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: | Lixia Tian |
| Institution: | Beijing Jiaotong University |
| Department: | School of Computer Science |
| Country: | |
| Proposed Analysis: | I’m interest in: 1) Investigating the relationship between age-gap (difference between estimated age based on MRI data and actual age) and AD severity. Formerly we performed several studies on individualized age predictions and observed obvious age-gaps in some subjects, but when we correlated age-gap with several cognitive variables (e.g., IQ, ADHD score) across subjects, no significant correlation was observed. Hopefully, AD severity is closely related to age-gap. (related paper: Cao X, Chen C, Tian L. Supervised Multidimensional Scaling and its Application in MRI-Based Individual Age Predictions. Neuroinformatics, https://doi.org/10.1007/s12021-020-09476-6). 2) Analyzing the dynamic functional connectivity patterns in patients with AD and MCI. We formerly observed significant changes in dynamic functional patterns in the elderly. Hopefully, the dynamic functional connectivity patterns in the AD and MCI patients are different from those of the healthy elderly. (related paper: Tian L, Li Q, Wang C, Yu J. Changes in dynamic functional connections with aging. Neuroimage. 2018 May 15;172:31-39. doi: 10.1016/j.neuroimage.2018.01.040.) 3) Classifying ADHD/MCI patients from the controls. Some of my students are of machine learning background, and they may perform ADHD/MCI classification analyses based on the dataset. |
| Additional Investigators | |
| Investigator's Name: | Hong Chen |
| Proposed Analysis: | AD classification based on novel machine learning techniques |
