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: | Xuan Wang |
| Institution: | The university of Texas Rio Grande Valley |
| Department: | Information Systems |
| Country: | |
| Proposed Analysis: | This study proposes a novel approach by integrating deep learning and causal machine learning techniques to understand the underlying mechanisms, identify potential biomarkers, and predict the onset and progression of Alzheimer's Disease. During preprocessing, data cleaning, feature selection, and normalization will be performed to prepare the data for deep learning algorithms. Convolutional Neural Networks, Recurrent Neural Networks, and Long Short-Term Memory networks will be used to analyze neuroimaging data and clinical data. The deep learning models will be employed to identify the most relevant biomarkers, including imaging-based, genetic, and clinical features. Propensity score matching, inverse probability weighting, and difference-in-differences will be used to identify the causal relationships among the identified biomarkers, risk factors, and AD progression. Additionally, staged predictive models will be developed for AD onset and progression. |
| Additional Investigators |
