CRCNS: US-France-Israel Research Proposal: A personalized approach to brain stimulation

CRCNS：美国-法国-以色列研究提案：个性化的大脑刺激方法

• 批准号: 10706955
• 负责人: Jin Hyung Lee
• 金额: $ 34.76万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-30 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10706955
• 关键词: Address; Anatomy; Animals; Behavior; Biological; Brain; Brain region; Cell model; Cells; Clinical Trials; Computer Models; Data; Deep Brain Stimulation; Dementia; Development; Diffusion Magnetic Resonance Imaging; Enhancers; Epilepsy; Evolution; Experimental Models; France; Frequencies; Functional Magnetic Resonance Imaging; Gene Expression; Generations; Genetic; Goals; Human; Individual; Instruction; International; Interruption; Israel; Magnetic Resonance Imaging; Memory; Mental Depression; Mental disorders; Modeling; Motor; Movement Disorders; Mus; Oxidopamine; Parkinson Disease; Pathology; Process; Research Proposals; Resolution; Rest; Scanning; Seizures; Specificity; Speed; Statistical Models; Stimulus; Technology; Testing; Time; Transgenic Animals; Transgenic Mice; Translations; Treatment outcome; Validation; biophysical model; cell type; clinically relevant; design; experimental study; genetic manipulation; improved; in silico; in vivo; individual patient; insight; motor behavior; mouse model; nervous system disorder; optogenetics; personalized approach; predictive modeling; success; symptom treatment; therapy outcome; virtual

Deep brain stimulation is approved for a number of neurological disorders. However, the where, when and how to stimulate the brain are still empirically solved. The goal of this proposal is to develop a systematic framework that will enable a better understanding of the effects of deep brain stimulation. We will expand The Virtual Brain (TVB), a computational model we developed, with a data-driven approach taking into account cell-type specific control. The project relies on a tight interaction between experimentation and computational modelling. First, using enhancer-based genetics, we will activate/silence specific cell types with opto- /chemo-genetics while acquiring resting state and stimulus driven fMRI in individual mice. Each brain will be virtualized in TVB and undergo data fitting, validation and inference using Stan, a platform for statistical modeling. Model predictions will then be verified in the same individual mice. As a first step towards an application in pathologies, the cells and parameters that need to be controlled will be predicted in silico to stop seizures in experimental epilepsy, and to restore resting state dynamics and rescue motor behavior in an experimental model of Parkinson's disease. Predictions will then be tested experimentally. The proposed project will allow the generation and testing of hypotheses concerning the control of specific cell types with unparalleled biological relevance, precision, and speed. The main goal is to show that it is possible to drive brain activity with stimulation in a predictable way. Through cell type specific modeling of whole brain function in mice, we aim to achieve a major milestone in the development of a realistic, large- scale, anatomical, biophysical model of the human brain. Through unique expertise and technologies our international, interdisciplinary team has pioneered, we will address the problem of understanding how brain stimulation can be systematically designed for individual patients. RELEVANCE (See instructions): The success of deep brain stimulation in treating movement disorders led to its application to various psychiatric and neurological disorders, in an attempt to treat symptoms as well as to directly improve memory function. However, recent clinical trials failed to demonstrate significant effects in epilepsy, depression and dementia, calling for new scientific developments to better understand where, how, and when to stimulate the brain to obtain specific effects. Our experiments will provide fundamental mechanistic insight into how the stimulations impact the brain and how it can be designed for optimal therapeutic outcome in individual brains.

大脑刺激被批准用于多种神经系统疾病。但是，何时，何时和 如何刺激大脑仍在经验上解决。该提议的目的是发展系统 框架可以更好地了解深脑刺激的影响。我们将扩展 虚拟大脑（TVB）是我们开发的计算模型，采用数据驱动的方法采用 帐户类型特异性对照。该项目依赖于实验和 计算建模。首先，使用基于增强子的遗传学，我们将激活/静音特定细胞类型 在单个小鼠中获取静息状态和刺激驱动的fMRI时，使用光学 /化学基因。每个 大脑将在TVB中虚拟化，并使用Stan进行数据拟合，验证和推理，Stan是一个平台 统计建模。然后将在同一个人小鼠中验证模型预测。作为迈向的第一步 在病理中的应用，需要控制需要控制的细胞和参数将在计算机中预测 在实验性癫痫中停止癫痫发作，并恢复静止状态动力并挽救运动行为 帕金森氏病的实验模型。然后将对预测进行实验测试。提议 项目将允许与有关特定细胞类型的控制的假设生成和测试 无与伦比的生物学相关性，精度和速度。主要目标是表明可以开车 以可预测的方式进行刺激的大脑活动。通过细胞类型的特定模型的整个大脑功能 在老鼠中，我们旨在在发展现实，大规模，解剖学， 人脑的生物物理模型。通过独特的专业知识和技术，我们的国际 跨学科的团队开创了率高，我们将解决理解大脑刺激的问题 可以系统地为个别患者设计。 相关性（请参阅说明）： 深脑刺激在治疗运动障碍中的成功导致其应用于各种 精神病和神经系统疾病，试图治疗症状并直接改善 内存功能。但是，最近的临床试验未能显示出对癫痫的显着影响， 抑郁和痴呆症，呼吁新的科学发展，以更好地了解何处，如何和 何时刺激大脑以获得特定效果。我们的实验将提供基本的机理 深入了解刺激如何影响大脑以及如何设计用于最佳治疗 个体大脑的结果。