There are many active research projects accessing and applying shared ADNI data. Use the search above to find specific research focuses on the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Jaeyoon Chung |
| Institution: | Boston University School of Medicine |
| Department: | Biomedical Genetics |
| Country: | |
| Proposed Analysis: | Cerebral small vessel disease (CSVD) describes the remodeling of brain microvessels due to hypertension, hypoperfusion, and other vascular factors. CSVD is known to increase the risk of stroke and dementia and to accelerate neurodegeneration in late onset Alzheimer's Disease (AD) during the prodromal stage. Amelioration of hypertension and other vascular risk factors lowers dementia risk by 5-20%, suggesting that understanding the underlying cerebrovascular contributions to AD could be utilized for therapeutic development. Furthermore, identification of individuals before the onset of significant clinical symptoms is essential for facilitating intervention when therapies may be most effective. Our goals are to 1) identify vascular-related genetic factors and mechanisms in AD using cross-phenotype analyses with CSVD traits and 2) estimate individual risk for developing the AD before the onset using machine learning with clinical traits and genetic risk scores (GRSs). Aim 1. Identify CSVD-mediated genetic risk loci for AD by conducting cross-phenotype genome-wide association studies (GWAS) using CSVD traits including Intracerebral Hemorrhage (ICH), Small Vessel Ischemic Stroke (SVS), White Matter Hyperintensities (WMH), and Cerebral Amyloid Angiopathy (CAA). Aim 2. Determine longitudinal associations for the individuals harboring the variants identified in aim 1 using secondary neurodegenerative traits including cognitive tests, neuro-imaging data (e.g. brain regional volume, cortex thickness, WMH, and microbleed counts), and Aβ and tau levels in CSF and PET scan to assess disease-relevant mechanisms and to identify pre-clinical markers of neurodegeneration linked to CSVD genetic risk. Aim 3. Develop a prediction model for AD risk before symptoms manifest using clinical traits (e.g. age, sex, ethnic, hypertension) and multiple GRSs of primary (e.g. disease status) and secondary traits of AD and CSVD using a machine learning approach. |
| Additional Investigators | |
| Investigator's Name: | Paul Crane |
| Proposed Analysis: | We will investigate classifying AD patients of ADNI into biologically relevant subgroups using network-based genetic risk score. Dr. Paul Crane group will share the AD subgroup information of ADNI with us. |
