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: | Jonghun Lee |
| Institution: | Takeda pharmaceuticals |
| Department: | Genetics and Systems biology |
| Country: | |
| Proposed Analysis: | Alzheimer's disease is the most common type of dementia. Although there are several known biomarkers indicating disease onset and progression, there is still discrepancy between change of the markers with that of cognition and memory function. One of the reasons is heterogeneity of the disease pathologies and patients, thus, we aim to stratify patients based on fluid biomarkers and brain imaging data to understand and predict disease progression and involved pathology, and eventually find a new target of Alzheimer's disease. To be specific, we will 1) build machine learning models to predict disease progression and pathology of AD in several dataset (e.g. ADNI and AIBL), and 2) characterize longitudinal changes in biomarkers and volumetric features, Altogether this study will provide insights to understand disease’ and patients’ variability and contribute to development of more efficient therapies. |
| Additional Investigators | |
| Investigator's Name: | Jaeyoon Chung |
| Proposed Analysis: | Alzheimer's disease is the most common type of dementia. Although there are several known biomarkers indicating disease onset and progression, there is still discrepancy between change of the markers with that of cognition and memory function. One of the reasons is heterogeneity of the disease pathologies and patients, thus, we aim to stratify patients based on fluid biomarkers and brain imaging data to understand and predict disease progression and involved pathology, and eventually find a new target of Alzheimer's disease. To be specific, we will 1) build machine learning models to predict disease progression and pathology of AD in several dataset (e.g. ADNI and AIBL), and 2) characterize longitudinal changes in biomarkers and volumetric features, Altogether this study will provide insights to understand disease’ and patients’ variability and contribute to development of more efficient therapies. |
| Investigator's Name: | Daria Prilutsky |
| Proposed Analysis: | Alzheimer's disease is the most common type of dementia. Although there are several known biomarkers indicating disease onset and progression, there is still discrepancy between change of the markers with that of cognition and memory function. One of the reasons is heterogeneity of the disease pathologies and patients, thus, we aim to stratify patients based on fluid biomarkers and brain imaging data to understand and predict disease progression and involved pathology, and eventually find a new target of Alzheimer's disease. To be specific, we will 1) build machine learning models to predict disease progression and pathology of AD in several dataset (e.g. ADNI and AIBL), and 2) characterize longitudinal changes in biomarkers and volumetric features, Altogether this study will provide insights to understand disease’ and patients’ variability and contribute to development of more efficient therapies. |
| Investigator's Name: | Arthur Simen |
| Proposed Analysis: | ADNI data will be used to study endpoints, including psychometric properties, sensitivity to change and relationships to biomarkers to facilitate planning of future clinical trials. |
| Investigator's Name: | Brian Harel |
| Proposed Analysis: | ADNI data will be used to study endpoints, including psychometric properties, sensitivity to change and relationships to biomarkers to facilitate planning of future clinical trials. |
| Investigator's Name: | Robin Mogg |
| Proposed Analysis: | ADNI data will be used to study endpoints, including psychometric properties, sensitivity to change and relationships to biomarkers to facilitate planning of future clinical trials. |
