Multi-Scale Models of Neural Mechanisms Controlling Breathing in Mammals

控制哺乳动物呼吸的神经机制的多尺度模型

• 批准号: 8557081
• 负责人: Jeffrey c Smith
• 金额: $ 49.42万
• 依托单位: NATIONAL INSTITUTE OF NEUROLOGICAL DISORDERS AND STROKE
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/8557081
• 关键词: Adult; Architecture; Behavior; Biological Neural Networks; Blood; Brain; Brain Hypoxia; Brain Stem; Breathing; Carbon Dioxide; Cell model; Cells; Computer Architectures; Computer Simulation; Coupled; Couples; Databases; Development; Disease; Elements; Feedback; Gases; Generations; Goals; Homeostasis; Hypercapnic respiratory failure; In Situ; In Vitro; Ion Channel; Life; Mammals; Methods; Modeling; Motor; Motor Activity; Movement; Nervous system structure; Neurons; Oxygen; Pattern; Peripheral; Phase; Physiological; Physiological Processes; Population; Preparation; Property; Rattus; Regulation; Research; Respiration; Respiratory Transport; Rodent; Role; Signal Transduction; Sleep Apnea Syndromes; Slice; Spinal Cord; Study models; Sudden infant death syndrome; Synapses; Syndrome; System; Systems Theory; Testing; Time; Transport Process; United States National Institutes of Health; cluster computing; design; experimental analysis; expiration; insight; large scale simulation; multi-scale modeling; multidisciplinary; network models; neural circuit; neural patterning; neurogenesis; neuromechanism; neurophysiology; neuroregulation; novel; operation; parallel processing; reconstruction; relating to nervous system; research study; respiratory; respiratory gas; simulation

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 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 conditions 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 continued implementation of simulation approaches involving cluster computing on large distributed parallel processing systems including the NIH Biowulf cluster 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.

研究涉及在啮齿动物大脑中并行研究的神经元和网络的新型神经动力学模型和网络的进一步发展。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逼真的细胞水平计算模型，其中包含了有关细胞结构和生物物理特性的当前信息，例如离子电导机制，例如神经元活性的离子电导机制； （2）脑干呼吸神经网络的大型模型，其中包含有关网络功能和结构架构的可用信息。这些建模研究的总体目的是获得机械洞察力，以了解细胞和电路级特性集成到微电路中的方式以及大规模呼吸网络，以进行哺乳动物呼吸神经控制系统的动力运行。啮齿动物脑干中的呼吸中心模式产生（CPG）网络的新模型进一步发展，包括分布在串行布置的脑干结构隔室中的相互作用的兴奋性和抑制性子网组成，每个作业在呼吸神经活动模式的产生和控制中具有独特的功能，这些功能在正常呼吸周期中随着呼吸周期而演变。该CpG模型中使用的基本网络结构和细胞性能源自在大鼠脑干脊髓原位和在活性电路中体外的活脑干切片制备中分离的亚分离的大鼠脑茎脊髓和亚网络中进行的电生理和神经解剖学重建研究。这些模型还通过建模的传入输入信号（包括来自关键神经调节控制系统的有节奏的主动输入）对不同电路组件进行了调节，这些输入涉及呼吸模式产生的调节。为了对CPG网络操作进行动态分析，还采用了动态系统理论的方法来识别临界动力学变量和电路操作的参数，这些变量是呼吸节奏和模式产生和模式生成并控制灵感和呼气神经活动功能上不同阶段之间的有序过渡的基础。具有微电路和大规模模型的计算机模拟模仿了单细胞和神经元种群活动模式的许多特征，在不同的体外和原位条件下实验发现。从实验研究中得出并进一步测试了这些模型的主要新假设是，在多个细胞和网络组织的呼吸道CPG中存在振荡活性的能力，形成了耦合振荡机制的动力学系统。因此，可以以大脑状态依赖性方式在功能上表达不同的呼吸节奏生成的机制，并且是多种呼吸运动行为的基础，其中一些发生在正常的生理条件下，而另一些则发生在病理生理状况下，例如在严重的脑缺氧期间（异常低氧气的条件）。具有不同级别的细胞和网络复杂性模型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细胞和网络机制的见解。我们还继续实施涉及大型分布式并行处理系统上的群集计算的模拟方法，包括NIH Biowulf群集，该群集可以实时模拟大型网络模型。在系统级别上，已经进一步开发了呼吸神经控制系统的模型，即将基本的神经回路动力学与周围氧气和二氧化碳交换，血液传输以及通过信号通过诸如氧气和脑部脑部水平等信号来调节中央呼吸道环境的生理反馈。这些后一种模型代表了系统级控制模型的第一代，该模型整合了神经系统结构功能的基本要素以及呼吸气体交换和运输系统的现实特征。目前，所有这些模型都用于进一步探索脑干呼吸回路运行原理，并控制呼吸活动，包括在各种（病情）生理条件下与大脑和人体氧气/二氧化碳稳态的障碍相关的各种生理条件。