Multi-Scale Models of Neural Mechanisms Controlling Breathing in Mammals

研究包括进一步开发新的神经元神经动力学模型和组成呼吸神经控制系统的网络，这是在啮齿类动物大脑中进行的平行实验研究。所开发的基于数据的模型包括：(1)脑干呼吸神经元的生物物理现实细胞水平计算模型，其中包括有关细胞结构和生物物理特性的当前信息，如神经元活动的离子电导机制；以及(2)脑干呼吸神经网络的大规模模型，其中纳入关于网络功能和结构结构的现有信息。这些建模研究的总体目标是从机制上深入了解哺乳动物呼吸神经控制系统动态运行的微电路和大规模呼吸网络中细胞和电路级别的特性是如何集成的。进一步发展了啮齿动物脑干呼吸中枢模式生成(CPG)网络的新模型，该模型由分布在连续排列的脑干结构隔室中的相互作用的兴奋性和抑制性亚网络组成，每个亚网络在呼吸神经活动模式的产生和控制中具有不同的功能，这些呼吸神经活动模式在正常的吸气呼吸周期和呼气周期中演变。这种CPG模型所用的基本网络结构和细胞特性来自于在大鼠脑干-脊髓的原位电生理和神经解剖学重建研究，以及在活体脑干切片制备的具有活性回路的体外分离的子网络上的研究。这些模型还首次通过模拟传入输入信号来整合不同电路组件的调节，包括来自关键神经调节控制系统的节律性活跃输入，这些系统已知参与呼吸模式的调节。对于CPG网络运行的动力学分析，还应用了动力学系统理论的方法来识别电路运行的关键动力学变量和参数，这些变量和参数是呼吸节律和模式产生的基础，并控制吸气和呼气神经活动功能不同阶段之间的有序转换。微电路和大规模模型的计算机模拟模拟了在不同的体外和现场条件下实验发现的单细胞和神经元群体活动模式的许多特征。从实验研究中得出并用这些模型进一步验证的一个重要的新假说是，呼吸CPG内存在着在多个细胞和网络组织水平上产生振荡活动的能力，形成了一个耦合振荡机制的动力系统。因此，呼吸节律产生的不同机制可以以大脑状态依赖的方式在功能上表达，并构成多种呼吸运动行为的基础，其中一些发生在正常生理条件下，另一些发生在病理生理传导下，如在严重脑缺氧(异常低氧条件)期间。用不同水平的蜂窝和网络复杂性的模型进行的模拟进一步证实了这一新概念的合理性，并提供了对所涉及的基本蜂窝和网络机制的洞察。我们还在包括NIH Biowulf集群在内的大型分布式并行处理系统以及使用图形处理单元(GPU)的桌面超级计算系统上启动了涉及集群计算的模拟方法的实施，这些系统允许实时模拟大规模网络模型。在系统水平上，呼吸神经控制系统的模型已经进一步发展，将基本神经回路动力学与外周氧气和二氧化碳交换、血气运输以及通过血液/大脑氧气和二氧化碳水平等信号进行的中央呼吸回路的生理反馈调节相结合。这些后一种模型代表了第一代系统级控制模型，它结合了神经系统结构-功能特性的基本要素和呼吸系统气体交换和运输系统的现实特征。所有这些模型目前都被应用于进一步探索脑干呼吸回路的工作原理和呼吸活动的控制，包括在与大脑和身体氧/二氧化碳动态平衡紊乱相关的各种(病理)生理条件下。

Research involved the further development of novel neurodynamical models of neurons and networks comprising the respiratory neural control system as studied experimentally in parallel in the rodent brain. Data-based models developed included: (1) biophysically realistic cellular-level computational models of brainstem respiratory neurons incorporating current information on cellular architecture and biophysical properties such as ionic conductance mechanisms underlying neuronal activity; and (2) large-scale models of brainstem respiratory neural networks incorporating available information on network functional and structural architecture. The overall objective of these modeling studies was to gain mechanistic insights into the manner in which cellular- and circuit-level properties are integrated into microcircuits as well as large-scale respiratory networks for dynamical operation of the mammalian respiratory neural control system. A new model of respiratory central pattern generation (CPG) networks in the rodent brainstem was further developed consisting of interacting excitatory and inhibitory subnetworks distributed in serially arranged brainstem structural compartments, each with distinct functional roles in generation and control of the respiratory neural activity patterns that evolve during the normal breathing cycle of inspiration followed by expiration. The basic network architecture and cellular properties used in this CPG model were derived from electrophysiological and neuroanatomical reconstruction studies conducted in the rat brainstem-spinal cord in situ and on subnetworks isolated in living brainstem slice preparations in vitro with active circuits. These models also incorporated for the first time regulation of different circuit components by modeled afferent input signals, including rhythmically active inputs from critical neuromodulatory control systems that are known to be involved in regulation of respiratory pattern generation. For dynamical analysis of CPG network operation, methods from dynamical systems theory were also applied to identify critical dynamical variables and parameters of circuit operation that underlie respiratory rhythm and pattern generation and control the orderly transitions between the functionally distinct phases of inspiratory and expiratory neural activity. Computer simulations with the microcircuit and large-scale models mimicked many features of the single-cell and neuron population activity patterns found experimentally under different in vitro and in situ conditions. A major new hypothesis derived from experimental studies and further tested with these models was that the capability to generate oscillatory activity exists within the respiratory CPG at multiple levels of cellular and network organization, forming a dynamical system of coupled oscillatory mechanisms. Thus different mechanisms of respiratory rhythm generation can be functionally expressed in a brain state-dependent manner and underlie multiple respiratory motor behaviors, some of which occur under normal physiological conditions and others of which emerge under pathophysiological conductions such as during severe brain hypoxia (conditions of abnormally low oxygen). Simulations with models of different levels of cellular and network complexity further confirmed the plausibility of this new concept and have provided insights into the essential cellular and network mechanisms involved. We have also initated implementation of simulation approaches involving cluster computing on large distributed parallel processing systems including the NIH Biowulf cluster as well as desktop supercomputing systems utilizing graphics processing units (GPUs) that allow real-time simulation of large-scale network models. At the system level, models of the respiratory neural control system have been further developed that couple essential neural circuit dynamics with peripheral oxygen and carbon dioxide exchange, blood gas transport, and physiological feedback regulation off central respiratory circuits by signals such as blood/brain levels of oxygen and carbon dioxide. These latter models represent the first generation of system-level control models that integrate essential elements of nervous system structural-functional properties and realistic features of the respiratory gas exchange and transport system. All of these models are currently being applied to further explore principles of operation of brainstem respiratory circuits and control of respiratory activity including under various (patho)physiological conditions associated with disturbances of brain and body oxygen/carbon dioxide homeostasis.