Stroke Connectome MRI biomarkers for VCID risk assessment

用于 VCID 风险评估的中风连接组 MRI 生物标志物

• 批准号: 10444411
• 负责人: Nagesh Adluru
• 金额: $ 73.69万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2027-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10444411
• 关键词: Address; Affect; African American population; Alzheimer' s Disease; Amyloid; Atlases; Attention; Biological; Biological Markers; Biology; Blood Vessels; Brain; Cardiovascular system; Categories; Chronic; Clinical; Clinical Research; Cognition; Cognitive; Complex; Data; Dementia; Development; Diagnosis; Diffusion Magnetic Resonance Imaging; Disease; Disease Progression; Distal; Foundations; Functional Magnetic Resonance Imaging; Future; Health; Hispanic Populations; Image; Impaired cognition; Impairment; Ischemic Stroke; Language; Left; Lesion; Life; Location; Machine Learning; Magnetic Resonance Imaging; Maps; Mediating; Memory; Microvascular Dysfunction; Minority Groups; Modeling; Monitor; Native Americans; Neuropsychology; Neurosciences; Outcome; Pathway interactions; Patient Recruitments; Patients; Pattern; Perfusion; Persons; Predisposition; Prognosis; Prospective cohort; Research; Research Project Grants; Rest; Risk; Risk Assessment; Risk Factors; Rural; Stroke; Structure; Survivors; Underrepresented Minority; United States; Validation; Visuospatial; White Matter Hyperintensity; accurate diagnosis; base; brain health; burden of illness; cerebral atrophy; clinical care; cognitive function; connectome; dementia risk; demographics; diagnostic tool; executive function; hypoperfusion; improved; innovation; machine learning method; magnetic resonance imaging biomarker; middle cerebral artery; neighborhood disadvantage; neural network; novel; post stroke; predictive modeling; prognostic tool; prospective; socioeconomic disadvantage; standard of care; stroke patient; stroke survivor; tool; vascular cognitive impairment and dementia; white matter

PROJECT SUMMARY Every year, more than 795,000 people in the United States have a stroke, with currently around 4.7 million survivors. Approximately 20% of survivors develop vascular contributions to cognitive impairments and dementia (VCID) which is second only to Alzheimer’s disease (AD). While several putative biomarkers are known, a considerable gap exists in stroke research in terms of validation and interaction of biomarkers of VCID. There is a critical need to better understand the complex interactions of VCID risk factors, baseline cognitive and brain health, and incident stroke lesion burden on post stroke brain changes and subsequent development of VCID. The specific aims of this project will address this need innovatively by (1) utilizing a novel neighborhood disadvantage atlas to geo-spatially map and quantify socio-economic disadvantage, (2) quantifying vascular risk burden, (3) incorporating baseline brain and cognitive health, (4) leveraging technical advances in state-of-the- art connectome MRI, and (5) applying network neuroscience and machine learning. In addition, we will recruit participants from underrepresented minority groups (African Americans, Hispanics, Native Americans), rural/urban, low/high SES who might be at increased risk for VCID. Our central hypothesis is that VCID risk factors, baseline cognitive and brain health, incident stroke damage, and post stroke brain changes will act in concert through brain perfusion, structure, and connectivity pathways in determining whether a stroke patient develops VCID. We will collect longitudinal connectome MRI and Neuropsychological data from a prospective cohort of patients 55-90 years old with incident ischemic stroke in the left (n=50) or right (n=50) middle cerebral artery territory. We will prospectively collect data on n=50 and retrospectively use n=100 from AD connectome project for matched healthy controls. Aim 1 (Brain changes): Characterize how the interaction of VCID risk factors (e.g., cardiovascular, demographics), baseline brain health and the extent of incident stroke damage will affect post stroke brain changes at 6 months. Aim 2 (Brain-cognition relationships): Characterize specific relationships between VCID risk factors, baseline cognition, brain, incident stroke, post stroke brain changes and post stroke cognitive function at 6 and 12-months across 5 cognitive domains including executive function, attention, language, memory and visuospatial. We will use advanced machine learning to build predictive models that will identify contributory and deleterious brain changes associated with post stroke cognitive outcomes. Successful completion of the project will provide currently lacking scientific understanding of the intricate biological relationships between VCID risk factors, stroke MRI biomarkers, and their interactions, that underlie the biology of cognitive outcomes after an ischemic stroke. The results will lay a strong foundation for building accurate diagnosis, prognosis, disease monitoring tools, and future clinical studies that can aid in positively altering disease progression and reducing illness burden on patients due to post ischemic stroke VCID.

项目概要 美国每年有超过 795,000 人中风，目前约有 470 万人 幸存者。大约 20% 的幸存者出现血管性认知障碍和痴呆症 （VCID）仅次于阿尔茨海默病（AD）。虽然已知有几种假定的生物标志物， 中风研究在 VCID 生物标志物的验证和相互作用方面存在相当大的差距。有 迫切需要更好地了解 VCID 风险因素、基线认知和大脑之间的复杂相互作用 健康状况和中风事件损伤对中风后大脑变化和 VCID 后续发展的影响。 该项目的具体目标将通过以下方式创新性地满足这一需求：(1) 利用一个新颖的社区 用于地理空间地图和量化社会经济劣势的劣势图集，(2) 量化血管风险 负担，（3）纳入基线大脑和认知健康，（4）利用最新的技术进步 艺术连接组 MRI，以及 (5) 应用网络神经科学和机器学习。此外，我们还将招募 来自代表性不足的少数群体（非裔美国人、西班牙裔、美洲原住民）的参与者， 农村/城市、低/高 SES 的人可能面临 VCID 风险增加。我们的中心假设是 VCID 风险 因素、基线认知和大脑健康、中风损伤和中风后大脑变化将 通过大脑灌注、结构和连接通路协同作用，以确定是否 中风患者出现 VCID。我们将收集纵向连接体 MRI 和神经心理学数据 55-90 岁左侧（n=50）或右侧（n=50）发生缺血性卒中患者的前瞻性队列 大脑中动脉区域。我们将前瞻性地收集 n=50 的数据，并回顾性地使用 n=100 用于匹配健康对照的 AD 连接组项目。目标 1（大脑变化）：描述交互方式 VCID 风险因素（例如心血管、人口统计）、基线大脑健康状况和中风事件的程度 损伤将影响中风后 6 个月时大脑的变化。目标 2（大脑与认知的关系）：表征 VCID 危险因素、基线认知、大脑、中风事件、中风后大脑之间的具体关系 6 个月和 12 个月时 5 个认知领域（包括执行能力）的变化和中风后认知功能 功能、注意力、语言、记忆和视觉空间。我们将使用先进的机器学习来构建 预测模型将识别与中风后相关的大脑变化 认知结果。该项目的成功完成将提供目前缺乏的科学认识 VCID 危险因素、中风 MRI 生物标志物及其相互作用之间复杂的生物学关系， 这是缺血性中风后认知结果的生物学基础。结果将奠定坚实的基础 用于构建准确的诊断、预后、疾病监测工具和未来的临床研究，以帮助 积极改变疾病进展并减轻缺血性中风后 VCID 患者的疾病负担。