CAREER: Multiscale modeling of perivascular flow in the brain

职业：大脑血管周围血流的多尺度建模

• 批准号: 2143702
• 负责人: Jessica Shang
• 金额: $ 50.68万
• 依托单位: University of Rochester
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-12-15 至 2026-11-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2143702&HistoricalAwards=false
• 关键词: CAREER; Multiscale; modeling; perivascular; flow

Cognitive diseases such as Alzheimer's are linked to the buildup of metabolic waste in the brain. In healthy brains, cerebrospinal fluid transports the waste through perivascular spaces that surround the arteries. Perivascular flow is affected by changes in arterial properties and blood flow distribution in the brain, which may explain the correlation between vascular decline and cognitive degeneration. This project is focused on modeling the fluid networks of blood and cerebrospinal fluid throughout the brain to provide insight into the relationship between arterial and perivascular activity. Understanding the mechanics of fluid flow in the brain will advance capabilities to correlate age-related ailments with neurodegeneration, predict patient outcomes, and drive treatment and prevention strategies. Pharmaceutical, surgical, or lifestyle interventions may alter the mechanical properties of vessels and brain tissue to promote waste clearance. The project will contribute to science education by including high school students from the Rochester City School District in undergraduate-level research coursework emphasizing hands-on lab experience, in collaboration with the University of Rochester David T. Kearns Center. Experiments strongly correlate blood and perivascular flows, but a causal mechanism has not yet been established in either experiment or simulation. This project will develop a multiscale model that explicitly relates arterial function to perivascular flow throughout the brain with wave propagation models coupling the arterial and perivascular networks to obtain a system-wide understanding of pressure and flow in the brain’s perivascular spaces. This model will be able to calculate flow distributions anywhere in the brain, beyond regions that are experimentally accessible, and at a broader scale than previous computational studies. Mechanical parameters in the model such as stiffness or diameter will be manipulated to mimic vascular conditions that result from chronic hypertension (high blood pressure), or transient spikes in neural activity (increased blood flow) that occur over short and long time scales. Both conditions will be validated with experimental data generated by collaborators at the University of Rochester Medical Center. The propagation of these effects will give insight into the relative impacts of vascular effects on perivascular flow, and the modeling framework can be expanded to study a range of vascular conditions beyond those in this project.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

认知疾病，如阿尔茨海默氏症，与大脑中代谢废物的积累有关。在健康的大脑中，脑脊液通过动脉周围的血管周围空间运输废物。血管周围血流受脑内动脉特性和血流分布变化的影响，这可能解释了血管衰退与认知退化之间的相关性。该项目的重点是模拟整个大脑的血液和脑脊液的流体网络，以深入了解动脉和血管周围活动之间的关系。 了解大脑中液体流动的机制将提高将年龄相关疾病与神经退行性疾病联系起来、预测患者结果并推动治疗和预防策略的能力。药物、手术或生活方式干预可能会改变血管和脑组织的机械特性，以促进废物清除。该项目将有助于科学教育，包括罗切斯特市学区的高中学生在本科水平的研究课程，强调动手实验室的经验，与罗切斯特大学大卫T。卡恩斯中心。实验强烈相关的血液和血管周围的流动，但因果机制尚未建立在实验或模拟。该项目将开发一个多尺度模型，该模型将动脉功能与整个大脑的血管周围流量明确联系起来，并将动脉和血管周围网络耦合起来，以获得对大脑血管周围空间的压力和流量的全系统理解。该模型将能够计算大脑中任何地方的流量分布，超出实验可访问的区域，并且比以前的计算研究更广泛。 模型中的机械参数（如刚度或直径）将被操纵，以模拟慢性高血压（高血压）引起的血管状况，或在短时间和长时间尺度上发生的神经活动（血流量增加）的瞬时峰值。 这两种情况都将通过罗切斯特大学医学中心的合作者产生的实验数据进行验证。这些效应的传播将使我们深入了解血管效应对血管周围血流的相对影响，并且建模框架可以扩展到研究本项目之外的一系列血管条件。该奖项反映了NSF的法定使命，并且通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。