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Graph Model Simulation of Human Brain’s Functional Activity at Resting State by Means of the FD Model

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
It is commonly accepted that the various parts of the human brain interact as a network at macroscopic, mesoscopic and microscopic level. Recently, different network models have been proposed to mime the brain behavior both at resting state and during tasks: Our study concerns one of those model that consider both the physical and functional connectivity as well as topological metrics of the brain networks. We provide evidence of the soundness of the model by means of a synthetic dataset based on the existing literature concerning the active cerebral areas at the resting state. Furthermore, we consider Ruzicka similarity measure in order to stress the predictive capability of the model and provide a thresholding criterium. Some network statistics are finally provided.
Wydawca
Rocznik
Strony
93--109
Opis fizyczny
Bibliogr. 19 poz., rys., tab., wykr.
Twórcy
autor
  • Dipartimento di Matematica, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
autor
  • Dipartimento di Matematica, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
autor
  • Dipartimento di Matematica, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
autor
  • Dipartimento di Matematica, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
autor
  • imec-Vision lab, Department of Physics, University of Antwerp, Universiteitsplein 1, 2610 Antwerp, Belgium
Bibliografia
  • [1] Achard S, Salvador R, Whitcher B, Suckling J, Bullmore E. A Resilient, Low-Frequency, Small-World Human Brain Functional Network with Highly Connected Association Cortical Hubs, The Journal of Neuroscience, 2016;26(1):63-72. doi: 10.1371/journal.pone.0044428.
  • [2] Alexander-Bloch AF, Gogtay N, Meunier D, Birn R, Clasen L, Lalonde F, Lenroot R, Giedd J, Bullmore ET. Disrupted modularity and local connectivity of brain functional networks in childhood-onset schizophrenia. Frontiers in systems neuroscience, 2010;4(147). doi: 10.3389/fnsys.2010.00147.
  • [3] Betzel RF, Avena-Koenigsberger A, Goñi J, He Y, de Reus MA, Griffa A, Vèrtes PE, Mišic B, Thiran JP, Hagmann P, van den Heuvel M, Zuo XN, Bullmore ET, Sporns O. Generative models of the human connectome. Neuroimage, 2016;124(Pt A):1054-1064. doi:10.1016/j.neuroimage.2015.09.041.
  • [4] Bordier C, Nicolini C, Bifone A. Graph Analysis and Modularity of Brain Functional Connectivity Networks: Searching for the Optimal Threshold. Frontiers in Neuroscience 2017;11(441). doi:10.3389/fnins.2017.00441.
  • [5] Buckner RL, Andrews-Hanna JR, Schacter DL. The Brain’s Default Network: Anatomy, Function, and Relevance to Disease. Annals of the New York Academy of Sciences, 2008;1124:1-38. doi:10.1196/annals.1440.011.
  • [6] Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 2009;10(3):186-198. doi:10.1038/nrn2575.
  • [7] Cha SH. Comprehensive Survey on Distance/Similarity Measures between Probability Density Functions International Journal of Mathematical Models and Methods in Applied sciences, 2007;4(1). doi:10.1.1.154.8446.
  • [8] Deza MM, and Deza E. Encyclopedia of Distances. Springer-Verlag Berlin Heidelberg, 2010. doi:10.1007/978-3-662-44342-2. 4th Edition.
  • [9] Finotelli P, Dulio P. A mathematical model for evaluating the functional connectivity strongness in healthy people. Archives Italiennes de Biologie, 2015;153(4):279-300. doi:10.12871/00039829201544.
  • [10] Finotelli P, Dulio P, Varotto G, Rotondi F, Panzica F. A statistical proposal for selecting a data-depending threshold in neurobiology. Archives Italiennes de Biologie, 2016;154(2-3):78-101. doi:10.12871/00039829201625.
  • [11] Latora V, Marchiori M. Efficient Behavior of Small-World Networks. Physical Review Letters 2001;87(19)198701:1-4. doi:10.1103/PhysRevLett.87.198701.
  • [12] Passingham R. How good is the macaque monkey model of the human brain? Current Opinion in Neurobiology, 2009;19(1):6-11. doi:10.1016/j.conb.2009.01.002.
  • [13] Manjeet Singh J. Relevancy Measurement of Retrieved Webpages Using Ruzicka Similarity Measure. International Journal of Engineering Research & Technology, 2014;3(7):1520-1523. ISSN:2278-0181.
  • [14] Moussa MN, Steen MR, Laurienti PJ, Hayasaka S. Consistency of Network Modules in Resting-State fMRI Connectome Data. PLoS ONE 2012;7(8), doi:10.1371/journal.pone.0044428.
  • [15] Obando C, de Vico Fallani F. Graph Models of Brain Connectivity Networks. In: Annual Meeting of the Organization for Human Brain Mapping, 2016. URL https://hal.inria.fr/hal-01564958.
  • [16] Sporns O. Structure and function of complex brain networks. Dialogues in Clinical Neuroscience, 2013;15(3):247-262. PMCID:PMC3811098.
  • [17] Vèrtes PE, Alexander-Bloch AF, Gogtay N, Gieddb JN, Rapoport JL, Bullmorea ET. Simple models of human brain functional networks. In: Proceedings of the National Academy of Sciences of the United, 2012;109(15):5868-5873. doi:10.1073/pnas.1111738109.
  • [18] Xiao W, Guanrong C. Complex networks: Small-world, scale-free and beyond. Circuits and Systems Magazine, IEEE, 2003;3(1):6-20. doi:10.1109/MCAS.2003.1228503.
  • [19] Watts DJ, Strogatz SH. Collective dynamics of ’small-world’ networks. Nature 1998;393: 440-442. doi:10.1038/30918.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-9afa8c30-29a2-4717-a21f-12388878a975
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