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: | zhu xuyan |
| Institution: | 兰考三农职业学院 |
| Department: | 信息工程系 |
| Country: | |
| Proposed Analysis: | A method which can accurate segment areas of the brain associated with Alzheimer's disease is proposed．Firstly，a template which fits for all the samples is designed.Secondly，during the acquisition of MＲ features，we make full use of deformation field information from the registration of images． Then，in the processing of PET images，we pre-register them to the corresponding MＲ images． Finally，according to features of each cerebral area，the features are classified using support vector machine． The accuracy from the whole brain is: MＲ:0.8736，PET:0.9195，0.3 MＲ + 0.7PET:0.8621; from the gray matter is: MＲ:0.8736，PET:0.9195，0.3MＲ + 0.7PET:0.8621; and from the two sample t-test is: MＲ:0.8391，PET:0.9195，0.3MＲ + 0.7PET:0.8966． When the classification is carried out using cerebral cortical areas，the highest image accuracy in entorhinal cortex of MＲ is 0.8931，the highest image accuracy in precuneus of PET is 0.9195，and the highest image accuracy in entorhinal cortex of 0.3MＲ + 0.7PET is 0.9425． The experimental results show that compared with existing methods，this method can distinguish mild AD patient and normal elder more accurately，and which will help to prevent and early diagnose AD． |
| Additional Investigators |
