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

基本信息

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
项目类别：
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&apos 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

项目摘要

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.

项目摘要每年，美国有795,000多人中风，目前约有470万幸存者。大约20％的幸存者对认知障碍和痴呆症产生血管贡献（VCID）仅次于阿尔茨海默氏病（AD）。虽然已知一些推定的生物标志物，但在中风研究中，就VCID的生物标志物的验证和相互作用而言，存在很大的差距。有迫切需要更好地了解VCID风险因素，基线认知和大脑的复杂相互作用健康和事件中风病变伯恩在中风后大脑变化以及随后的VCID发展。该项目的具体目的将通过（1）使用新颖的社区来创新这一需求劣势地图地图地图地图上绘制并量化社会经济灾难，（2）量化血管风险负担，（3）结合基线大脑和认知健康，（4）利用最新的技术进步 Art Connectome MRI和（5）应用网络神经科学和机器学习。此外，我们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，可能会增加VCID风险的粗糙/城市，低/高SE。我们的中心假设是VCID风险因素，基线认知和大脑健康，事件中风损害以及中风后大脑的变化将会通过大脑灌注，结构和连通性途径共同起作用中风患者的发展VCID。我们将从 55-90岁患者的前瞻性队列，左侧（n = 50）或右（n = 50）出现缺血性中风（n = 50）脑动脉中部。我们可能会收集n = 50的数据，并回顾性地使用n = 100 用于匹配健康控制的AD Connectome项目。目标1（大脑变化）：表征相互作用的方式 VCID危险因素（例如心血管，人口统计学），基线脑健康和事件中风的程度损害会影响6个月时中风后大脑的变化。目标2（脑认知关系）：特征 VCID风险因素，基线认知，大脑，事件中风，中风后大脑之间的特定关系在5个认知领域的6个月和12个月中的变化和中风后认知功能在内功能，注意力，语言，记忆和视觉。我们将使用高级机器学习来构建预测模型将识别与后冲后相关的贡献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科学理解 VCID风险因素，中风MRI生物标志物及其相互作用之间的复杂生物学关系这是缺血性中风后认知结果的生物学的基础。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，可以帮助由于缺血性中风VCID，积极改变了疾病进展并减少患者的疾病烧伤。