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: | Yuxi Cai |
| Institution: | The University of Hong Kong |
| Department: | Statistics & Actuarial Science |
| Country: | |
| Proposed Analysis: | We formulate the widely-used convolutional block as a novel tensor regression form. The proposed method allows a more accurate and efficient feature extraction from multi-dimensional images. In particular, we can apply the method to the MRI data that are related to Alzheimer’s disease and use our regression method to detect the brain regions that are high associated with the Alzheimer’s disease. |
| Additional Investigators | |
| Investigator's Name: | Feiqing Huang |
| Proposed Analysis: | We formulate the widely-used convolutional block as a form of tensor regression, which enables a more accurate and efficient feature extraction. This can be applied to multi-dimensional image data. In particular, we would like to perform regression on the MRI data that are related to Alzheimer’s disease and investigate which brain regions are highly associated with the occurrence of the disease. |
| Investigator's Name: | Guodong Li |
| Proposed Analysis: | We formulate the widely-used convolutional block as a form of tensor regression, which enables a more accurate and efficient feature extraction. This can be applied to multi-dimensional image data. In particular, we would like to perform regression on the MRI data that are related to Alzheimer’s disease and investigate which brain regions are highly associated with the occurrence of the disease. |
