Respiratory Neural Network: Activity and Connectivity
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).
KeywordsRespiration Neural network Pre-Bötzinger complex Synchronization
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