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: | Karin Shmueli |
| Institution: | UCL |
| Department: | Medical Physics and Biomedical Engineering |
| Country: | |
| Proposed Analysis: | Building on the work of Casamitjana et al. (MRI-Based Screening of Preclinical Alzheimer’s Disease for Prevention Clinical Trials, Journal of Alzheimer’s Disease, 64 (2018) 1099–1112) we propose to build a machine learning network and train it using the ADNI data to identify amyloid positive subjects. In this preliminary study, we propose to base the training on quantitatitive susceptibility maps (QSM) we will calculate from the T2*-weighted phase data acquired in ADNI. We will then then validate the network on independent (local) cohorts with similar MRI acquisitions (and PET and CSF tests). The goal is to assess whether using machine learning, together with QSM, can reduce the need for invasive PET and/or CSF testing for subject classification in the context of clinical trials of AD prevention. |
| Additional Investigators |
