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: | Haixing Dai |
| Institution: | University of Georgia |
| Department: | Computer Science |
| Country: | |
| Proposed Analysis: | Introduction: Importance of an accurate graph for GCN, and in general, all graph-based analysis, covering: • Our hypothesis and observation that there exists a better graph (lower-dimensional manifold), comparing with a correlation-derived graph, where the imaging data resides on, that can lead to better representation of imaging data (in our case, PET in AD classification). • Our approach: searching a better graph based on GCN • Previous work on graph discovery from imaging (e.g., Bayesian network, partial correlation, etc.) • Previous work on optimization of a process, where we are using a greedy approach (e.g., dynamic programing, reinforcement learning) • Previous work on the importance of thresholding for brain networks study • Previous work on GCN application in medical imaging |
| Additional Investigators |
