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.

脑深部电刺激被批准用于许多神经系统疾病。然而，在何处，何时， 如何刺激大脑仍然是凭经验解决的该提案的目标是制定一个系统的 该框架将使人们能够更好地理解脑深部电刺激的效果。我们将扩大 虚拟大脑（TVB），我们开发的一个计算模型，采用数据驱动的方法， 特定于帐户单元格类型的控件。该项目依赖于实验和 计算机建模首先，使用基于增强子的遗传学，我们将激活/沉默特定的细胞类型， 与光/化学遗传学，同时获得静息状态和刺激驱动的功能磁共振成像在个别小鼠。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是一个用于 统计建模然后将在相同的个体小鼠中验证模型预测。作为第一步 病理学中的应用，需要控制的细胞和参数将通过计算机预测， 在实验性癫痫中停止癫痫发作，并恢复静息状态动力学和挽救运动行为， 帕金森病的实验模型然后，将通过实验对预测进行检验。拟议 该项目将允许生成和测试有关控制特定细胞类型的假设， 无与伦比的生物相关性、精确性和速度。主要目的是为了证明驾驶是可能的 大脑活动与刺激以可预测的方式。通过全脑功能的细胞类型特异性建模 在小鼠中，我们的目标是实现一个重要的里程碑，在一个现实的，大规模的，解剖学的， 人脑的生物物理模型。通过独特的专业知识和技术， 跨学科团队已经开创了，我们将解决这个问题，了解大脑刺激如何 可以针对个别患者进行系统设计。 相关性（参见说明）： 脑深部电刺激在治疗运动障碍方面的成功导致其应用于各种疾病。 精神和神经系统疾病，试图治疗症状以及直接改善 记忆功能然而，最近的临床试验未能证明对癫痫的显著作用， 抑郁症和痴呆症，呼吁新的科学发展，以更好地了解在哪里，如何， 什么时候刺激大脑以获得特定的效果。我们的实验将提供基本的 深入了解刺激如何影响大脑，以及如何设计最佳治疗方案， 结果在个别大脑中。