ADNI data is made available to researchers around the world. As such, there are many active research projects accessing and applying the shared ADNI data. To further encourage Alzheimer’s disease research collaboration, and to help prevent duplicate efforts, the list below shows the specific research focus of the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Russell Shinohara |
| Institution: | Johns Hopkins University |
| Department: | Biostatistics |
| Country: | |
| Proposed Analysis: | MRI data are commonly used both in clinical practice and in the study of disease etiology. However, these images are acquired in arbitrary units that are not interpretable and differ from subject to subject and between scanning sessions of the same subject. The normalization of these images is crucial for the analysis of MRI data and the study of diseases through MRI. The primary goal of this work is to study the normalization of magnetic resonance images (MRI) in a statistically principled framework. In particular, we will: 1. Develop new normalization techniques for studying populations of images 2. Compare these techniques to existing methods |
| Additional Investigators | |
| Investigator's Name: | Ciprian Crainiceanu |
| Proposed Analysis: | MRI data are commonly used both in clinical practice and in the study of disease etiology. However, these images are acquired in arbitrary units that are not interpretable and differ from subject to subject and between scanning sessions of the same subject. The normalization of these images is crucial for the analysis of MRI data and the study of diseases through MRI. The primary goal of this work is to study the normalization of magnetic resonance images (MRI) in a statistically principled framework. In particular, we will: 1. Develop new normalization techniques for studying populations of images 2. Compare these techniques to existing methods |
| Investigator's Name: | Elizabeth Sweeney |
| Proposed Analysis: | MRI data are commonly used both in clinical practice and in the study of disease etiology. However, these images are acquired in arbitrary units that are not interpretable and differ from subject to subject and between scanning sessions of the same subject. The normalization of these images is crucial for the analysis of MRI data and the study of diseases through MRI. The primary goal of this work is to study the normalization of magnetic resonance images (MRI) in a statistically principled framework. In particular, we will: 1. Develop new normalization techniques for studying populations of images 2. Compare these techniques to existing methods |