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: | Pingkun Yan |
| Institution: | Rensselaer Polytechnic Institute |
| Department: | Biomedical Engineering |
| Country: | |
| Proposed Analysis: | Alzheimer’s disease (AD) poses a severe personal impact and economic burden to millions of people every year. AD presents intricate roots and marginally differentiable early symptomatology; thus, causing difficulties to accurately diagnose AD and to develop treatments that can stop progression on time. In this research, we set to obtain a clearer understanding of the genomic roots, their phenotypical expression, and the underlying mechanisms behind AD. The hypothesis underlying this research is that deep learning methods integrating brain imaging features and genetic mutation data will discover genotypes that have a phenotypical expression contributing toward AD development. By using deep learning methods, we expect to solve the high dimensionality challenge presented in the genetics domain and through learned representations from the multiple data modalities capture complex structures and interactions of the genetic mutations and imaging features with increased performance over the traditional machine learning and statistical methods. ADNI will allow us to complete our objectives as the availability and integration of a wide set of data modalities allows for establishing novel relations between the multimodal features, which in turn, leads to a more comprehensive understanding of the disease. |
| Additional Investigators |
