Modeling Neurodegeneration and Regeneration in Parkinson's Disease

Authors

  • Charley Wan Student

DOI:

https://doi.org/10.5195/pur.2024.48

Keywords:

Neurodegeneration, Dopamine, Connectome, Graph theory, Small-World, Hegselmann-Krause, Parkinson's disease

Abstract

The diagnosis and prevention of neurodegenerative diseases is a heavily examined topic in the neuroscience discipline. Whether it be from the anatomical and biological perspectives or the psychological and sociological perspectives, the ultimate goal is to discover strategies to pinpoint the infection as soon as possible. This article begins with reviewing the small-world network structure and then combines the sociological and anatomical perspectives to explain the progression of neuronal death within the brain by using rsfMRI data and the Hegselmann-Krause Model of Opinion Dynamics to illustrate critical interactions between brain regions, and to predict the ultimate behavior of the neural network after initial degeneration

Following experimentation–in which critical regions related to Parkinson’s Disease were studied–thresholds were identified in specific regions which exhibited consistent converging behavior of the neural network toward either degenerative or regenerative directions. Furthermore, a simple graphical model is proposed to demonstrate the ranges of values in which current brain health could be of concern. We concluded that neuron death in one brain region can lead to further infection in the resulting system; however, some regions can also directly/indirectly compensate for the system’s decreased function. In future research endeavors, this could provide insight into developing more accurate predictive models for the goal of early detection of a diseased brain and its recovery.

References

Brunelli, F., Valente, E. M., & Arena, G. (2020). Mechanisms of Neurodegeneration in Parkinson’s Disease: Keep Neurons in the PINK1. Mechanisms of Ageing and Development, 189, 111277. https://doi.org/10.1016/j.mad.2020.111277

Chou, S.-C., Aggarwal, A., Dawson, V. L., Dawson, T. M., & Kam, T.-I. (2021). Recent Advances in Preventing Neurodegenerative Diseases. Faculty Reviews, 10. https://doi.org/10.12703/r/10-81

Wareham, L. K., Liddelow, S. A., Temple, S., Benowitz, L. I., Di Polo, A., Wellington, C., Goldberg, J. L., He, Z., Duan, X., Bu, G., Davis, A. A., Shekhar, K., Torre, A. L., Chan, D. C., Canto-Soler, M. V., Flanagan, J. G., Subramanian, P., Rossi, S., Brunner, T., … Calkins, D. J. (2022). Solving Neurodegeneration: Common Mechanisms and Strategies for New Treatments. Molecular Neurodegeneration, 17(1), 23. https://doi.org/10.1186/s13024-022-00524-0

Fox, M. D. (2018). Mapping Symptoms to Brain Networks with the Human Connectome. New England Journal of Medicine, 379(23), 2237–2245. https://doi.org/10.1056/NEJMra1706158

Griffa, A., Baumann, P. S., Thiran, J.-P., & Hagmann, P. (2013). Structural Connectomics in Brain Diseases. NeuroImage, 80, 515–526. https://doi.org/10.1016/j.neuroimage.2013.04.056

Sporns, O. (2016). Connectome Networks: From Cells to Systems. In H. Kennedy, D. C. Van Essen, & Y. Christen (Eds.), Micro-, Meso- and Macro-Connectomics of the Brain. Springer. http://www.ncbi.nlm.nih.gov/books/NBK435773/

Fortunato, S. (2005). On the Consensus Threshold for the Opinion Dynamics of Krause–Hegselmann. International Journal of Modern Physics C, 16(02), 259–270. https://doi.org/10.1142/S0129183105007078

Sîrbu, A., Loreto, V., Servedio, V. D. P., & Tria, F. (2017). Opinion Dynamics: Models, Extensions and External Effects (pp. 363–401). http://arxiv.org/abs/1605.06326

Galantucci, S., Agosta, F., Stefanova, E., Basaia, S., van den Heuvel, M. P., Stojković, T., Canu, E., Stanković, I., Spica, V., Copetti, M., Gagliardi, D., Kostić, V. S., & Filippi, M. (2017). Structural Brain Connectome and Cognitive Impairment in Parkinson Disease. Radiology, 283(2), 515–525. https://doi.org/10.1148/radiol.2016160274

Kurz, S., & Rambau, J. (2014). On the Hegselmann-Krause Conjecture in Opinion Dynamics. https://doi.org/10.48550/ARXIV.1401.4383

Ódor, G., & Hartmann, B. (2020). Power-Law Distributions of Dynamic Cascade Failures in Power-Grid Models. Entropy, 22(6), 666. https://doi.org/10.3390/e22060666

Watts, D. J., & Strogatz, S. H. (1998). Collective Dynamics of ‘small-World’ Networks. Nature, 393(6684), 440–442. https://doi.org/10.1038/30918

Freeman, L. C. (1978). Centrality in Social Networks Conceptual Clarification. Social Networks, 1(3), 215–239. https://doi.org/10.1016/0378-8733(78)90021-7

Caron, F., & Fox, E. B. (2017). Sparse Graphs Using Exchangeable Random Measures. Journal of the Royal Statistical Society Series B: Statistical Methodology, 79(5), 1295–1366. https://doi.org/10.1111/rssb.12233

Kalcher, K., Huf, W., Boubela, R., Filzmoser, P., Pezawas, L., Biswal, B., Kasper, S., Moser, E., & Windischberger, C. (2012). Fully Exploratory Network Independent Component Analysis of the 1000 Functional Connectomes Database. Frontiers in Human Neuroscience, 6. https://www.frontiersin.org/articles/10.3389/fnhum.2012.00301

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Published

2024-04-12

How to Cite

Wan, C. (2024). Modeling Neurodegeneration and Regeneration in Parkinson’s Disease. Pittsburgh Undergraduate Review, 3(1). https://doi.org/10.5195/pur.2024.48

Issue

Vol. 3 No. 1 (2024): Pittsburgh Undergraduate Review

Section

Research

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Copyright (c) 2024 Charley Wan

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