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: | Pingfan Song |
| Institution: | University of Cambridge |
| Department: | Engineering |
| Country: | |
| Proposed Analysis: | Trustworthy machine learning for early prediction of Alzheimer's Disease, including: * developing trustworthy machine learning approach for AD classification/stratification in order to distinguish stable vs. progressive MCI individuals using cognitive data (e.g. memory, executive function, affective measurements) or biological data (e.g. mean cortical β-amyloid burden, grey matter density, APOE 4). * developing trustworthy machine learning approach for trajectory modelling in order to predict individualised future cognitive decline rate to indicate the change in memory scores from baseline. |
| Additional Investigators |
