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: | Lin Qiu |
| Institution: | Penn State University |
| Department: | Statistics |
| Country: | |
| Proposed Analysis: | As scientific data problems grow in terms of both expanding parameter dimension and sample sizes, dimension reduction and integrative analysis become central concepts in practical data analysis and inference. Recent advancements in high-throughput, biomedical technologies have enabled the measurement of multiple high-dimensional omics data types in a single study, including genomics, epigenomics, transcriptomics and metabolomics. Each of these data types provides a different snapshot of the underlying biological system, and combining multiple data types has been shown to be very valuable in investigating diseases. Individual components in these data are functionally structured in networks or pathways and incorporation of such structural information can improve analysis and lead to biologically more meaningful results. We are developing longitudinal deep learning models to study the hidden structure between different data sources from the study subjects/patients. We are particularly interested in the longitudinal brain image data, gene expression data, and clinical data. |
| Additional Investigators | |
| Investigator's Name: | Lin Lin |
| Proposed Analysis: | Dynamic deep learning model to explore the hidden structure between different types of data from the single study. |
