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      Balance-energy of resting state network in obsessive-compulsive disorder

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          Abstract

          Stability of the brain functional network is directly linked to organization of synchronous and anti-synchronous activities. Nevertheless, impact of arrangement of positive and negative links called links topology requires to be well understood. In this study, we investigated how topology of the functional links reduce balance-energy of the brain network in obsessive-compulsive disorder (OCD) and push the network to a more stable state as compared to healthy controls. Therefore, functional associations between the regions were measured using the phase synchrony between the EEG activities. Subsequently, balance-energy of the brain functional network was estimated based on the quality of triadic interactions. Occurrence rates of four different types of triadic interactions including weak and strong balanced, and unbalanced interactions were compared. In addition, impact of the links topology was also investigated by looking at the tendency of positive and negative links to making hubs. Our results showed although the number of positive and negative links were not statistically different between OCD and healthy controls, but positive links in OCDs’ brain networks have more tendency to make hub. Moreover, lower number of unbalanced triads and higher number of strongly balanced triad reduced the balance-energy in OCDs’ brain networks that conceptually has less requirement to change. We hope these findings could shed a light on better understanding of brain functional network in OCD.

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          Collective dynamics of 'small-world' networks.

          Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks 'rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them 'small-world' networks, by analogy with the small-world phenomenon (popularly known as six degrees of separation. The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.
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            Complex brain networks: graph theoretical analysis of structural and functional systems.

            Recent developments in the quantitative analysis of complex networks, based largely on graph theory, have been rapidly translated to studies of brain network organization. The brain's structural and functional systems have features of complex networks--such as small-world topology, highly connected hubs and modularity--both at the whole-brain scale of human neuroimaging and at a cellular scale in non-human animals. In this article, we review studies investigating complex brain networks in diverse experimental modalities (including structural and functional MRI, diffusion tensor imaging, magnetoencephalography and electroencephalography in humans) and provide an accessible introduction to the basic principles of graph theory. We also highlight some of the technical challenges and key questions to be addressed by future developments in this rapidly moving field.
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              On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other

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                Author and article information

                Contributors
                saeid_yazdiravandi@yahoo.com
                r_khosroabadi@sbu.ac.ir
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                27 June 2023
                27 June 2023
                2023
                : 13
                : 10423
                Affiliations
                [1 ]GRID grid.412266.5, ISNI 0000 0001 1781 3962, Department of Biomedical Engineering, , Tarbiat Modares University, ; Tehran, Iran
                [2 ]GRID grid.411950.8, ISNI 0000 0004 0611 9280, Behavioral Disorders and Substance Abuse Research Center, , Hamadan University of Medical Sciences, ; Hamadan, Iran
                [3 ]GRID grid.464595.f, ISNI 0000 0004 0494 0542, Department of Nursing, College of Basic Science, Hamadan Branch, , Islamic Azad University, ; Hamadan, Iran
                [4 ]GRID grid.411600.2, Behavioral ScienBces Research Center, , Shahid Beheshti University of Medical Sciences, ; Tehran, Iran
                [5 ]GRID grid.411950.8, ISNI 0000 0004 0611 9280, Neurophysiology Research Center, , Hamadan University of Medical Sciences, ; Hamadan, Iran
                [6 ]GRID grid.411950.8, ISNI 0000 0004 0611 9280, Department of Biostatistics, Modeling of Noncommunicable Disease Research Center, School of Public Health, , Hamadan University of Medical Sciences, ; Hamadan, Iran
                [7 ]GRID grid.412502.0, ISNI 0000 0001 0686 4748, Institute for Cognitive and Brain Science, , Shahid Beheshti University, ; Evin Sq., Tehran, 19839-63113 Iran
                [8 ]GRID grid.412345.5, ISNI 0000 0000 9012 9027, Biomedical Engineering Faculty, , Sahand University of Technology, ; Tabriz, Iran
                Author information
                http://orcid.org/0000-0003-0428-383X
                http://orcid.org/0000-0002-6282-9389
                Article
                37304
                10.1038/s41598-023-37304-9
                10300010
                37369689
                d997febd-7e0d-41bd-915e-bd0d40a0b84b
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 1 September 2022
                : 20 June 2023
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                © Springer Nature Limited 2023

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                biomedical engineering,obsessive compulsive disorder,computational neuroscience

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