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: | Zilu Wang |
| Institution: | Cornell University |
| Department: | Student |
| Country: | |
| Proposed Analysis: | In the paper "Machine learning based multi-modal prediction of future decline toward Alzheimer's disease: An empirical study," by Karaman, et al., the authors explore prediction of Alzheimer's disease in individuals through use of multi-modal learning models. In the paper, they tackled two problems. One is that most related literature focus on the prediction of mild cognitive impairment individual to individual with Alzheimer's, while this paper focuses on predicting cognitively normal individuals to mild cognitive impairment individuals and mild cognitive impairment individual to individual with Alzheimer's. Second is that most models predict in a single time horizon, while this paper introduces models that predict at any interval length. The authors concluded that magnetic resonance imaging (MRI) features, such as volumes of certain regions of the brain, are statistically significant for predicting the outcome for cognitively normal individuals. In this project, we will use deep learning models to extract features from the 3D MRI scans by integrating convolutional architecture into the model and measure the increase in prediction accuracy. |
| Additional Investigators |
