LEAP-HI: Tackling Brain Diseases with Mechanics: A Data-Driven Approach to Merge Advanced Neuroimaging and Multi-Physics Modeling

LEAP-HI：用力学解决脑部疾病：一种融合先进神经成像和多物理场建模的数据驱动方法

• 批准号: 2227232
• 负责人: Mehmet Kurt
• 金额: $ 200万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-15 至 2027-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2227232&HistoricalAwards=false
• 关键词: LEAP; HI; Tackling; Brain; Diseases

The human brain has been studied extensively for centuries, but the role of biomechanics remains mostly unknown. Recent advances in medical imaging, experimentation, and computational modeling, however, have led to a growing body of evidence linking biomechanics of the human brain with major processes in brain development, disease, and damage. Brain biomechanics establishes a relationship between the brain’s structure, function, and motion using the methods of applied mechanics. This Leading Engineering for America's Prosperity, Health, and Infrastructure (LEAP-HI) project combines novel medical imaging methods, image analysis, computational modeling, and mechanical testing to determine the fundamental mechanical properties of living brain tissues and the differences in properties between healthy and diseased tissues, and may enable the early diagnosis and prevention of neurological disorders, such as stroke, traumatic brain injury, and dementia. As such, the project has the potential to reduce the financial burden on society and increase the quality of life for millions of people. Outreach activities in brain mechanics will be provided for underrepresented groups in science and engineering, as well as training opportunities for undergraduate and graduate students, and postdoctoral researchers.The research will provide a novel platform for investigating the mechanobiology of the human brain in health and disease. The research team will develop a novel approach to merge advanced neuroimaging tools and multi-physics brain modeling into a semi-automated pipeline for the in vivo investigation of brain mechanics. Ultrahigh field magnetic resonance imaging technology merged with automated imaging-modeling integration will be utilized to enable the subject-specific investigation of brain mechanics across disparate spatio-temporal scales. Specifically, ultrahigh resolution mechanical, structural, and connectomic neuroimaging tools will be developed and integrated with automatic brain segmentation and mesh generation for finite element and isogeometric analysis to create multi-scale brain mechanics computer models. These tools will then be utilized to provide an in-depth characterization of the mechanobiochemical response of traumatic brain injury, in decompressive craniectomies for stroke patients, and the coupling between prion-like protein progression and cerebral atrophy in dementia. By developing a pipeline for the creation of personalized, data-driven brain models, the research team will demonstrate the transformative power of combined imaging, modeling, and machine learning techniques towards better understanding, improved treatment, and ultimately preventive medicine for neurological disorders.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.

人类的大脑已经被广泛研究了几个世纪，但生物力学的作用仍然是未知的。然而，最近在医学成像、实验和计算建模方面的进展，已经导致越来越多的证据将人类大脑的生物力学与大脑发育、疾病和损伤的主要过程联系起来。脑生物力学运用应用力学的方法建立了大脑的结构、功能和运动之间的关系。这个美国繁荣、健康和基础设施（LEAP-HI）项目结合了新颖的医学成像方法、图像分析、计算建模和力学测试，以确定活体脑组织的基本力学特性以及健康和病变组织之间的特性差异，并可能实现早期诊断和预防神经系统疾病，如中风、创伤性脑损伤和痴呆。因此，该项目有可能减轻社会的财政负担，提高数百万人的生活质量。将为科学和工程领域代表性不足的群体提供脑力学方面的拓展活动，并为本科生、研究生和博士后研究人员提供培训机会。这项研究将为研究人类大脑在健康和疾病中的机械生物学提供一个新的平台。该研究团队将开发一种新的方法，将先进的神经成像工具和多物理场脑建模合并到半自动化的脑力学体内研究管道中。超高场磁共振成像技术与自动成像建模集成将被用于跨不同时空尺度的脑力学研究。具体来说，将开发超高分辨率机械、结构和连接神经成像工具，并将其与用于有限元和等几何分析的自动脑分割和网格生成集成，以创建多尺度脑力学计算机模型。然后，这些工具将用于提供创伤性脑损伤的机械生化反应的深入表征，用于卒中患者的减压开颅手术，以及朊病毒样蛋白进展与痴呆症脑萎缩之间的耦合。通过开发创建个性化、数据驱动的大脑模型的管道，研究团队将展示结合成像、建模和机器学习技术的变革力量，以更好地理解、改进治疗，并最终预防神经系统疾病。该奖项反映了美国国家科学基金会的法定使命，并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。

项目成果

Isogeometric analysis-based physics-informed graph neural network for studying traffic jam in neurons

• DOI: 10.1016/j.cma.2022.115757
• 发表时间: 2023-01
• 期刊: Computer Methods in Applied Mechanics and Engineering
• 影响因子: 7.2
• 作者: Angran Li;Y. Zhang

Multifrequency Magnetic Resonance Elastography (MRE) at 7T

7T 多频磁共振弹性成像 (MRE)

• 发表时间: 2022
• 期刊: Proceedings of the 10th Annual BioMedical Engineering and Imaging Institute (BMEII
• 作者: Triolo, ER;Alipour, A;Khegai, O;Balchandani, P;Kurt, M
• 通讯作者: Kurt, M

Parameter Optimization for High-Resolution MR Elastography of the Human Brain at 7T

7T 人脑高分辨率 MR 弹性成像的参数优化

• 发表时间: 2022
• 期刊: Proceedings of the Joint Annual Meeting ISMRM-ESMRMB 2022
• 作者: Triolo, ER;Khegai, O;Veraart, J;Alipour, A;Hedden, T;Kurt, M;Balchandani, P
• 通讯作者: Balchandani, P

Exploring the multiphysics of the brain during development, aging, and in neurological diseases

探索大脑在发育、衰老和神经系统疾病过程中的多物理现象

• DOI: 10.1016/j.brain.2023.100068
• 发表时间: 2023
• 期刊: Brain Multiphysics
• 作者: Weickenmeier, Johannes

Frequency Response of the Human Brain Substructures During Helmeted Side Impacts

头盔侧面碰撞时人脑子结构的频率响应

• 发表时间: 2022
• 期刊: USNC/TAM2022
• 作者: Rezayaraghi, F.;Abderezaei, J.;Ozkaya, E.;Pionteck, A.;Stein, D.;Kurt, M
• 通讯作者: Kurt, M