology: The diagnostic power of the new neurophysiology techniques. In MeMeA 2018—2018 IEEE International Symposium on Medical Measurements and Applications, Proceedings. Art. 8438667. https://doi.org/10.1109/MeMeA.2018.8438667
Melloni, L., Molina, C., Peña, M., Torres, D., Singer, W., & Rodriguez, E. (2007). Synchronization of neural activity across cortical areas correlates with conscious perception. Journal of Neuroscience, 27(11), 2858–2865.
Modolo, J., Hassan, M., Wendling, F., & Benquet, P. (2020). Decoding the circuitry of consciousness: From local microcircuits to brain-scale networks. Network Neuroscience, 4(2), 315–337. https://doi.org/10.1162/netn_a_00119
Moran, L. V., & Hong, L. E. (2011). High vs low frequency neural oscillations in Schizophrenia. Schizophrenia Bulletin, 37(4), 659–663.
Raichle, M. E. (2011). The restless brain. Brain Connect, 1(1), 3–12. https://doi.org/10.1089/brain.2011.0019. PMID: 22432951; PMCID: PMC3621343.
van Wijk, B. C., Jha, A., Penny, W., & Litvak, V. (2015). Parametric estimation of cross-frequency coupling. Journal of Neuroscience Methods, 243, 94–102. https://doi.org/10.1016/j.jneumeth.2015.01.032. Epub 2015 Feb 9. PMID: 25677405; PMCID: PMC4364621.
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Marconi, P.L., Scognamiglio, R., Mascia, M.L., Conti, R., Marconi, C., Penna, M.P. (2024). A Theoretical Model for EEG Interpretation. In: Minati, G., Pietronilla Penna, M. (eds) Multiple Systems. AIRSNC 2023. Contributions to Management Science. Springer, Cham. https://doi.org/10.1007/978-3-031-44685-6_4
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