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: | Andrew Chen |
| Institution: | University of Pennsylvania |
| Department: | Department of Biostatistics |
| Country: | |
| Proposed Analysis: | Dimension reduction tools preserving similarity and graph structure can capture biological patterns in complex high-dimensional data. However, these tools cannot separate these patterns from unwanted effects due to confounders. We introduce the partial embedding (PARE) framework, which enables removal of confounders from any distance-based dimension reduction tool. We then develop partial $t$-SNE and partial UMAP, which effectively recover patterns in genomic and neuroimaging measures. We apply these methods to data from the Alzheimer's Disease Neuroimaging Initiative to isolate biological and technical variability. |
| Additional Investigators |
