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: | Ani Eloyan |
Institution: | Brown University |
Department: | Biostatistics |
Country: | |
Proposed Analysis: | The use of ADNI PET and MRI imaging data in prediction of AD diagnosis has been widely studied. We plan to investigate the associations of PET and MRI imaging with specific cognitive variables collected as part of ADNI to identify potential associations of imaging data with cognitive domains. We will start by using exploratory models to find associations of imaging based biomarkers provided by ADNI with cognitive scores. Then, we will develop deep convolutional neural networks to predict cognitive decline in the identified domains using the full 3D images or the 3D regions of interest that we select in the process of our exploratory analysis. |
Additional Investigators | |
Investigator's Name: | Yimo Zhang |
Proposed Analysis: | Yimo Zhang is a PhD student working with me on the main analysis that I proposed for the data. |
Investigator's Name: | Joanna Walsh |
Proposed Analysis: | Joanna is a research assistant helping me with the statistical analysis of the main project proposed for these data. |
Investigator's Name: | Alex Taurone |
Proposed Analysis: | Comparison of LEADS early-onset AD group to ADNI late onset participants in terms of cognitive testing. |
Investigator's Name: | Robert Zielinski |
Proposed Analysis: | Development of longitudinal principal manifold estimation methods for biomarker estimation from PET. |
Investigator's Name: | Maryanne Thangarajah |
Proposed Analysis: | Comparisons between ADNI late-onset and LEADS early-onset AD participants in terms of cognitive status and imaging biomarkers. |
Investigator's Name: | Akhil Ambekar |
Proposed Analysis: | Akhil will perform data preprocessing and preparation for implementing statistical models to estimate biomarkers from PET images. |