Stroke Connectome MRI biomarkers for VCID risk assessment

项目摘要 美国每年有超过79.5万人中风，目前约有470万人 幸存者大约20%的幸存者发展为认知障碍和痴呆的血管贡献 （VCID），其仅次于阿尔茨海默病（AD）。虽然已知几种推定的生物标志物， 在VCID生物标志物的验证和相互作用方面，卒中研究存在相当大的差距。有 迫切需要更好地了解VCID风险因素、基线认知和大脑功能之间的复杂相互作用， 健康和意外中风损伤对中风后脑变化和随后VCID发展的负担。 该项目的具体目标将通过以下创新方式满足这一需求：（1）利用一个新的社区 劣势地图集，以地理空间映射和量化社会经济劣势，（2）量化血管风险 负担，（3）纳入基线大脑和认知健康，（4）利用国家的技术进步， art连接体MRI;（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/高社会经济地位人群可能面临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，事件中风损害，和中风后大脑的变化将 通过脑灌注、结构和连接途径共同作用， 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）缺血性卒中患者的前瞻性队列 大脑中动脉区我们将前瞻性收集n=50的数据，并回顾性使用n=100的数据， 匹配的健康对照的AD连接体项目。目标1（大脑变化）：描述相互作用 VCID风险因素（例如，心血管，人口统计学），基线脑健康和中风事件的程度 损伤将影响中风后6个月的大脑变化。目标2（大脑-认知关系）：描述 VCID危险因素、基线认知、脑、卒中事件、卒中后脑之间的特定关系 6个月和12个月时5个认知领域（包括执行）的卒中后认知功能变化 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，将识别与卒中后相关的贡献性和有害的大脑变化 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、卒中MRI生物标志物及其相互作用之间错综复杂的生物学关系， 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 用于建立准确的诊断、预后、疾病监测工具和未来的临床研究， 积极地改变疾病进展并减少由于缺血性卒中后VCID而对患者造成的疾病负担。

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.