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: | Xinhui Ma |
| Institution: | University of Hull |
| Department: | Computer Science |
| Country: | |
| Proposed Analysis: | The work aims to identify mild cognitive impairment (MCI) patients who have a high likelihood of developing Alzheimer's disease (AD) using novel deep learning methods. The deep learning method will combine structural magnetic resonance imaging (MRI), demographic, neuropsychological, and APOe4 genetic data as input measures. The deep learning methods will be based on dual learning and an ad hoc layer for 3D separable convolutions. The work will develop objective measures that are able to discriminate the MCI patients who are at risk of AD from those MCI patients who have less risk to develop AD, so that doctors can identify and choose effective and personalized strategies to prevent or slow the progression of AD. |
| Additional Investigators | |
| Investigator's Name: | Emma Wolverson |
| Proposed Analysis: | The work aims to identify mild cognitive impairment (MCI) patients who have a high likelihood of developing Alzheimer's disease (AD) using novel deep learning methods. The deep learning method will combine structural magnetic resonance imaging (MRI), demographic, neuropsychological, and APOe4 genetic data as input measures. The deep learning methods will be based on dual learning and an ad hoc layer for 3D separable convolutions. The work will develop objective measures that are able to discriminate the MCI patients who are at risk of AD from those MCI patients who have less risk to develop AD, so that doctors can identify and choose effective and personalized strategies to prevent or slow the progression of AD. |
