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: | Eric Ho |
| Institution: | UCLA |
| Department: | Education |
| Country: | |
| Proposed Analysis: | One important problem is how to monitor the progression of Alzheimer's Disease among patients, foremost among disadvantaged subpopulations of patients. With the requested data, we hope to introduce and validate progression maps with a view to monitoring patients and detecting patients who need more, and different medical support and intervention than other patients. The basic idea is to embed both patients and clinical items in a shared metric space. The fact that patients and items are embedded in the same metric space enables medical practitioners to assess how patients interact with items and how much progress patients make with respect to the illnesses measured by the items. An important advantage is that learning interaction and progression maps provide a simple and visual summary of patient healing or regression in a metric space (e.g., a low-dimensional Euclidean space), helping detect patients from underrepresented groups who need more, and different support than other patients. |
| Additional Investigators | |
| Investigator's Name: | Minjeong Jeon |
| Proposed Analysis: | One important problem is how to monitor the progression of Alzheimer's Disease among patients, foremost among disadvantaged subpopulations of patients. With the requested data, we hope to introduce and validate progression maps with a view to monitoring patients and detecting patients who need more, and different medical support and intervention than other patients. The basic idea is to embed both patients and clinical items in a shared metric space. The fact that patients and items are embedded in the same metric space enables medical practitioners to assess how patients interact with items and how much progress patients make with respect to the illnesses measured by the items. An important advantage is that learning interaction and progression maps provide a simple and visual summary of patient healing or regression in a metric space (e.g., a low-dimensional Euclidean space), helping detect patients from underrepresented groups who need more, and different support than other patients. |
