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.

数百年来，已经对人的大脑进行了广泛的研究，但生物力学的作用仍然尚不清楚。然而，医学成像，实验和计算建模的最新进展导致越来越多的证据将人脑的生物力学与大脑发育，疾病和损害中的主要过程联系起来。脑生物力学使用应用力学方法建立了大脑的结构，功能和运动之间的关系。 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 disseased tissues, and may enable the early diagnostic and prevention of neurologic disorders, such as stroke, traumatic brain injury, and dementia.这样的项目有可能减少社会的财务燃烧，并提高数百万人的生活质量。将为科学和工程领域的代表性群体提供大脑力学中的外展活动，以及本科生和研究生的培训机会，以及博士后研究人员。该研究将为研究人类大脑在健康和疾病中的机制生物学提供一个新的平台。该研究团队将开发出一种与先进的神经图像型工具和多个脑图合并的新方法，以融合了多种多样的脑图，以融合了多种多样的脑图，以融入Viiv semimagy iniv iniv iniv iniv iniv iniv iniv iniv inviv大脑力学。超高磁场磁共振成像技术与自动成像模型集成合并在一起，将利用在不同的空间范围内对大脑力学的特定于主题投资。具体而言，将开发和集成到有限元元素和同几何分析的自动脑部分割和网格生成的超高分辨率机械，结构和连接神经影像学工具，以创建多尺度的大脑力学计算机模型。然后，这些工具将被用来对中风患者的减压颅骨切除术中的创伤性脑损伤的机械化学反应以及痴呆症患者样蛋白的进展与脑萎缩之间的耦合。通过为创建个性化的，数据驱动的大脑模型的创建管道，研究团队将展示组合成像，建模和机器学习技术的变革性能力，以更好地理解，改善治疗以及最终的神经系统疾病预防医学。该奖项颁发奖项，反映了NSF的法定任务，并通过评估了基金会的支持，反映了支持的概念，并概述了基础的支持。

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

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

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

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