Principal Investigator: Dr Jin-Moo Lee
Washington University in St. Louis, St. Louis, Missouri, USATags: 43782, cerebral-small-vessel-disease, genetics/genotyping, Machine Learning, MRI, stroke, white matter hyperintensity
Collaborator: Professor Paul Matthews, Imperial College London, London, UK
Cerebral small vessel disease (SVD) is a common and debilitating neurological disease affecting older populations causing 20-30% of ischemic stroke, 90% of intracerebral hemorrhage (ICH), and is the second leading cause of dementia after Alzheimer’s disease. However, the cause and progression of SVD remain poorly understood due to the wide spectrum of disease presentation and pathologies. The aim of our research is to utilize machine learning and genetics approaches towards brain MRI images and genetic data in the UK Biobank to improve understanding of the biologic mechanisms underlying different causes of SVD. Evidence suggest that the brain has location-specific vulnerability to different SVD pathologic processes. We propose a series of studies to analyze brain MRI data to identify imaging signatures for distinguishing between different SVD causes. We will then perform genetic analyses to understand the biological processes that give rise to different forms of SVD. We hope to understand how to better assess the risk of developing different forms of SVD, and how to improve the prevention, diagnosis, and treatment of SVD. Ultimately, we hope that results from this study will allow us to better treat SVD to reduce the occurrence of stroke and dementia in older populations. We estimate that this project will require 24 months, and may lead to identification of new genetic contributions or biologic pathways that contribute to development of SVD. This may help reveal underlying disease mechanisms and provide new insights into SVD disease process that may help guide development of new therapies or management options.