A Joint Computational Respiratory Neural Network-Biomechanical Model for Breathing and Airway Defensive Behaviors

Data-driven computational neural network models have been used to study mechanisms for generating the motor patterns for breathing and breathing related behaviors such as coughing. These models have commonly been evaluated in open loop conditions or with feedback of lung volume simply represented as a filtered version of phrenic motor output. Limitations of these approaches preclude assessment of the influence of mechanical properties of the musculoskeletal system and motivated development of a biomechanical model of the respiratory muscles, airway, and lungs using published measures from human subjects. Here we describe the model and some aspects of its behavior when linked to a computational brainstem respiratory network model for breathing and airway defensive behavior composed of discrete “integrate and fire” populations. The network incorporated multiple circuit paths and operations for tuning inspiratory drive suggested by prior work. Results from neuromechanical system simulations included generation of a eupneic-like breathing pattern and the observation that increased respiratory drive and operating volume result in higher peak flow rates during cough, even when the expiratory drive is unchanged, or when the expiratory abdominal pressure is unchanged. Sequential elimination of the model’s sources of inspiratory drive during cough also suggested a role for disinhibitory regulation via tonic expiratory neurons, a result that was subsequently supported by an analysis of in vivo data. Comparisons with antecedent models, discrepancies with experimental results, and some model limitations are noted.