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Respiratory Neural Network: Activity and Connectivity

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Advances in Dynamics, Patterns, Cognition

Part of the book series: Nonlinear Systems and Complexity ((NSCH,volume 20))

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Abstract

Chaos in the rhythmic activity is a major issue that has been discussed in many studies of neuroscience and physiology, and especially in the respiratory air flow. Here, we present the results of two studies concerning the activity and the connectivity of the respiratory neural network in healthy humans and patients with obstructive lung disease. Our results show an increase in the dynamic chaos of airway flow in patients, focusing on expiratory flow. We then sought the reasons for this augmentation in analyzing the activity of neural centers involved in respiratory rhythmogenesis, using functional brain imaging of the automatic neural networks, the first group generating inspiration (pre-Bötzinger complex) and the second in charge of expiration (the parafacial group). Brain imaging reveals in healthy humans a significant activation of the pre-Bötzinger complex linked to a high active inspiration while patients have a higher expiratory parafacial excitability leading to an active expiration. We also propose a theoretical model of respiratory rhythmogenesis which reproduces the synchronized respiratory neural network from two chaotic pacemakers, the first modelling the pre-Bötzinger complex and the second modelling the expiration. Our model reveals how the synchronized chaotic activity of this network reproduced the experimental data of the activity of the respiratory nerve centers both in healthy humans and the patients. We are able to reproduce fMRI signal after hemodynamic convolution of the simulated synchronized neural network. Besides, the respiratory neural network comprises the automatic brainstem and voluntary cortical network. The extension of the study to other important aspects as functional connectivity and Granger causality allow to better understand the communication within the network with the aim to develop new therapeutic strategies involving the modulation of brain oscillation (Hess et al., PLoS One 8:e75740, 2013; Yu et al., Hum. Brain Mapp. 37:2736–2754, 2016).

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Authors and Affiliations

  1. Laboratoire Matière et Systèmes Complexes (MSC), UMR 7057 CNRS et Université Paris 7 - Denis Diderot, Case 7056, Bâtiment Condorcet, 10, rue Alice Domon et Léonie Duquet, 75205, Paris Cedex 13, France

    Laurence Mangin & Maurice Courbage

  2. Physiology Dpt, APHP, Bichat Hospital, Paris 7 University, Paris, France

    Laurence Mangin

Authors
  1. Laurence Mangin
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  2. Maurice Courbage
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Correspondence to Laurence Mangin .

Editor information

Editors and Affiliations

  1. Pennsylvania State University, University Park, Pennsylvania, USA

    Igor S. Aranson

  2. Institute for Physics and Astronomy, University of Potsdam, Potsdam, Germany

    Arkady Pikovsky

  3. BioCircuits Institute, University of California San Diego, La Jolla, California, USA

    Nikolai F. Rulkov

  4. BioCircuits Institute, University of California San Diego, La Jolla, California, USA

    Lev S. Tsimring

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Mangin, L., Courbage, M. (2017). Respiratory Neural Network: Activity and Connectivity. In: Aranson, I., Pikovsky, A., Rulkov, N., Tsimring, L. (eds) Advances in Dynamics, Patterns, Cognition. Nonlinear Systems and Complexity, vol 20. Springer, Cham. https://doi.org/10.1007/978-3-319-53673-6_14

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  • DOI: https://doi.org/10.1007/978-3-319-53673-6_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-53672-9

  • Online ISBN: 978-3-319-53673-6

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