BRAIN CONNECTIONS ANALYSIS USING GRAPH THEORY MEASURES

Oļesja Minejeva; Zigurds Markovics; Nauris Zdanovskis

BRAIN CONNECTIONS ANALYSIS USING GRAPH THEORY MEASURES

Oļesja Minejeva, Zigurds Markovics, Nauris Zdanovskis

Last modified: 04.04.2019

Abstract

Brain is a part of the organism’s complex structure that performs many functions, which are responsible for the main human abilities: to talk, to hear, to move, to see, etc. The brain consists of several areas that are not only directly connected with the different body systems, but also depend and may affect each other. Researchers and doctors are trying to summarize and visualize these relationships for an important purpose – to get the information about possible reactions of the body in case of various diseases, possibilities of recovery, risks, etc. important issues. Neurologists are looking for ways to "move" through the brain in virtual space for viewing the synapses between different areas. It might be useful to get a general idea of how brain regions are interrelated. The term "connectome", which is the complete structural description of the brain connections, or the map of connections, is used for the common perception of brain relationships. Connectome is a network of thousands of nerve fibres that transmits signals between the special regions responsible for functions such as vision, hearing, movement and memory, and combines these functions in a system that perceives, decides and acts as a whole. So, the relationships of brain neural regions can be represented as a graph with vertices corresponding to specific areas, but edges are links between these areas. This graph can be analysed using quantitative measures, like node degree, centrality, modularity etc. This article discusses the different network measures for the connections between brain's regions. The purpose is to determine the most important areas and the role of individual connections in the general functional brain model.

Keywords

Brain network; connectome; functional connectivity; graph theory

References

[1] E. Bullmore and O. Sporns, “Complex brain networks: graph theoretical analysis of structural and functional systems”, Nat. Rev. Neurosci. 10, pp. 186–198, 2009.

[2] J. M. Perkel, “This Is Your Brain: Mapping the Connectome”, LIFE SCIENCE TECHNOLOGIES.

[3] L. Fan, H. Li, J. Zhuo, Y. Zhang, J. Wang, L. Chen, Z. Yang, C. Chu, S. Xie, A. R. Laird, P. T. Fox, S. B. Eickhoff, C. Yu, and T. Jiang, “The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture”, Cereb Cortex. 2016 Aug; 26(8): 3508–3526.

[4] O. Sporns, D. Chialvo, M. Kaiser, and C. C. Hilgetag, “Organization, development and function of complex brain networks”, Trends Cogn. Sci. 8, 418–425 (2004).

[5] K. Börner, S. Sanyal, and A. Vespignani, “Network science”. Annu. Rev. Inform. Sci. Technol. 41, 537–607 (2007).

[6] J. C. Reijneveld, S. C. Ponten, H. W. Berendse, and C. J. Stam, “The application of graph theoretical analysis to complex networks in the brain”, Clin. Neurophysiol. 118, 2317–2331 (2007).

[7] C. J. Stam and J. C. Reijneveld, “Graph theoretical analysis of complex networks in the brain”, Nonlin. Biomed. Phys. 1, 3 (2007).

[8] O. Sporns, G. Tononi, and R. Kotter, “The human connectome: A structural description of the human brain”, PLoS Comput Biol 1(4): e42. 2005.

[9] D. J. Watts, and S. H. Strogatz, “Collective dynamics of 'small-world' networks”, Nature, 393:440-442, 1998.

[10] R. Noldus and P. V. Mieghem, “Assortativity in complex networks”, Journal of Complex Networks, Volume 3, Issue 4, pp. 507–542. 1 December 2015.

[11] S.P. Borgatti and M.G. Everett, “A Graph-theoretic perspective on centrality”, Social Networks. 28 (4), pp. 466-484. 2006.

[12] M. E. Shaw, “Group structure and the behavior of individuals in small groups”, J. Psychology,. V. 38. pp. 139-149.1954.

[13] G. Sabidussi, “The centrality index of a graph”, Psychometrika. 31 (4): 581–603. 1966.

[14] T. Opsahl and P. Panzarasa (2009). "Clustering in Weighted Networks". Social Networks. 31 (2): 155–163.

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