EAGER: Computational investigation of the distributed decentralized control of cerebral blood flow

EAGER：脑血流分布式分散控制的计算研究

• 批准号: 1301198
• 负责人: Andreas Linninger
• 金额: $ 7万
• 依托单位: University of Illinois at Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1301198&HistoricalAwards=false
• 关键词: EAGER; Computational; investigation; distributed; decentralized

Intellectual Merit:Significance - Cerebral autoregulation is the remarkable control task of maintaining constant cerebral blood flow over a wide range of disturbances such as posture change, physical activity, or changes in cardiac output. In addition, the brain is capable of dynamically up-regulating blood flow to very specific, locally confined territories of the brain to support the metabolic reactions of neuronal firing, referred to as functional hyperemia. This servo problem is accomplished in fractions of a second in a small confined cortical domain without affecting blood perfusion to other cortical regions. Though physiological and anatomical details of cerebral autoregulation and functional hyperemia have been well researched, the systems control behavior is not well understood. We propose a holistic systems approach for the investigation of the fundamental principles of decentralized distributed blood flow control in the entire human brain. Our project plan foresees a non-conventional pioneering approach integrating control theory, computational fluid dynamics, biochemistry, neurophysiology, and biomedical imaging. While our interdisciplinary approach entails ahigh risk approach, novel insights from research on complex dynamics and regulation of the human brain may be transformative, making the project an excellent candidate for the EAGER funding.We have already constructed a morphologically accurate, physiologically consistent, multi-scale computer network model of the entire cerebral vasculature to predict the functional interaction of structural and hemodynamic parameters in tissue oxygen perfusion. Systems engineering methods will be used to adapt this model for the investigation of autoregulatory control and functional hyperemia of the human brain. To research the distributed decentralized control functions of the brain, we will incorporatethe several biochemical and physiological principles: (i) active vasodilation through vasoactive signaling molecules, (ii) feed forward signal processing, and (iii) distributed oxygen-sensing chemo-receptors in the brain tissue.This research will introduce spatially distributed, time-dependent simulations based on first principles of fluid dynamics and passivity control theory of the entire brain1. The decentralized distributed control mechanisms will be shown to require only biochemical sensors sensitive to neural tissue oxygenation and local wall shear stress without the need for centralized supervision. We will investigate the brain?s remarkable stability and specificity in achieving highly localized blood flow distribution without altering flow to adjacent cortical territories. This localized specificity of cortical blood flow has been observed in functional Magnetic Resonance Imaging (fMRI), but the hemodynamic control principles are not known. We will predict and explain the time delay between neuronal firing, changes in relative cerebral blood flow (rCBF) and tissue oxygen perfusion. This first principles model will explain the physical and chemical kinetic principles underlying fMRI, which are currently under debate in the medical imaging community. The final systems model of cerebral blood flow control will predict the decentralized, distributed, dynamic behavior of cerebral blood flow in response to local neuronal firing and stable rCBF despite inlet arterial blood pressure fluctuations. Model predictions will be validated using advanced distributed mathematical programming techniques to match a spectrum of temporally and spatially distributed data acquired in vivo by medical imaging modalities.Broader Impact:This project offers -for the first time- a dynamic computer model to elucidate the principles of cerebral autoregulation by integrating control theory, computational fluid mechanics and medical imaging into a single visionary project plan. The insights from investigating rCBF dynamics of the entire brain will unravel nature?s design for robust, distributed and decentralized control. Due to the complex distributed blood flow demand in the human cortex, a systems approach is needed to quantify and characterize the underlying dynamic mechanisms. The knowledge gain is expected to create new opportunities for controlling distributed technical systems such as artificial organs, dialysis machines or process engineering to hypothermal stroke treatments. To achieve a broader impact, the final computational model will be disseminated to the research community via a comprehensive data sharing plan and distribution via the lectures in the UIC CAVE-2 virtual reality environment at UIC. Undergraduate students and high school teachers will benefit from the intellectual core created in this through the NSF-sponsored REU and RET programs directed by the PI.

智力优点：意义 - 大脑自动调节是一项显着的控制任务，可在各种干扰（例如姿势变化、体力活动或心输出量变化）下维持恒定的脑血流量。此外，大脑能够动态上调流向非常特定的、局部受限的大脑区域的血流量，以支持神经元放电的代谢反应，称为功能性充血。这个伺服问题是在一个小的有限皮质区域中在几分之一秒内完成的，而不影响其他皮质区域的血液灌注。尽管大脑自动调节和功能性充血的生理和解剖学细节已得到充分研究，但系统控制行为尚不清楚。我们提出了一种整体系统方法来研究整个人脑分散式血流控制的基本原理。我们的项目计划预见了一种结合控制理论、计算流体动力学、生物化学、神经生理学和生物医学成像的非传统开创性方法。虽然我们的跨学科方法需要高风险的方法，但对人脑复杂动力学和调节的研究的新见解可能具有变革性，使该项目成为 EAGER 资助的优秀候选者。我们已经构建了整个脑血管系统的形态准确、生理一致、多尺度计算机网络模型，以预测组织氧灌注中结构和血流动力学参数的功能相互作用。将使用系统工程方法来调整该模型，以研究人脑的自动调节控制和功能性充血。为了研究大脑的分布式分散控制功能，我们将结合几个生化和生理原理：（i）通过血管活性信号分子进行主动血管舒张，（ii）前馈信号处理，以及（iii）脑组织中的分布式氧传感化学受体。这项研究将引入基于流体第一原理的空间分布、时间依赖性模拟 全脑动力学与被动控制理论1．分散的分布式控制机制将被证明只需要对神经组织氧合和局部壁剪切应力敏感的生化传感器，而不需要集中监督。我们将研究大脑在不改变邻近皮质区域血流的情况下实现高度局部化的血流分布的显着稳定性和特异性。皮质血流的这种局部特异性已在功能磁共振成像（fMRI）中观察到，但血流动力学控制原理尚不清楚。我们将预测并解释神经元放电之间的时间延迟、相对脑血流量（rCBF）和组织氧灌注的变化。这个第一原理模型将解释功能磁共振成像的物理和化学动力学原理，目前医学成像界正在争论这一点。脑血流控制的最终系统模型将预测脑血流响应局部神经元放电的分散的、分布式的动态行为和稳定的 rCBF，尽管入口动脉血压波动。模型预测将使用先进的分布式数学编程技术来验证，以匹配通过医学成像模式在体内获取的一系列时间和空间分布的数据。更广泛的影响：该项目首次提供了一个动态计算机模型，通过将控制理论、计算流体力学和医学成像集成到一个单一的有远见的项目计划中来阐明大脑自动调节的原理。研究整个大脑 rCBF 动力学的见解将揭示自然的稳健、分布式和去中心化控制的设计。由于人类皮质中复杂的分布式血流需求，需要一种系统方法来量化和表征潜在的动态机制。知识的获得预计将为控制分布式技术系统（例如人造器官、透析机或低温中风治疗的工艺工程）创造新的机会。为了实现更广泛的影响，最终的计算模型将通过全面的数据共享计划传播给研究界，并通过 UIC CAVE-2 虚拟现实环境中的讲座进行分发。本科生和高中教师将受益于由 PI 指导的 NSF 资助的 REU 和 RET 项目所创建的知识核心。