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: | liu qian |
| Institution: | Beijing Jiaotong University |
| Department: | Computer and Information Technology College |
| Country: | |
| Proposed Analysis: | Most existing methods of constr ucting dynamic functional connectivity (dFC) network obtain the connectivity strength between pairs of brain regions via the sliding window cor relation (SWC) method. However, SWC estimates the connectivity str ength at each time segment, rather than at each time point, and thus its performance is commonly affected by the window type, window width and step size, which is difficult to produce accurate dFC network. Besides, the deep learning methods have been employed to investigate the dFC for mild cognitive impairment (MCI) identification, while they may not capture the discriminative spatio-temporal patterns that are closely related to disease, thus impacting the performance of MCI identification. |
| Additional Investigators |
