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: 10268236
• 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/10268236
• 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; base; biophysical model; cell type; clinically relevant; design; experimental study; 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.

深部脑刺激被批准用于治疗一些神经疾病。然而，在哪里，何时，以及 如何刺激大脑仍是经验性的问题。这项提议的目标是开发一种系统的 这一框架将使我们能够更好地了解脑深部刺激的效果。我们将扩大规模 虚拟大脑(TVB)，我们开发的计算模型，采用数据驱动的方法 帐户单元格类型的特定控件。该项目依赖于实验和实验之间的紧密交互 计算建模。首先，使用基于增强子的遗传学，我们将激活/沉默特定类型的细胞 在获得静息状态和刺激驱动的单个小鼠的功能磁共振成像的同时，进行光/化学遗传学研究。每个人 Brain将在TVB中进行虚拟，并使用Stan平台进行数据拟合、验证和推理 统计建模。然后，模型预测将在相同的小鼠身上得到验证。作为迈向 在病理学中的一个应用，需要控制的细胞和参数将在计算机中被预测 停止实验性癫痫发作，恢复静息状态动力学和抢救运动行为 帕金森氏症的实验模型。然后，这些预测将得到实验验证。建议数 该项目将允许生成和测试关于控制特定细胞类型的假设 无与伦比的生物相关性、精确度和速度。主要的目标是展示驾驶是可能的 以可预测的方式进行刺激的大脑活动。通过对整个大脑功能的特定细胞类型建模 在老鼠身上，我们的目标是实现一个重要的里程碑，在开发一个现实的，大规模的，解剖的， 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， 跨学科团队已经开创了先河，我们将解决如何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)： 深部脑刺激在治疗运动障碍方面的成功使其在各种疾病中得到应用。 精神和神经障碍，试图治疗症状和直接改善 记忆功能。然而，最近的临床试验未能证明对癫痫有显著效果， 抑郁症和痴呆症，呼吁新的科学发展来更好地了解地点、方式和 何时刺激大脑才能获得特定的效果。我们的实验将提供基本的机械原理 洞察刺激如何影响大脑，以及如何设计它来实现最佳治疗 在个体大脑中的结果。