Collaborative Research: Extreme Mechanics of the Human Brain via Integrated In Vivo and Ex Vivo Mechanical Experiments

合作研究：通过体内和离体综合力学实验研究人脑的极限力学

• 批准号: 2331294
• 负责人: Kshitiz Upadhyay
• 金额: $ 37.02万
• 依托单位: Louisiana State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-04-01 至 2027-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2331294&HistoricalAwards=false
• 关键词: Collaborative; Research; Extreme; Mechanics; Human

The human brain exhibits complex mechanical behavior. Its deformation under external forces depends on the extent and speed of loading. Rapid deformation of the brain during events such as blasts and automotive crashes can cause traumatic brain injury. Understanding the mechanical behavior of the human brain under such extreme conditions is critical to developing computer models for predicting brain injury. This knowledge is also needed to design safer personal protective equipment and brain injury management and prevention strategies. Unfortunately, the current understanding of the mechanical behavior of living humans' brains is restricted to small deformations and a narrow range of loading rates that do not represent the full spectrum of injury-causing conditions. This award supports fundamental research combining high-rate mechanical testing, analytical and computational modeling, and machine learning to generate insights into how the living human brain responds to large and rapid loading. Results from this research will positively impact U.S. national health and welfare and will contribute to the fields of tissue mechanics, traumatic brain injury, and machine learning. This project will lead to new courses and involve contributions from underrepresented minorities.The overarching goal of this research is to understand the high strain rate mechanics of the brain in its native biophysical environment. The first stage will focus on tissue responses under small deformations and dynamic strain rates. Wide-band Magnetic Resonance Elastography experiments will be conducted on brain tissue specimens from multiple brain regions to develop linear viscoelastic constitutive models. Multi-fidelity models will be developed to fuse the observed responses with available narrow-band in vivo brain tissue responses for predicting linear viscoelastic properties of the in vivo brain tissue in a wide range of loading frequencies. The second stage will focus on tissue responses under large deformations and extreme strain rates. Quasi-static and dynamic mechanical testing will be conducted to develop visco-hyperelastic constitutive models. Physics-informed multi-fidelity models will be developed to fuse the ex vivo visco-hyperelastic responses with the in vivo linear viscoelastic responses characterized in the previous stage. This study will significantly advance our understanding of brain biomechanics by generating insights into the relationship between in vivo and ex vivo tissue mechanics and the first-ever full-field maps of the living brain’s mechanical properties applicable under extreme loading conditions.This project is jointly funded by the Biomechanics and Mechanobiology (BMMB) program and the Established Program to Stimulate Competitive Research (EPSCoR).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.

人脑在外力作用下表现出复杂的机械行为，这取决于爆炸和汽车碰撞等事件中大脑的快速变形，了解人脑在外力作用下的机械行为。这种极端条件对于开发预测脑损伤的计算机模型至关重要，也需要这些知识来设计更安全的个人防护设备以及脑损伤管理和预防策略。不幸的是，目前对活人大脑机械行为的理解仅限于。小变形和窄该奖项支持将高速机械测试、分析和计算建模以及机器学习相结合的基础研究，以深入了解活人大脑如何应对大的和造成伤害的情况。这项研究的结果将对美国国民健康和福利产生积极影响，并将为组织力学、创伤性脑损伤和机器学习领域做出贡献。该项目将带来新课程，并涉及代表性不足的少数群体的贡献。这项研究的目的是为了了解第一阶段将重点研究小变形下的组织反应和动态应变率，将对来自多个大脑区域的脑组织样本进行宽带磁共振弹性成像实验。将开发多保真度模型，将观察到的响应与可用的窄带体内脑组织响应相融合，以预测体内脑组织在各种加载频率下的线性粘弹性特性。将重点关注大变形和极端应变率下的组织响应，以开发粘性超弹本构模型，以将离体粘性超弹响应与融合。这项研究将通过深入了解体内和离体组织力学之间的关系以及有史以来第一个全场图来显着增进我们对前一阶段特征的体内线性粘弹性响应的理解。活体大脑在极端负载条件下的机械特性。该项目由生物力学和机械生物学 (BMMB) 计划和刺激竞争性研究既定计划 (EPSCoR) 联合资助。该奖项是 NSF 的法定使命，并被认为值得通过以下方式支持：使用基金会的智力价值和更广泛的影响审查标准进行评估。