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: | Nicholas McCrory |
| Institution: | Duke University |
| Department: | Radiology |
| Country: | |
| Proposed Analysis: | The primary objective is to explore candidate functional imaging biomarkers, namely the Network Failure Quotient and its constituent elements, for significant differences among groups of cognitively normal and MCI ADNI 2 study participants cognitively categorized via two different methods. Thus, group-level differences in the functional imaging biomarkers of interest will be analyzed in a single cohort of patients under two different approaches to cognitive classification. The first method is that specified within the ADNI 2 study Procedures Manual. The alternative approach, developed by Edmonds, et. al., classifies subjects through a cluster analysis based on alternative comprehensive neuropsychological test data. The secondary objective is to ascertain a reliable threshold value for the NFQ and/or any significant constituent elements to differentiate cognitively normal from cognitively impaired individuals within the MCI/AD spectrum. Through this study, I hope to build upon advances in our understanding of functional imaging biomarkers and methods of cognitive classification within the context of MCI/AD. |
| Additional Investigators | |
| Investigator's Name: | Jeffrey Petrella |
| Proposed Analysis: | The primary objective is to explore candidate functional imaging biomarkers, namely the Network Failure Quotient and its constituent elements, for significant differences among groups of cognitively normal and MCI ADNI 2 study participants cognitively categorized via two different methods. Thus, group-level differences in the functional imaging biomarkers of interest will be analyzed in a single cohort of patients under two different approaches to cognitive classification. The first method is that specified within the ADNI 2 study Procedures Manual. The alternative approach, developed by Edmonds, et. al., classifies subjects through a cluster analysis based on alternative comprehensive neuropsychological test data. The secondary objective is to ascertain a reliable threshold value for the NFQ and/or any significant constituent elements to differentiate cognitively normal from cognitively impaired individuals within the MCI/AD spectrum. Through this study, I hope to build upon advances in our understanding of functional imaging biomarkers and methods of cognitive classification within the context of MCI/AD. |
