CRCNS: Collaborative Research: Model-Based Control of Spreading Depression

CRCNS：合作研究：基于模型的抑郁症蔓延控制

• 批准号: 8258411
• 负责人: BRUCE J GLUCKMAN
• 金额: $ 20.5万
• 依托单位: PENNSYLVANIA STATE UNIV HERSHEY MED CTR
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-15 至 2014-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8258411
• 关键词: Algorithms; American; Area; Auras; Berlin; Biophysics; Brain; Brain Diseases; Collaborations; Computer Simulation; Computer software; Data; Discipline; Disease; Doctor of Philosophy; Engineering; Experimental Models; Extracellular Space; Faculty; Feedback; Foundations; German population; Goals; Grant; Image; In Vitro; Intervention; Ions; Learning; Measurement; Mentors; Migraine; Modeling; Neocortex; Neuroglia; Neurons; Pathologic Processes; Patients; Physics; Physiological; Physiological Processes; Potassium; Preparation; Process; Refractory; Research; Resources; Robotics; Rodent; Science; Services; Signal Transduction; Slice; Spreading Cortical Depression; Students; Swelling; System; Systems Theory; Technology; Time; Training; Underrepresented Minority; Universities; Visual Cortex; Woman; Work; base; charge coupled device camera; computational neuroscience; data management; design; graduate student; improved; in vivo; indexing; instrumentation; neurophysiology; optical imaging; prevent; real time model; relating to nervous system; research study; spatiotemporal; spreading depression; theories; time use; two-dimensional

DESCRIPTION (provided by applicant): This CRCNS application derives from work performed in a current DAAD (Deutscher Akademischer Austausch Dienst, German Academic Exchange Service) Grant between the Technical University of Berlin and Penn State University entitled: "Feedback control of spreading depolarizations in neural systems: Theory and Experiments". The design of this CRCNS proposal, and all preliminary data, were generated during the course of German Faculty and PhD students coming to Penn State University, and the synergistic collaborative efforts to establish the feasibility of feedback control of spreading depression. Spreading depression (SD) is a dramatic depolarization of brain that propagates slowly and is the physiological underpinning of the initial aura in migraines. The following hypothesis is posed: SD can be represented in computational models of the underlying neuronal biophysics, and can therefore be controlled using model-based control strategies. The project starts by developing an experimental preparation using a tangential 2-dimensional visual cortex rodent brain slice. SD is triggered with a perfusate potassium perturbation, and SD is imaged using a sensitive CCD camera that detects the intrinsic optical imaging signal associated with index of refraction changes from cellular swelling. A model-based strategy similar to that used in autonomous robotics such as airframe autolanders is employed. A hardware and software control system takes the optical image in real-time, fuses it with a model of SD, reconstructs the underlying physiological processes, calculates needed control, and modulates an electrical field to modulate SD. Both biophysically accurate models of the neuronal compartments and ion flows, and reduced models that reflect the dynamics of the wave propagation, will be used as observation and control models. Intellectual merit: This will be the first experimental demonstration of model-based control of a neuronal network. Similar engineering strategies have revolutionized advanced robotics, and the fundamentals learned from a fusion of computational neuroscience with control engineering will have wide ranging adaptations in other areas of neuronal modulation. Furthermore, this will be the first model-based control of a physiological mechanism that underlies a dynamical disease of the brain - migraine auras. The control models will further serve as probes to gain increased understanding of the mechanisms of SD. The team assembled has a substantial track record in the range of disciplines required to carry out this project: neurophysiology, experimental and theoretical physics, computational neuroscience, control theory and neural engineering. The preliminary work shown in the proposal suggests that this project is feasible given the resources requested. Broader impact: Fusing computational neuroscience models with modern model-based control theory will lay the foundation for a transformational paradigm for the observation of activity within the brain, as well as access to a more optimal technology for the control of pathological processes in the brain. A transdisciplinary German-American educational collaboration will be formed where the graduate students trained (and the PIs) will synergistically work together within the interface between computational neuroscience, control theory, experimental neurophysiology, and control system engineering. The PIs have a track record in training and mentoring women and underrepresented minorities, and they will make every effort to seek such trainees for the mentoring opportunities of this project. As a collaborative partnership, the PIs anticipate that what is learned in controlling SD may provide a set of testable strategies for electrical control of migraines in people who suffer from severe migraine attacks and are pharmacologically intractable. Furthermore, based upon this CRCNS, the same science and engineering will be applicable to the modulation of oscillatory waves and rhythms in both in vitro (e.g. Schiff et al 2007) and in vivo (e.g. Sunderam et al 2009) systems. They plan to widely disseminate the algorithms and hardware design developed as described in the Data Management Plan.

描述（由申请人提供）：该 CRCNS 应用程序源自柏林工业大学和宾夕法尼亚州立大学之间当前 DAAD（德国学术交流中心，德国学术交流服务）资助项目中进行的工作，题为：“神经系统中传播去极化的反馈控制：理论与实验”。该 CRCNS 提案的设计和所有初步数据都是在德国教师和博士生来到宾夕法尼亚州立大学期间产生的，并通过协同合作努力建立了反馈控制抑郁症蔓延的可行性。扩散性抑郁（SD）是一种缓慢传播的大脑剧烈去极化，是偏头痛最初先兆的生理基础。提出以下假设：SD 可以用基础神经元生物物理学的计算模型来表示，因此可以使用基于模型的控制策略进行控制。该项目首先使用切向二维视觉皮层啮齿动物大脑切片开发实验制剂。 SD 由灌注液钾扰动触发，SD 使用灵敏的 CCD 相机进行成像，该相机检测与细胞肿胀引起的折射率变化相关的固有光学成像信号。采用基于模型的策略，类似于机身自动着陆器等自主机器人中使用的策略。硬件和软件控制系统实时获取光学图像，将其与 SD 模型融合，重建底层生理过程，计算所需的控制，并调制电场以调制 SD。神经元区室和离子流的生物物理精确模型以及反映波传播动力学的简化模型都将用作观察和控制模型。智力价值：这将是基于模型的神经网络控制的第一个实验演示。类似的工程策略已经彻底改变了先进的机器人技术，从计算神经科学与控制工程的融合中学到的基础知识将在神经元调制的其他领域具有广泛的适应性。此外，这将是第一个基于模型的对大脑动力疾病——偏头痛先兆的生理机制的控制。控制模型将进一步作为探针，以加深对 SD 机制的理解。组建的团队在执行该项目所需的学科范围内拥有丰富的记录：神经生理学、实验和理论物理学、计算神经科学、控制理论和神经工程。提案中显示的初步工作表明，鉴于所需资源，该项目是可行的。更广泛的影响：将计算神经科学模型与基于模型的现代控制理论相融合，将为观察大脑内活动的转变范式奠定基础，并为控制大脑病理过程提供更优化的技术。将形成跨学科的德美教育合作，受训的研究生（和 PI）将在计算神经科学、控制理论、实验神经生理学和控制系统工程之间的界面内协同工作。 PI 在培训和指导女性和代表性不足的少数群体方面拥有良好的记录，他们将尽一切努力寻找此类学员以获得该项目的指导机会。作为合作伙伴关系，PI 预计，在控制 SD 方面所学到的知识可能会提供一套可测试的策略，用于对患有严重偏头痛发作且药物难以治愈的患者进行偏头痛的电控制。此外，基于该 CRCNS，相同的科学和工程将适用于体外（例如 Schiff 等人 2007）和体内（例如 Sunderam 等人 2009）系统中振荡波和节律的调制。他们计划广泛传播按照数据管理计划中所述开发的算法和硬件设计。