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: | Matthew Gunther |
| Institution: | Georgia Institute of Technology |
| Department: | Biomedical Engineering |
| Country: | |
| Proposed Analysis: | The goal of this project is to 1.Predict the Alzheimer’s disease (AD) status with brain MR images; 2.Predict the AD progression with MR images as well as the clinical and genomic data available. Compared to the prediction of AD status, the prediction of AD progression is much more challenging because we need to identify or capture biomarkers (either imaging or genomics) that are related to the AD development. These biomarkers are not well established compared to the diagnosis biomarkers. In this project, we are going to first use the MR data for the prediction of AD status. After implementing an AD status classification model, we can explore the prediction of AD progression with the longitudinal data available from ADNI. We can combine the MR imaging data with genomic data (e.g., SNPs) and clinical data (e.g., age, mental test, etc.). |
| Additional Investigators | |
| Investigator's Name: | Shreyans Mehta |
| Proposed Analysis: | The goal of this project is to 1.Predict the Alzheimer’s disease (AD) status with brain MR images; 2.Predict the AD progression with MR images as well as the clinical and genomic data available. Compared to the prediction of AD status, the prediction of AD progression is much more challenging because we need to identify or capture biomarkers (either imaging or genomics) that are related to the AD development. These biomarkers are not well established compared to the diagnosis biomarkers. In this project, we are going to first use the MR data for the prediction of AD status. After implementing an AD status classification model, we can explore the prediction of AD progression with the longitudinal data available from ADNI. We can combine the MR imaging data with genomic data (e.g., SNPs) and clinical data (e.g., age, mental test, etc.). Proposed Analysis Update |
