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: 8529207
• 负责人: BRUCE J GLUCKMAN
• 金额: $ 18.02万
• 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 2015-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8529207
• 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）：此CRCN申请源于目前的DAAD（Deutscher Akademischer Austausch Dienst，德国学术交流服务）在柏林技术大学和宾夕法尼亚州立大学之间的授予：”系统：理论和实验”。该CRCN提案的设计以及所有初步数据都是在来到宾夕法尼亚州立大学的德国教职员工和博士生过程中产生的，以及协同合作的努力，以确定对抑郁症的反馈控制的可行性。扩散抑郁（SD）是大脑的戏剧性去极化，它缓慢地传播，并且是偏头痛初始光环的生理基础。提出了以下假设：可以在基础神经元生物物理学的计算模型中表示SD，因此可以使用基于模型的控制策略来控制。该项目首先使用切线二维视觉皮层啮齿动物脑切片开发实验制剂。 SD是用灌注钾摄动触发的，SD使用敏感的CCD摄像机成像，该摄像头摄像机检测与细胞肿胀相关的折射变化相关的固有光学成像信号。采用了类似于自动机器人技术（例如机体自动行器）类似的模型策略。硬件和软件控制系统实时采用光学图像，将其与SD模型融合，重建基本的生理过程，计算所需的控制并调节电场以调节SD。神经元区和离子流的生物物理精确模型，以及反映波传播动力学的模型，将用作观察和控制模型。智力优点：这将是基于模型的神经元网络控制的第一个实验证明。类似的工程策略彻底改变了先进的机器人技术，并且从计算神经科学与控制工程的融合中学到的基本原理将在神经元调制的其他领域进行广泛的适应。此外，这将是对脑部动态疾病基础的生理机制的第一个基于模型的控制 - 偏头痛。控制模型将进一步充当探针，以增加对SD机制的了解。组装团队在执行该项目所需的学科范围内具有巨大的记录：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，鉴于所需的资源，该项目是可行的。更广泛的影响：将计算神经科学模型与现代模型的控制理论进行融合将为观察大脑活动活动的转化范式奠定基础，并访问更最佳的技术，以控制大脑中的病理过程。将形成一个跨学科的德国教育合作，在计算神经科学，控制理论，实验性神经生理学和控制系统工程之间的界面中，受过培训的研究生（和PIS）将协同工作。 PI在培训和指导妇女和代表性不足的少数民族方面都有往绩，他们将尽一切努力为这项项目的指导机会寻求这些受训者。作为合作伙伴关系，PIS预计控制SD中所学到的知识可能会为患有严重偏头痛攻击并且在药理学上棘手的人的偏头痛电气控制提供一组可测试策略。此外，基于此CRCN，相同的科学和工程将适用于体外的振荡波和节奏的调节（例如Schiff等，2007）和体内（例如Sunderam等人2009）系统。他们计划广泛传播按数据管理计划中所述开发的算法和硬件设计。