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: | Lingjie Fan |
| Institution: | Sichuan University |
| Department: | College of computer science |
| Country: | |
| Proposed Analysis: | We intend to use this dataset to construct a multimodal model based on multimodal imaging information with biometric information based on machine learning. In this study, a sample of AD early diagnosis and MCI conversion risk prediction was selected from the TADPOLE database. These features include age, gender, year of education, APOe4 gene information, cognitive test scores, and MRI image features of the hippocampus, internal olfactory and middle temporal lobes. For the samples used in the AD early diagnosis task, the goal was to classify the disease status at baseline, including NC, MCI, and AD. for the samples used in the MCI conversion risk prediction task, the goal was to predict whether the MCI sample could be converted to AD within 0-36 months. table I presents basic information on AD early diagnosis and MCI conversion prediction for two different cohorts |
| Additional Investigators |
