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: | Xianxian Long |
| Institution: | Center for Aging and Health Research, School of Public Health, Xiamen University |
| Department: | Public Health |
| Country: | |
| Proposed Analysis: | Dear ADNI database management team: I am a graduate student from Xiamen University, and I am very interested in the Alzheimer's Disease Neuroimaging Database (ADNI) project you are working on. As a graduate student focusing on the field of neuroimaging, I believe that the ADNI database is a great resource for my future research, and I am submitting my Proposed Analysis here for your approval. My research will focus on exploring screening tools for patients with Alzheimer's disease and their correlation with patient neurological function. I will use neuroimaging and clinical feature information from the ADNI database to construct differential diagnostic models based on machine learning algorithms and find correlates with model fit. Specifically, my Proposed Analysis will include the following steps: 1. Data processing: Pre-processing of patients from the ADNI database, including magnetic resonance imaging (MRI) and positional alignment scanning tomography (PET) exams from the National Library of Medical Imagery (NCMI). 2. Feature extraction: Extraction of features associated with Alzheimer's disease, including indicators of structural and functional aspects of the nervous system, such as brain area volume, cortical thickness, white matter connectivity, EEG, etc. 3. analysis of variance: to explore the differences between patients with Alzheimer's disease and normal control groups by methods such as one-way ANOVA and independent samples t-test. 4. machine learning model construction: multiple machine learning algorithms (e.g., support vector machines, decision trees, random forests, deep learning, etc.) were used to build Alzheimer's disease screening models, and the algorithms were evaluated, and the best algorithm was selected based on cross-validation and test set accuracy. 5. Correlation analysis: Use Pearson or Spearman correlation analysis to find factors associated with different features and screening tools, analyze their effects on Alzheimer's disease, and explore their mechanisms of action. 6. Interpretation of results: To interpret the correlation between screening tools and Alzheimer's disease based on the results of machine learning algorithms and correlated factors, and also, to try to provide a quick and accurate screening tool for Alzheimer's disease. I believe that my Proposed Analysis will be useful in the study of early screening tools for Alzheimer's disease and its association with neurological function in patients. If my Proposed Analysis is approved, I will conduct my research in an honest, rigorous, and responsible scientific manner in an effort to improve the accuracy and efficiency of Alzheimer's disease diagnosis and treatment, as well as to better contribute to the operation of the ADNI database. Thank you for taking the time to consider my request. Yours sincerely With best regards! Xianxian Long |
| Additional Investigators |
