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: | Nicolai Hans |
| Institution: | Humboldt University |
| Department: | Applied Statistics |
| Country: | |
| Proposed Analysis: | We are aiming at implementing a boosting algorithm in the context of multivariate copula regression. Our field of application is medicine and we are especially interested in high-dimensional data, because the new method allows for automated variable selection. Via the two articles 'Bayesian multitask learning regression for heterogeneous patient cohorts' (Goncalves et al, 2019) and 'Modeling Alzheimer's disease cognitive scores using multi-task sparse group lasso' (Liu et al, 2018) we became aware of one of your datasets that might suit our regression setting very well, namely multivariate responses (five cognitive test scores) and a high number of possibly important features (cortical reconstruction and volumetric segmentation of MRI images (processed by a team from the University of California at San Francisco)). We want to use the dataset to figure out and quantify important drivers of the cognitive status of individuals and to explain possible relations among the different cognitive test scores. |
| Additional Investigators | |
| Investigator's Name: | Nadja Klein |
| Proposed Analysis: | We are aiming at implementing a boosting algorithm in the context of multivariate copula regression. Our field of application is medicine and we are especially interested in high-dimensional data, because the new method allows for automated variable selection. Via the two articles 'Bayesian multitask learning regression for heterogeneous patient cohorts' (Goncalves et al, 2019) and 'Modeling Alzheimer's disease cognitive scores using multi-task sparse group lasso' (Liu et al, 2018) we became aware of one of your datasets that might suit our regression setting very well, namely multivariate responses (five cognitive test scores) and a high number of possibly important features (cortical reconstruction and volumetric segmentation of MRI images (processed by a team from the University of California at San Francisco)). We want to use the dataset to figure out and quantify important drivers of the cognitive status of individuals and to explain possible relations among the different cognitive test scores. |
