ADNI data is made available to researchers around the world. As such, there are many active research projects accessing and applying the shared ADNI data. To further encourage Alzheimer’s disease research collaboration, and to help prevent duplicate efforts, the list below shows the specific research focus of the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Xiangliang Zhang |
| Institution: | King Abdullah University of Science and Technology (KAUST) |
| Department: | Computer, Electrical and Mathematical Sciences and |
| Country: | |
| Proposed Analysis: | My research domain is machine learning and data mining. We propose to develop novel multi-task learning techniques to predict the Alzheimer’s Disease progression, as it is important to understand how the disease progresses and identify related pathological biomarkers for the progression. Multi-task learning utilizes the intrinsic relationships among multiple related tasks in order to improve the generalization prediction performance. It identifies how the tasks are related and builds learning models to capture such task relatedness. We intend to analyze the ADNI data, predicting the disease status measured by clinical scores, such as Mini Mental State Examination (MMSE) and Alzheimer’s Disease Assessment Scale cognitive subscale (ADAS-Cog), which are the multi-task referred previously. |
| Additional Investigators |
