Multiple time scales, coupling properties, and network interactions in respiratory rhythmicity

呼吸节律中的多时间尺度、耦合特性和网络相互作用

• 批准号: 1612913
• 负责人: Jonathan Rubin
• 金额: $ 28万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2020-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1612913&HistoricalAwards=false
• 关键词: Multiple; time; scales; coupling; properties

Animals perform a variety of repetitive behaviors, such as breathing and walking, without the need for conscious control. This automation is made possible by particular sets of neurons that are specialized to produce the outputs that drive these behaviors. There are many unanswered questions about how these neurons generate activity with the appropriate features in a way that adapts quickly and automatically to evolving conditions, such as changes in respiratory demand that occur in a switch from a walk to a run. This project uses mathematical and computational approaches to address several such questions that arise in the context of breathing. A first set of issues that will be studied relates to the understanding of how processes that evolve on very different timescales contribute to respiratory rhythms. A second set of issues relates to how neurons involved in a particular phase of respiration, as well as populations of neurons active at different phases, become coordinated to produce effective breathing rhythms. How these interactions break down to yield respiratory dysfunction, particularly in the context of the severe breathing disruptions arising in Rett syndrome, will also be studied. The work will be completed in collaboration with experimentalists, and results of the project will lead to improved models of respiratory data and novel ideas on how to counter respiratory disorders. In addition to enhancing understanding of respiratory function and dysfunction, this project will have broad implications, since rhythmic patterns produced by interacting networks of dynamic components involving multiple timescales are common across a wide range of biological and physical systems. Trainees contributing to this research will gain experience with using computational methods to address data-driven questions in neuroscience. Methods and findings developed will contribute to the training of students via local group meetings and courses and will be disseminated more broadly via publications, presentations, and model sharing. A variety of repetitive behaviors fundamental to animals' interactions with the environment are driven by the rhythmic activity of networks of coupled neurons. This project will address rhythm generation and control via a focus on neuronal networks in the mammalian brainstem associated with respiration. In the rhythms that these networks produce, multiple populations of neurons take turns activating at specific relative times within each breathing cycle, and the activity within each population is synchronized when it arises. This work will analyze how synchronized activity with complex dynamic features, called bursting, arises in a particular brainstem region during the inspiratory phase of breathing. Doing so will involve novel mathematical analysis of the dynamics of systems with components that evolve on several distinct timescales. This work will lead to new insights into how respiratory processes should be modeled, which will be useful for the study of particular respiratory phenomena such as sighing and which will also generalize to other biological and physical systems with multiple timescale dynamics. New results will also be attained, using mathematical and computational methods, about how synchronized bursting in neuronal networks depends on how the neurons in the network are interconnected and communicate with each other. Respiratory rhythms must be robust to changes in environmental and metabolic demands, and these findings will yield predictions about what features provide this robustness. Finally, this project will use analysis methods that reveal the dynamic effects of particular parameter variations to elucidate how the intrinsic properties of neurons within particular respiratory areas and the patterns of connections between areas contribute to overall respiratory rhythmicity and to disruptions of respiratory dynamics. These steps will be guided by novel experimental data and will result in advances in basic understanding as well as predictions about effective interventions to counter respiratory dysfunction.

动物进行各种重复的行为，如呼吸和行走，而不需要有意识的控制。这种自动化是由一组特定的神经元实现的，这些神经元专门产生驱动这些行为的输出。关于这些神经元如何以一种快速、自动地适应不断变化的条件的方式产生具有适当特征的活动，比如从步行到跑步的转换中发生的呼吸需求的变化，还有许多悬而未决的问题。这个项目使用数学和计算方法来解决在呼吸的上下文中出现的几个这样的问题。将研究的第一组问题涉及理解在非常不同的时间尺度上进化的过程如何影响呼吸节律。第二组问题涉及到参与呼吸的特定阶段的神经元，以及在不同阶段活跃的神经元群体，如何协调产生有效的呼吸节奏。还将研究这些相互作用如何分解产生呼吸功能障碍，特别是在Rett综合征引起的严重呼吸中断的情况下。这项工作将与实验人员合作完成，该项目的结果将导致改进呼吸数据模型和如何对抗呼吸系统疾病的新想法。除了加强对呼吸功能和功能障碍的理解外，该项目将具有广泛的意义，因为涉及多个时间尺度的动态组件相互作用网络产生的节律模式在广泛的生物和物理系统中是常见的。为这项研究做出贡献的学员将获得使用计算方法解决神经科学中数据驱动问题的经验。所开发的方法和发现将有助于通过当地小组会议和课程培训学生，并将通过出版物、报告和模式分享更广泛地传播。动物与环境相互作用的各种基本重复行为是由耦合神经元网络的节律性活动驱动的。该项目将通过关注哺乳动物脑干中与呼吸相关的神经网络来解决节律的产生和控制问题。在这些神经网络产生的节律中，在每个呼吸周期中，多个神经元群在特定的相对时间轮流激活，每个神经元群的活动在出现时是同步的。这项工作将分析具有复杂动态特征的同步活动，称为爆发，是如何在呼吸的吸气阶段在特定的脑干区域产生的。这样做将涉及对系统动力学的新颖数学分析，这些系统的组件在几个不同的时间尺度上演化。这项工作将导致对如何模拟呼吸过程的新见解，这将有助于研究特定的呼吸现象，如叹气，也将推广到其他具有多时间尺度动力学的生物和物理系统。使用数学和计算方法，也将获得新的结果，关于神经元网络中的同步爆发如何取决于网络中的神经元如何相互连接和相互通信。呼吸节律必须对环境和代谢需求的变化具有稳健性，这些发现将有助于预测哪些特征提供了这种稳健性。最后，该项目将使用揭示特定参数变化的动态效应的分析方法来阐明特定呼吸区域内神经元的内在特性以及区域之间的连接模式如何有助于整体呼吸节律性和呼吸动力学的中断。这些步骤将以新的实验数据为指导，并将在基本理解和预测有效干预措施方面取得进展，以对抗呼吸功能障碍。