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: | Marco Palma |
| Institution: | University of Warwick |
| Department: | MRC Biostatistics Unit |
| Country: | |
| Proposed Analysis: | Improved inference for scalar-on-image regression, with application to brain age estimation. The aim of this research is to build a statistical model that provides estimates of brain age from 3-dimensional structural MR images. The potential differences between the patient's chronological age and her estimated brain age might then be used as an indication of healthy/unhealthy ageing process or neurodegenerative diseases. |
| Additional Investigators | |
| Investigator's Name: | Thomas Nichols |
| Proposed Analysis: | Quantifying uncertainty in brain-predicted age using scalar-on-image quantile regression. The aim of this research is to produce brain-derived predictions of subject age. The outcome of the statistical model proposed is not only a point prediction, but also a prediction interval which could take into account the uncertainty. These results could potentially inform about the individual ageing process or neurodegenerative diseases. |
| Investigator's Name: | Shahin Tavakoli |
| Proposed Analysis: | Quantifying uncertainty in brain-predicted age using scalar-on-image quantile regression. The aim of this research is to produce brain-derived predictions of subject age. The outcome of the statistical model proposed is not only a point prediction, but also a prediction interval which could take into account the uncertainty. These results could potentially inform about the individual ageing process or neurodegenerative diseases. |
| Investigator's Name: | Julia Brettschneider |
| Proposed Analysis: | Quantifying uncertainty in brain-predicted age using scalar-on-image quantile regression. The aim of this research is to produce brain-derived predictions of subject age. The outcome of the statistical model proposed is not only a point prediction, but also a prediction interval which could take into account the uncertainty. These results could potentially inform about the individual ageing process or neurodegenerative diseases. |
