O'Connor Russell, Segers Lauren S, Morris Kendall F, Nuding Sarah C, Pitts Teresa, Bolser Donald C, Davenport Paul W, Lindsey Bruce G
Department of Molecular Pharmacology and Physiology, Morsani College of Medicine, University of South Florida Tampa, FL, USA.
Front Physiol. 2012 Jul 23;3:264. doi: 10.3389/fphys.2012.00264. eCollection 2012.
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.
数据驱动的计算神经网络模型已被用于研究产生呼吸运动模式以及咳嗽等与呼吸相关行为的机制。这些模型通常在开环条件下进行评估，或者仅将肺容积反馈表示为膈神经运动输出的滤波版本。这些方法的局限性妨碍了对肌肉骨骼系统力学特性影响的评估，并促使人们利用来自人类受试者的已发表测量数据，开发呼吸肌、气道和肺的生物力学模型。在这里，我们描述了该模型及其与一个由离散的“积分发放”神经元群体组成的用于呼吸和气道防御行为的计算脑干呼吸网络模型相连时的一些行为方面。该网络纳入了先前工作中提出的用于调节吸气驱动的多条电路路径和操作。神经力学系统模拟的结果包括产生类似正常呼吸的呼吸模式，以及观察到即使呼气驱动不变或呼气腹部压力不变，增加呼吸驱动和操作容积也会导致咳嗽时更高的峰值流速。在咳嗽过程中依次消除模型的吸气驱动源，也表明了通过紧张性呼气神经元进行去抑制调节的作用，这一结果随后得到了体内数据分析的支持。文中还指出了与先前模型的比较、与实验结果的差异以及一些模型的局限性。
