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: | Jennifer Harris |
| Institution: | The Norwegian Institute of Public Health |
| Department: | Center for Fertility and Health |
| Country: | |
| Proposed Analysis: | Analyses to be conducted: 1. EWAS analyses for differential methylation between each of the groups: Alzheimer disease (AD), Mild Cognitive Impairment (MCI) and Normal Control (CN) groups (reproduce, validate and expand the results of: “Harnessing peripheral DNA methylation differences in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to reveal novel biomarkers of disease” based on the ADNI data and own methylation data from Norwegian biobanks). 2. Use machine learning (mostly Weighted Gene Co-Expression Network Analysis combined with own methods) to identify patterns associated with AD and MCI, and hopefully also predictive of MCI transitioning to AD. Aims of the project: The aim of the study is to find causes of age-related diseases and create and test hypotheses to treat these. Additional aims are to detect early signs of age-related diseases and to describe the development, course, treatment effects, and prognosis of such diseases and aging, to identify patterns of DNA methylation and other molecular data that are strongly correlated with biological age, mortality risk and risk of developing specific age-associated diseases, particularly diseases that results in a high loss of life years, including dementias. |
| Additional Investigators | |
| Investigator's Name: | Arne Søraas |
| Proposed Analysis: | Each investigator will be working as a team on the same set of analyses (described above) and repeated here: 1. EWAS analyses for differential methylation between each of the groups: Alzheimer disease (AD), Mild Cognitive Impairment (MCI) and Normal Control (CN) groups (reproduce, validate and expand the results of: “Harnessing peripheral DNA methylation differences in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to reveal novel biomarkers of disease” based on the ADNI data and own methylation data from Norwegian biobanks). 2. Use machine learning (mostly Weighted Gene Co-Expression Network Analysis combined with own methods) to identify patterns associated with AD and MCI, and hopefully also predictive of MCI transitioning to AD. |
| Investigator's Name: | Karl Trygve Kalleberg |
| Proposed Analysis: | Each investigator will be working as a team on the same set of analyses (described above) and repeated here: 1. EWAS analyses for differential methylation between each of the groups: Alzheimer disease (AD), Mild Cognitive Impairment (MCI) and Normal Control (CN) groups (reproduce, validate and expand the results of: “Harnessing peripheral DNA methylation differences in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to reveal novel biomarkers of disease” based on the ADNI data and own methylation data from Norwegian biobanks). 2. Use machine learning (mostly Weighted Gene Co-Expression Network Analysis combined with own methods) to identify patterns associated with AD and MCI, and hopefully also predictive of MCI transitioning to AD. |
| Investigator's Name: | Espen Riskedal |
| Proposed Analysis: | Each investigator will be working as a team on the same set of analyses (described above) and repeated here: 1. EWAS analyses for differential methylation between each of the groups: Alzheimer disease (AD), Mild Cognitive Impairment (MCI) and Normal Control (CN) groups (reproduce, validate and expand the results of: “Harnessing peripheral DNA methylation differences in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to reveal novel biomarkers of disease” based on the ADNI data and own methylation data from Norwegian biobanks). 2. Use machine learning (mostly Weighted Gene Co-Expression Network Analysis combined with own methods) to identify patterns associated with AD and MCI, and hopefully also predictive of MCI transitioning to AD. |
