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: | Mohammad-Erfan Farhadieh |
| Institution: | University of Isfahan |
| Department: | Cell and Molecular Biology |
| Country: | |
| Proposed Analysis: | The proposed analysis will involve integrating and analyzing data from multiple sources to better understand the relationship between genetic variation, alternative splicing events, neuroimaging, and clinical variables in Alzheimer's disease (AD) patients. The first step will be to access a database of AD patients and their corresponding genetic and clinical data. This will include data on genetic variants and expression levels of the genes associated with AD, as well as clinical variables such as age, sex, and disease severity. Next, I will extract the list of alternative splicing events identified in my previous M.Sc. thesis and map them to the corresponding genes in the AD dataset. I will then perform correlation analysis to assess the relationship between the expression levels of the significant expression quantitative trait loci (eQTL) and the alternative splicing events. In addition, I will also extract neuroimaging data from the AD dataset, including MRI scans and other imaging modalities, and assess their relationship with the eQTL and alternative splicing events. This will involve performing voxel-wise analysis to identify brain regions that show significant correlations with the genetic and splicing variables. Finally, I will integrate all the data sources to identify potential biomarkers that may be useful for predicting disease progression and treatment response in AD patients. This will involve using machine learning algorithms to identify patterns and relationships between the genetic, splicing, clinical, and imaging variables, and developing predictive models based on these findings. Overall, this proposed analysis will provide a comprehensive and integrative approach to understanding the complex genetic and molecular mechanisms underlying AD, and may lead to the development of new biomarkers and therapeutic targets for this devastating disease. |
| Additional Investigators |
