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: | Da Li |
| Institution: | University of Calgary |
| Department: | Mathematics and Statistics Department |
| Country: | |
| Proposed Analysis: | This research work is to conduct machine learning techniques to determine the stage of AD. Moreover, the long-term goal of this work is to construct a workflow through machine learning and GWAS technology to analyze the effects of human genes on brain MRI and AD stage. To achieve this goal, we carry out the following studies: - Design a new neural net model based on U-Net and traditional convolution neural network to achieve a better performance of AD detection. - Include a priori information such as imaging and AD stage to increase the machine learning model performance, e.g., accuracy and training speed. - Extract brain MRI imaging-derived phenotypes (IDPs) with a data-driven strategy by using machine learning technique. Then, the extracted IDPs will be accessed with the GWAS technique. |
| Additional Investigators |
