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.

项目总结