Abstract
Birth of neurons in the human brain relocates from their place of birth to other regions of the brain. Designing a model to read the structural changes in the brain will help scientists to understand more about the process of the life cycle of neurons. In this paper, Hypergraph-based model for recognizing the structural changes during the birth and death of neurons was developed and its performance was evaluated quantitatively with small-world network and robust connectivity measures. This neuron reconstruction model will operate as a treatment modality to cure brain diseases and disorders that affect the lives of millions of human being.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Levine, D.S.: Theory of the Brain and Mind: Visions and History. Artif. Intell. Age Neural Networks Brain Comput. 191–203 (2019). https://doi.org/10.1016/B978-0-12-815480-9.00009-8
Einevoll, G.T., Destexhe, A., Diesmann, M., Grün, S., Jirsa, V., de Kamps, M., Migliore, M., Ness, T.V., Plesser, H.E., Schürmann, F.: The Scientific Case for Brain Simulations. Neuron. 102, 735–744 (2019). https://doi.org/10.1016/J.NEURON.2019.03.027
Colombo, M.: Olaf Sporns: Discovering the Human Connectome. Minds Mach. 24, 217–220 (2014). https://doi.org/10.1007/s11023-013-9334-2
van den Heuvel, M.P., Sporns, O.: A cross-disorder connectome landscape of brain dysconnectivity. Nat. Rev. Neurosci. 20, 435–446 (2019). https://doi.org/10.1038/s41583-019-0177-6
Teeter, C., Iyer, R., Menon, V., Gouwens, N., Feng, D., Berg, J., Szafer, A., Cain, N., Zeng, H., Hawrylycz, M., Koch, C., Mihalas, S.: Generalized leaky integrate-and-fire models classify multiple neuron types. Nat. Commun. 9, 709 (2018). https://doi.org/10.1038/s41467-017-02717-4
Sporns, O.: Graph theory methods: applications in brain networks. Dialogues Clin. Neurosci. 20, 111–121 (2018)
Lippert, T., Thomas: HPC for the human brain project. In: Proceedings of the 28th ACM international conference on Supercomputing - ICS ’14. pp. 1–1. ACM Press, New York, New York, USA (2014). https://doi.org/10.1145/2597652.2616584
Yadati, N., Nitin, V., Nimishakavi, M., Yadav, P., Louis, A., Talukdar, P.: Link Prediction in Hypergraphs using Graph Convolutional Networks (2018)
Bhalla, S., Dura-Bernal, S., Suter, B.A., Gleeson, P., Cantarelli, M., Quintana, A., Rodriguez, F., Kedziora, D.J., Chadderdon, G.L., Kerr, C.C., Neymotin, S.A., McDougal, R.A., Hines, M., Shepherd, G.M., Lytton, W.W.: NetPyNE, a tool for data-driven multiscale modeling of brain circuits. https://doi.org/10.7554/eLife.44494.001
Biamonte, J., Faccin, M., De Domenico, M.: Complex networks from classical to quantum. Commun. Phys. 2, 53 (2019). https://doi.org/10.1038/s42005-019-0152-6
Fleischer, V., Radetz, A., Ciolac, D., Muthuraman, M., Gonzalez-Escamilla, G., Zipp, F., Groppa, S.: Graph Theoretical Framework of Brain Networks in Multiple Sclerosis: A Review of Concepts. Neuroscience 403, 35–53 (2019). https://doi.org/10.1016/j.neuroscience.2017.10.033
Bullmore, E., Sporns, O.: Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 2009 103. 10, 186–198 (2009). https://doi.org/10.1038/nrn2575
Lee, H., Kim, E., Ha, S., Kang, H., Huh, Y., Lee, Y., Lim, S., Lee, D.S.: Volume entropy for modeling information flow in a brain graph. Sci. Rep. 9, 256 (2019). https://doi.org/10.1038/s41598-018-36339-7
Stam, C.J.: Modern network science of neurological disorders. Nat. Rev. Neurosci. 15, 683–695 (2014). https://doi.org/10.1038/nrn3801
Bansal, K., Nakuci, J., Muldoon, S.F.: Personalized brain network models for assessing structure-function relationships. Curr. Opin. Neurobiol. 52, 42–47 (2018). https://doi.org/10.1016/J.CONB.2018.04.014
Lynn, C.W., Bassett, D.S.: The physics of brain network structure, function and control. Nat. Rev. Phys. 1, 318–332 (2019). https://doi.org/10.1038/s42254-019-0040-8
Shalini, R., Mohan, R.: Drugs Relationship Discovery using Hypergraph. Int. J. Inf. Technol. Comput. Sci. 10, 54–63 (2018). https://doi.org/10.5815/ijitcs.2018.06.06
Mohan R Shalini R: Neuroinformatics Conference, https://abstracts.g-node.org/conference/NI2018/abstracts#/uuid/340cca06-1ea0-42bc-9a37-04f07828da89
Shalini R, Mohan R: Diagnosis of Alzheimer’s disease using Hypergraph. In: G-Node (2018). https://doi.org/10.12751/incf.ni2018.0098
Ritz, A., Avent, B., Murali, T.M.: Pathway Analysis with Signaling Hypergraphs. IEEE/ACM Trans. Comput. Biol. Bioinforma. 14, 1042–1055 (2017). https://doi.org/10.1109/TCBB.2015.2459681
Wei, K., Cieslak, M., Greene, C., Grafton, S.T., Carlson, J.M.: Sensitivity analysis of human brain structural network construction. Netw. Neurosci. 1, 446–467 (2017)
Mertz, A., Slough, W.: Graphics with TikZ. Pr, E X J (2007)
Berge, C.: Hypergraph-Combinatorics of finite sets. North Holland (1989)
Bretto, A. : Hypergraph Theory : An Introduction. Springer, cham; New York (2013)
Weisstein, E.W.: Incidence Matrix, http://mathworld.wolfram.com/IncidenceMatrix.html
Mueller, S.G., Weiner, M.W., Thal, L.J., Petersen, R.C., Jack, C.R., Jagust, W., Trojanowski, J.Q., Toga, A.W., Beckett, L.: Ways toward an early diagnosis in Alzheimer’s disease: the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Alzheimers. Dement. 1, 55–66 (2005). https://doi.org/10.1016/j.jalz.2005.06.003
Naresh, Korrapati: Alzheimer’s Disease and Memory Loss - A Review. (2016). https://doi.org/10.4172/2161-0460.1000259
Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature. 393, 440–442 (1998). https://doi.org/10.1038/30918
Liu, J., Zhou, M., Wang, S., Liu, P.: A comparative study of network robustness measures. Front. Comput. Sci. 11, 568–584 (2017). https://doi.org/10.1007/s11704-016-6108-z
Golas, U.: Analysis and Correctness of Algebraic Graph and Model Transformations. Vieweg+Teubner, Wiesbaden (2011). https://doi.org/10.1007/978-3-8348-9934-7
Yoo, H.-J.: 1.2 Intelligence on Silicon: From Deep-Neural-Network Accelerators to Brain Mimicking AI-SoCs. In: 2019 IEEE International Solid- State Circuits Conference—(ISSCC). pp. 20–26. IEEE (2019). https://doi.org/10.1109/ISSCC.2019.8662469
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ramanathan, S., Ramasundaram, M. (2021). Multilevel Neuron Model Construction Related to Structural Brain Changes Using Hypergraph. In: Panigrahi, C.R., Pati, B., Mohapatra, P., Buyya, R., Li, KC. (eds) Progress in Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 1199. Springer, Singapore. https://doi.org/10.1007/978-981-15-6353-9_19
Download citation
DOI: https://doi.org/10.1007/978-981-15-6353-9_19
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-6352-2
Online ISBN: 978-981-15-6353-9
eBook Packages: EngineeringEngineering (R0)