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: | qinghua han |
| Institution: | Yunnan university |
| Department: | Software engineering |
| Country: | |
| Proposed Analysis: | A long-standing question is how to best use brain morphometric and genetic data to distinguish Alzheimer's disease(AD) patients from cognitively normal (CN) subjects and to predict those who will progress from mild cognitive impairment (MCI) to AD, Some studies using machine learning methods for outcome prediction via integrating imaging and genomics data. Some studies directly applied conventional learning methods to the combined data sets and showed improved performances. Some studies developed new learning models to address various challenges, such as feature selection at a group level, and joint classification and regression. With a couple of successful attempts, NN models have started to attract attention in the field of brain imaging genomics. We propose a new deep neural network approach for integrating MRI and SNP data, which we believe will yield good results. We hope to test the performance of our model on ADNI data sets |
| Additional Investigators |
