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: | Gloria Menegaz |
| Institution: | University of Verona |
| Department: | Computer Science |
| Country: | |
| Proposed Analysis: | We target imaging genetics and we would like to investigate the link between multimodal MRI-derived phenotypes (brain microstructure (diffusion MRI), morphometry and connectivity (structural and functional) features) and genomics (SNPs, gene expression). We aim at building an open and flexible pipeline exploiting machine and deep learning (where applicable). This targets different conditions and pathologies thus we request the access to the different available datasets. Though, for the time being priority will be on AD. |
| Additional Investigators | |
| Investigator's Name: | Ilaria Boscolo Galazzo |
| Proposed Analysis: | Structural and functional brain connectivity and modeling the link with genomic information. |
| Investigator's Name: | Ilaria Boscolo Galazzo |
| Proposed Analysis: | Structural and functional brain connectivity and modeling the link with genomic information. |
| Investigator's Name: | Silvia Francesca Storti |
| Proposed Analysis: | Analysis of fMRI data and investigation of the link between functional connectivity, structural connectivity and genetics. |
| Investigator's Name: | Lorenza Brusini |
| Proposed Analysis: | Investigation of the link between structural connectivity and genetics, accounting for microstructure and relying on machine and deep learning. |
| Investigator's Name: | Federica Cruciani |
| Proposed Analysis: | Investigation of the link between structural connectivity and genetics, accounting for microstructure and relying on machine and deep learning. |
| Investigator's Name: | Walter Riviera |
| Proposed Analysis: | Investigation of the link between genetic and MRI-based features thorough linear and nonlinear models including deep learning. |
