49
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Progressive glucose stimulation of islet beta cells reveals a transition from segregated to integrated modular functional connectivity patterns

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Collective beta cell activity in islets of Langerhans is critical for the supply of insulin within an organism. Even though individual beta cells are intrinsically heterogeneous, the presence of intercellular coupling mechanisms ensures coordinated activity and a well-regulated exocytosis of insulin. In order to get a detailed insight into the functional organization of the syncytium, we applied advanced analytical tools from the realm of complex network theory to uncover the functional connectivity pattern among cells composing the intact islet. The procedure is based on the determination of correlations between long temporal traces obtained from confocal functional multicellular calcium imaging of beta cells stimulated in a stepwise manner with a range of physiological glucose concentrations. Our results revealed that the extracted connectivity networks are sparse for low glucose concentrations, whereas for higher stimulatory levels they become more densely connected. Most importantly, for all ranges of glucose concentration beta cells within the islets form locally clustered functional sub-compartments, thereby indicating that their collective activity profiles exhibit a modular nature. Moreover, we show that the observed non-linear functional relationship between different network metrics and glucose concentration represents a well-balanced setup that parallels physiological insulin release.

          Related collections

          Most cited references37

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Community structure in social and biological networks

          A number of recent studies have focused on the statistical properties of networked systems such as social networks and the World-Wide Web. Researchers have concentrated particularly on a few properties which seem to be common to many networks: the small-world property, power-law degree distributions, and network transitivity. In this paper, we highlight another property which is found in many networks, the property of community structure, in which network nodes are joined together in tightly-knit groups between which there are only looser connections. We propose a new method for detecting such communities, built around the idea of using centrality indices to find community boundaries. We test our method on computer generated and real-world graphs whose community structure is already known, and find that it detects this known structure with high sensitivity and reliability. We also apply the method to two networks whose community structure is not well-known - a collaboration network and a food web - and find that it detects significant and informative community divisions in both cases.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Dynamic reconfiguration of human brain networks during learning.

            Human learning is a complex phenomenon requiring flexibility to adapt existing brain function and precision in selecting new neurophysiological activities to drive desired behavior. These two attributes--flexibility and selection--must operate over multiple temporal scales as performance of a skill changes from being slow and challenging to being fast and automatic. Such selective adaptability is naturally provided by modular structure, which plays a critical role in evolution, development, and optimal network function. Using functional connectivity measurements of brain activity acquired from initial training through mastery of a simple motor skill, we investigate the role of modularity in human learning by identifying dynamic changes of modular organization spanning multiple temporal scales. Our results indicate that flexibility, which we measure by the allegiance of nodes to modules, in one experimental session predicts the relative amount of learning in a future session. We also develop a general statistical framework for the identification of modular architectures in evolving systems, which is broadly applicable to disciplines where network adaptability is crucial to the understanding of system performance.
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Autism and abnormal development of brain connectivity.

                Bookmark

                Author and article information

                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group
                2045-2322
                19 January 2015
                2015
                : 5
                : 7845
                Affiliations
                [1 ]Faculty of Natural Sciences and Mathematics, University of Maribor , Koroška cesta 160, 2000 Maribor, Slovenia
                [2 ]Institute of Physiology, Faculty of Medicine, University of Maribor , Taborska ulica 8, 2000 Maribor, Slovenia
                [3 ]Centre for Open Innovations and Research, University of Maribor , Slomškov trg 15, 2000 Maribor, Slovenia
                [4 ]Faculty of Education, University of Maribor , Koroška cesta 160, 2000 Maribor, Slovenia
                [5 ]Institute of Physiology, Center for Physiology and Pharmacology, Medical University of Vienna , Schwarzspanierstra βe 17, A-1090 Vienna, Austria
                Author notes
                [*]

                These authors contributed equally to this work.

                Article
                srep07845
                10.1038/srep07845
                4297961
                25598507
                96460588-e5c3-4ce9-8096-471508b840b6
                Copyright © 2015, Macmillan Publishers Limited. All rights reserved

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder in order to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/

                History
                : 25 July 2014
                : 16 December 2014
                Categories
                Article

                Uncategorized
                Uncategorized

                Comments

                Comment on this article