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 - 脑自动调节是在广泛的干扰（例如姿势变化，身体活动或心脏输出变化）中保持恒定脑血流的显着控制任务。此外，大脑能够动态上调血液流动到非常特异性的，局部狭窄的大脑领土，以支持神经元发射的代谢反应，称为功能性充血。这个伺服问题是在小小的约束皮质结构域中的第二个部分中完成的，而不会影响其他皮质区域的血液灌注。尽管已经对脑自动调节和功能性充血的生理和解剖学细节进行了充分的研究，但系统控制行为尚未得到充分了解。我们提出了一种整体系统方法，以研究整个人脑中分散分配的血流控制的基本原理。我们的项目计划预见了一种非规定的先驱方法，整合控制理论，计算流体动力学，生物化学，神经生理学和生物医学成像。虽然我们的跨学科方法需要Ahigh的风险方法，但对复杂动力学和人类大脑调节的研究的新见解可能具有变革性，使该项目成为渴望资助的好候选者。我们已经构建了一个准确的，生理上一致的，在生理上一致，多尺度的计算机网络模型的整个脑脑血管型型型型和型组合型的型型锻炼，并构成了整个脑干的插件。系统工程方法将用于调整该模型，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分散控制功能，我们将结合几种生物化学和生理原理：（i）通过血管活性信号分子进行积极的血管扩张，（ii）正向信号处理，（ii）（iii）（iii）（iii）分布的含氧化学物质在大脑组织中的分布式分配，以介绍的是介绍的跨度分配，以实时的分配，定时介绍了定期启用，以实时的分配，以实时的分配，以实时的定位量，整个大脑的动力和被动控制理论。分散的分布式控制机制将显示仅需要对神经组织氧合和局部壁剪应力敏感的生化传感器，而无需集中监督。我们将研究大脑在达到高度局部的血流分布时的显着稳定性和特异性，而不会改变与相邻皮质区域的流动。在功能磁共振成像（fMRI）中已经观察到了皮质血流的局部特异性，但是血液动力学控制原理尚不清楚。我们将预测并解释神经元放电，相对脑血流（RCBF）和组织氧灌注之间的时间延迟。第一个原理模型将解释fMRI基础的物理和化学动力学原理，该原理目前正在医学成像界争论。脑血流控制的最终系统模型将预测脑血流的分散，分布式，动态行为，响应局部神经元放电和稳定的RCBF，尽管入学动脉血压波动。模型预测将使用先进的分布式数学编程技术验证，以匹配通过医学成像模式在体内获取的时间和空间分布的数据。BOADER的影响：该项目提供 - 首次提供的动态计算机模型，以阐明通过将单个医学计算机进行计算的单身制度和有影响力的单身图像阐明大脑自动调查的原理。研究整个大脑的RCBF动力学的见解将揭示出强大，分布和分散控制的设计。由于人皮层中复杂的分布流动需求，需要进行系统方法来量化和表征潜在的动态机制。预计知识增益将为控制分布式技术系统（例如人造器官，透析机或工艺工程）创造新的机会，以进行体温过低。为了实现更广泛的影响，最终的计算模型将通过UIC的UIC Cave-2虚拟现实环境中的演讲来通过全面的数据共享计划和分​​发传播到研究社区。本科生和高中教师将受益于通过NSF赞助的REU和PI指导的RET计划创建的知识核心。