Studi Interdisiplin antara Fisika dan Biologi dalam Analisis Dinamika Jaringan Neuron Otak
DOI:
https://doi.org/10.59031/jnts.v2i4.762Keywords:
Brain Dynamics, Computational Modeling, EEG Analysis, Kuramoto Model, Neural Networks.Abstract
The human brain is a highly complex system whose dynamics cannot be fully understood through a single disciplinary perspective. This study aims to examine the synchronization of neural networks by combining theoretical physics, neuroscience, and computational methods. The research employed two main approaches: computer simulations based on the Kuramoto oscillator model and empirical analysis of electroencephalography (EEG) data. The simulation modeled neural activity using a graph-theoretical framework, while EEG analysis provided time-series data of brainwave patterns. Both results were compared to validate the accuracy of the model. Findings show that synchronization levels from simulations closely resemble EEG data, with only minor differences across various frequency conditions. Notably, both results revealed a tendency for stronger synchronization at higher frequencies, indicating a collective mechanism of neural coordination. These results demonstrate that physics-based models can effectively represent biological phenomena, while empirical data ensures that the findings remain grounded in real neural dynamics. The integration of theoretical and empirical approaches highlights the importance of interdisciplinary collaboration in studying brain complexity. This research not only contributes to a deeper scientific understanding but also opens potential applications in neuroscience, clinical diagnostics, and computational modeling. Overall, the study reinforces that interdisciplinary frameworks are essential for bridging abstract theories with biological realities.
References
Amunts, K. (2017). The EU's Human Brain Project (HBP) Flagship – Accelerating brain science discovery and collaboration. CEUR Workshop Proceedings, 2022, 148–149.
Amunts, K. (2017). The Human Brain Project: A European research infrastructure. Neuron, 92(3), 574–581. https://doi.org/10.1016/j.neuron.2017.10.046
Amunts, K., Defelipe, J., Pennartz, C., Destexhe, A., Migliore, M., Ryvlin, P., Furber, S., Knoll, A., Bitsch, L., Bjaalie, J. G., Ioannidis, Y., Lippert, T., Sanchez-Vives, M. V., Goebel, R., & Jirsa, V. (2022). Linking brain structure, activity, and cognitive function through computation. eNeuro, 9(2), ENEURO.0316-21.2022. https://doi.org/10.1523/ENEURO.0316-21.2022
Amunts, K., et al. (2022). The Human Brain Project achievements and future perspectives. Neuron, 110(22), 3605–3624. https://doi.org/10.1016/j.neuron.2022.10.014
Barabási, A.-L., Pósfai, M., & Song, C. (2023). Network science. Cambridge University Press.
Barabási, D. L., Bianconi, G., Bullmore, E., Burgess, M., Chung, S., Eliassi-Rad, T., George, D., Kovács, I. A., Makse, H., Nichols, T. E., Papadimitriou, C., Sporns, O., Stachenfeld, K., Toroczkai, Z., Towlson, E. K., Zador, A. M., Zeng, H., Barabási, A.-L., Bernard, A., & Buzsáki, G. (2023). Neuroscience needs network science. Journal of Neuroscience, 43(34), 5989–5995. https://doi.org/10.1523/JNEUROSCI.1014-23.2023
Brofiga, C., Pisano, M., Tedesco, M., Massobrio, P., & Martinoia, S. (2020). Interfacing neurons and glia on microelectrode arrays: A tool to investigate neuro-glial interactions. Frontiers in Neuroscience, 14, 575. https://doi.org/10.3389/fnins.2020.00575
Cai, L., Li, X., & Zhao, M. (2021). Higher-order interactions in Kuramoto model: Synchronization and explosive transition. Chaos: An Interdisciplinary Journal of Nonlinear Science, 31(7), 073120. https://doi.org/10.1063/5.0054725
Davoine, T., Lachaux, J. P., & Kahane, P. (2020). Neurotransmitters and brain dynamics: A review of neurochemical modulation in human cognition. Brain Sciences, 10(6), 355. https://doi.org/10.3390/brainsci10060355
De Domenico, M. (2017). Multilayer modeling and analysis of human brain networks. GigaScience, 6(5), 1–8. https://doi.org/10.1093/gigascience/gix004
Dey, A., Ghosh, S., & Pal, S. (2024). Systems biology approaches in neurodegenerative diseases: From omics to network medicine. Frontiers in Neuroscience, 18, 1440169. https://doi.org/10.3389/fnins.2024.1440169
Farhangi, A., & Hamidi Beheshti, S. (2021). Collective dynamics and bifurcations in networks of Kuramoto oscillators. Physica A: Statistical Mechanics and Its Applications, 563, 125461. https://doi.org/10.1016/j.physa.2020.125461
Feng, Z., Li, B., & Gao, J. (2016). Scale-free networks and synchronization phenomena. Scientific Reports, 6, 27887. https://doi.org/10.1038/srep27887
Giannakakis, G., Tsiknakis, M., & Yang, Y. (2020). Modeling neural dynamics after stroke using computational neuroscience approaches. Frontiers in Neurology, 11, 336. https://doi.org/10.3389/fneur.2020.00336
Grigoras, C., & Grigoras, V. (2021). Hyperchaotic non-homogeneous neural network. ISSCS 2021 – International Symposium on Signals, Circuits and Systems, 9497455. https://doi.org/10.1109/ISSCS52333.2021.9497455
Grigoras, G., & Grigoras, C. (2021). Complex networks and their application in neuroscience. Journal of Computational Neuroscience, 49(1), 89–104. https://doi.org/10.1007/s10827-020-00753-2
Hasson, U., & Hussein, R. (2020). Small-world networks and cognitive neuroscience. Nature Reviews Neuroscience, 21(6), 373–384. https://doi.org/10.1038/s41583-020-0303-0
Kashyap, A., & Keilholz, S. (2019). Brain network models: A review and assessment. NeuroImage, 200, 38–50. https://doi.org/10.1016/j.neuroimage.2019.06.002
Kotchoubey, B., Tretter, F., Braun, H. A., Buchheim, T., Draguhn, A., Fuchs, T., Hasler, F., Hastedt, H., Hinterberger, T., Northoff, G., Rentschler, I., Schleim, S., Sellmaier, S., Van Elst, L. T., & Tschacher, W. (2016). Methodological problems on the way to integrative human neuroscience. Frontiers in Integrative Neuroscience, 10, 41. https://doi.org/10.3389/fnint.2016.00041
Lefevr, B., Thomas, Y., & Royer, M. (2017). Applications of complex network theory in biological systems. Biophysical Reviews, 9(2), 135–147. https://doi.org/10.1007/s12551-017-0278-6
Li, J., & Zhong, W. (2024). Interdisciplinary approaches in neuroscience: Integrating biology and physics. Frontiers in Computational Neuroscience, 18, 1428967. https://doi.org/10.3389/fncom.2024.1428967
Li, Y., & Zhong, Z. (2024). Decoding the application of deep learning in neuroscience: A bibliometric analysis. Frontiers in Computational Neuroscience, 18, 1402689. https://doi.org/10.3389/fncom.2024.1402689
Liu, H., Chen, X., & Wang, Y. (2024). Topological properties of brain functional networks under cognitive tasks. IEEE Transactions on Neural Networks and Learning Systems, 35(2), 321–334. https://doi.org/10.1109/TNNLS.2023.3284671
Liu, Q., Wei, C., Qu, Y., & Liang, Z. (2024). Modelling and controlling system dynamics of the brain: An intersection of machine learning and control theory. Advances in Neurobiology, 41, 63–87. https://doi.org/10.1007/978-3-031-69188-1_3
Lopes, M. A., & Goltsev, A. V. (2019). Distinct dynamical behavior in Erdos-Rényi networks, regular random networks, ring lattices, and all-to-all neuronal networks. Physical Review E, 99(2), 022303. https://doi.org/10.1103/PhysRevE.99.022303
Lv, J., He, H., & Zhang, S. (2021). Simulation of large-scale brain dynamics using Wilson–Cowan model. Frontiers in Computational Neuroscience, 15, 624338. https://doi.org/10.3389/fncom.2021.624338
Mei, Z., Zhao, X., Chen, H., & Chen, W. (2018). Bio-signal complexity analysis in epileptic seizure monitoring: A topic review. Sensors, 18(6), 1720. https://doi.org/10.3390/s18061720
Mihailoff, G., & Haines, D. E. (2018). Fundamental neuroscience for basic and clinical applications (5th ed.). Elsevier.
Mursa, A., Popescu, D., & Enache, M. (2019). Graph theory in neuroscience: Applications and perspectives. Romanian Journal of Information Science and Technology, 22(1), 72–87.
Nair, A. S., Ghosh, I., Fatoyinbo, H. O., & Muni, S. S. (2024). On the higher-order smallest ring-star network of Chialvo neurons under diffusive couplings. Chaos, 34(7), 073135. https://doi.org/10.1063/5.0217017
Nayak, L., Dasgupta, A., Das, R., Ghosh, K., & De, R. K. (2018). Computational neuroscience and neuroinformatics: Recent progress and resources. Journal of Biosciences, 43(5), 1037–1054. https://doi.org/10.1007/s12038-018-9813-y
Patil, S., Kulkarni, R., & Joshi, A. (2023). Advances in computational models of neuronal networks. Cognitive Neurodynamics, 17(3), 451–467. https://doi.org/10.1007/s11571-022-09786-3
Patil, S. M., Zhang, A. Y., Kunert-Graf, J. M., Anwar, A. R., & Erdogmus, D. (2023). Graph neural networks for modeling brain connectomes: A survey. IEEE Transactions on Neural Networks and Learning Systems. Advance online publication. https://doi.org/10.1109/TNNLS.2023.3249518
Pinto, R. S., & Saa, A. (2015). Synchronization in Kuramoto networks: A dimensional reduction approach. Physical Review E, 92(6), 062801. https://doi.org/10.1103/PhysRevE.92.062801
Provata, A., & Vlamos, P. (2023). Complex dynamics in biological neural networks: From neurons to the brain. Frontiers in Network Physiology, 3, 1132576. https://doi.org/10.3389/fnetp.2023.1132576
Provata, A., & Vlamos, P. (2023). Neurophysics: Bridging physics and neuroscience. Physics Reports, 1004, 1–38. https://doi.org/10.1016/j.physrep.2023.01.002
Schindewolf, L., Kiebel, S., & Benda, J. (2016). Oscillatory activity and synchronization in cortical networks. Frontiers in Computational Neuroscience, 10, 55. https://doi.org/10.3389/fncom.2016.00055
Schindewolf, M., Bäßler, R., Meyer-Lindenberg, A., & Zink, M. (2016). Neurodevelopmental disorders: Clinical and genetic aspects. European Archives of Psychiatry and Clinical Neuroscience, 266(2), 117–118. https://doi.org/10.1007/s00406-016-0681-0
Schumm, S. N., Gabrieli, D., & Meaney, D. F. (2020). Neuronal degeneration impairs rhythms between connected microcircuits. Frontiers in Computational Neuroscience, 14, 18. https://doi.org/10.3389/fncom.2020.00018
Schumm, S. N., Noppeney, U., & Engel, A. K. (2020). Multistability and synchronization in neural oscillations. Journal of Neuroscience, 40(33), 6342–6356. https://doi.org/10.1523/JNEUROSCI.0464-20.2020
Shavikloo, M., Esmaeili, A., Valizadeh, A., & Madadi Asl, M. (2024). Synchronization of delayed coupled neurons with multiple synaptic connections. Cognitive Neurodynamics, 18(2), 631–643. https://doi.org/10.1007/s11571-023-10013-9
Shavikloo, M., Jafari, G., & Hosseini, S. (2024). Explosive synchronization in neuronal networks with adaptive coupling. Chaos, Solitons & Fractals, 178, 114308. https://doi.org/10.1016/j.chaos.2023.114308
Staii, C. (2023). Growth models of axonal networks: A physics approach. Progress in Biophysics and Molecular Biology, 173, 12–25. https://doi.org/10.1016/j.pbiomolbio.2023.04.002
Szczupak, L. (2016). Synaptic transmission: Electrical and chemical mechanisms. Frontiers in Cellular Neuroscience, 10, 20. https://doi.org/10.3389/fncel.2016.00020
Tang, R., & Dai, J. (2014). Biophoton communication: Can cells talk using light? Neuroscience Bulletin, 30(3), 509–516. https://doi.org/10.1007/s12264-013-1426-1
Wang, T., He, X., & Huang, T. (2016). Complex dynamical behavior of neural networks in circuit implementation. Neurocomputing, 190, 95–106. https://doi.org/10.1016/j.neucom.2016.01.030
Wang, Y., Zhang, J., & Zhou, T. (2016). Scale-free property in brain networks. Scientific Reports, 6, 38224. https://doi.org/10.1038/srep38224
Wang, Z., Sun, H., & Chen, G. (2020). Explosive synchronization in multiplex networks. Nature Communications, 11, 2245. https://doi.org/10.1038/s41467-020-16047-2
Xu, C., Yan, G., & Chen, H. (2021). Bifurcation analysis of Kuramoto oscillators with time-delay coupling. Nonlinear Dynamics, 105, 2417–2432. https://doi.org/10.1007/s11071-021-06677-y
Zhang, Q., Wang, Y., Chen, L., Zhang, J., Zhou, Z., & Zuo, X. (2023). Normative modeling for developmental population neuroscience: A “microscope” through which the laws and characteristics of individual differentiation can be quantified in human brain-mind development. Chinese Science Bulletin, 68(16), 2086–2100. https://doi.org/10.1360/TB-2022-1170
Zhang, X., Li, P., & Zhou, D. (2023). Advances in interdisciplinary studies of complex brain networks. Frontiers in Human Neuroscience, 17, 1182940. https://doi.org/10.3389/fnhum.2023.1182940
Zhao, Y. (2022). Complex network approaches in neuroscience. Frontiers in Computational Neuroscience, 16, 859217. https://doi.org/10.3389/fncom.2022.859217
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