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: | Prabha Siddarth |
| Institution: | UCLA |
| Department: | Semel Institute |
| Country: | |
| Proposed Analysis: | Mild Cognitive Impairment (MCI) is a risk factor for developing Alzheimer’s dementia (AD), but not all MCI patients progress to AD. We aim to develop an algorithm using machine learning techniques that will differentiate MCI progressing to AD from stable MCI. Machine learning algorithms generate a function that maps an input (a set of predictors) to a desired output (MCI to AD conversion) by training a dataset until a desired level of accuracy is achieved. This model can then be applied to new cases where the outcome is unknown. Previous studies of MCI-to-AD conversion utilizing machine learning techniques have either used small datasets; not been tested in independent samples; required hard to acquire or expensive inputs; or yielded limited predictive accuracy. The large cohort available from ADNI offers a unique opportunity to develop and test an algorithm that can be used for accurate prediction of MCI to AD conversion. |
| Additional Investigators |
