Advancing MR elastography to map mechanical signatures of key AD/ADRD processes

推进 MR 弹性成像以绘制关键 AD/ADRD 过程的机械特征

• 批准号: 10585119
• 负责人: Matthew Christopher Murphy
• 金额: $ 44.2万
• 依托单位: MAYO CLINIC ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-12-01 至 2027-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10585119
• 关键词: Affect; Aging; Alzheimer' s Disease; Alzheimer' s disease related dementia; Alzheimer’s disease biomarker; Amyloid; Amyloid deposition; Anatomy; Anisotropy; Atrophic; Biological; Biological Markers; Biomechanics; Brain; Buffers; Chemicals; Clinic; Clinical Trials; Cognition; Cognitive; Country; Coupled; Data; Data Discovery; Data Set; Dementia; Development; Diffusion Magnetic Resonance Imaging; Disease; Environment; Face; Functional disorder; Future; Goals; Health; Impaired cognition; Individual; Inflammatory Response; Knowledge; Learning; Link; Machine Learning; Magnetic Resonance Elastography; Maps; Measurement; Measures; Mechanics; Microglia; Modality; Modeling; Nerve Degeneration; Neurophysiology - biologic function; Neuropsychological Tests; Noise; Pathologic; Pathology; Patient Recruitments; Performance; Persons; Physiological Processes; Play; Positioning Attribute; Positron-Emission Tomography; Predisposition; Process; Prognosis; Relaxation; Reporter; Reporting; Research; Resolution; Role; Sampling; Signal Transduction; Structure; Techniques; Technology; Testing; Therapeutic; Translating; United States; White Matter Hyperintensity; Work; burden of illness; cardiometabolism; cell motility; cognitive function; cognitive performance; demographics; design; elastography; experimental study; follow-up; imaging modality; improved; in vivo; insight; magnetic field; mechanical properties; resilience; simulation; structural determinants; success; tau Proteins; tau aggregation; tool; vector; white matter

1 PROJECT SUMMARY 2 Alzheimer’s disease (AD) is the leading cause of dementia in the United States and its impact is only growing with 3 shifting demographics. The development of powerful biomarkers, measuring amyloid deposition, tau accumulation, 4 and neurodegeneration, has provided important insights into the pathophysiology of AD and AD-related dementias 5 (ADRD). Nonetheless, given the large variability across individuals, our understanding of the link between pathology 6 and cognitive dysfunction remains incomplete. Structural factors contribute significantly to this pathology-cognition 7 disconnect and are termed as “brain reserve.” There is a critical need for objective measures of reserve that will 8 improve the assessment of individual prognosis and guide therapy. 9 Brain biomechanics are an understudied structural feature of the brain, due in large part to the difficulty in measuring 10 relevant biomechanical states in vivo. Magnetic resonance elastography (MRE) is currently unmatched for 11 noninvasive measurement of brain mechanical properties. We have previously demonstrated that brain stiffness is 12 reduced due to AD, and our group and others have demonstrated in multiple studies that brain stiffness is a significant 13 reporter of cognitive function. However, previous studies face important limitations, namely technologies that were 14 optimized for reliability over resolution, and incomplete pathological assessment. Therefore, we will investigate two 15 aims with the overall goals to (1) advance MRE technology in order to (2) evaluate of the role of biomechanics in 16 brain reserve. 17 In Aim 1, we will optimize our machine learning-based MRE inversion framework by incorporating new a priori 18 information into the model that is specific to the brain. These advances to the model include the incorporation of 19 partial volume effects to reduce atrophy-related bias, and mechanical anisotropy to accurately model the coherent 20 structure of white matter tracts. Each advance will be tested in simulation and phantom experiments, and finally in 21 vivo for its ability to boost sensitivity to key AD/ADRD processes. 22 In Aim 2, we will use these tools to simultaneously map the mechanical signature of 4 pathophysiological processes 23 including amyloid, tau, white matter hyperintensities, and cardiometabolic conditions. Using first a discovery data set, 24 we will extract the mechanical feature that best reports cognitive performance, both globally and in specific domains. 25 These MRE-based features will then be evaluated in an independent test set for their ability to predict concurrent and 26 future cognitive performance. Finally, we will assess the unique value of mechanical biomarkers to predict cognitive 27 performance, using a parallel analysis but controlling for existing biomarkers derived from anatomical, functional, and 28 diffusion MRI. 29 In sum, the success of this proposal will shed new light on alterations to brain biomechanics with respect to 30 AD/ADRD processes, and their role as a buffer between pathology and cognition.

1个项目摘要 2阿尔茨海默病(AD)是美国痴呆症的主要原因，其影响只会随着 3.人口结构的变化。强大的生物标志物的开发，测量淀粉样蛋白沉积，tau积累， 4和神经退行性变，为AD和AD相关痴呆的病理生理学提供了重要的见解 5(ADRD)。尽管如此，考虑到个体之间的巨大差异，我们对病理之间的联系的理解 6认知功能障碍仍不完全。结构性因素对这种病理-认知有很大贡献 7断线，被称为“大脑储备”。迫切需要客观的储备措施，以便 8改进个体预后评估，指导治疗。 9大脑生物力学是大脑的一个未被充分研究的结构特征，这在很大程度上是由于测量的困难。 体内相关的10种生物力学状态。磁共振弹性成像(MRE)目前无与伦比的 11脑机械特性的无创性测量。我们之前已经证明，大脑僵硬是 12由于阿尔茨海默病，我们的团队和其他人在多项研究中证明，大脑僵硬是一个显著的 13、认知功能报告人。然而，以前的研究面临着重要的限制，即技术 14优化的可靠性高于分辨率，以及不完整的病理评估。因此，我们将调查两个 15个目标，总体目标是(1)推进磁共振血管造影技术，以便(2)评估生物力学在 脑力储备16人。 17在目标1中，我们将通过引入新的先验知识来优化基于机器学习的MRE反演框架 18条信息输入特定于大脑的模型。这些对模型的改进包括纳入 19部分体积效应用于减少与萎缩相关的偏差，以及机械各向异性以精确地模拟相干 20白质束结构。每一项进步都将在模拟和模型实验中进行测试，并最终在 21 vivo有能力提高对AD/ADRD关键过程的敏感性。 22在目标2中，我们将使用这些工具同时映射4个病理生理过程的机械特征 23包括淀粉样蛋白、tau蛋白、白质高信号和心脏代谢状况。首先使用发现数据集， 24我们将提取最能反映全球和特定领域认知表现的机械特征。 这些基于MRE的功能随后将在独立的测试集中进行评估，以确定其预测并发和 26未来认知表现。最后，我们将评估机械生物标志物对预测认知能力的独特价值 27性能，使用平行分析，但控制来自解剖、功能和 弥散磁共振28例。 总而言之，这项提议的成功将为大脑生物力学方面的改变提供新的线索。 30 AD/ADRD过程，以及它们作为病理和认知之间的缓冲区的作用。