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: | Zahraa Abdallah |
| Institution: | University of Bristol |
| Department: | Engineering Mathematics |
| Country: | |
| Proposed Analysis: | My background and expertise are in machine learning, deep learning, and time series classification. This data is particularly interetsing because of the availability of various types of data; images, EHR, and SNP. Hence, my main aim in this research is to explore a multi-modality machine learning approach for the early diagnosis and detection of evolving symptoms. Various multi-modality architectures will be explored including early, intermediate, and late fusion. Part of this study will be focused on the interpretability of the models/features/results. In terms of genetic data, time-series features will be applied to extract the most important patterns/shapelets that contribute to various stages of the diseas |
| Additional Investigators | |
| Investigator's Name: | Mohammed Abdelsamea |
| Proposed Analysis: | Mohamed is an expert in analyzing medical images. His expertise will be a key contribution in the multi-modality architecture |
