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: | Seyedemad Zolhavarieh |
| Institution: | Sharif University of Technology |
| Department: | Computer Engineering |
| Country: | |
| Proposed Analysis: | As a researcher at the Sharif University of Technology, my proposed analysis of the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset aims to investigate the relationship between brain network connectivity, cognitive function, and the progression of Alzheimer's disease (AD). The methodology for this analysis would begin with the extraction of functional MRI data to calculate measures of brain network connectivity, such as resting-state functional connectivity or dynamic functional connectivity. This information would explore differences in connectivity between healthy individuals, those with mild cognitive impairment (MCI), and those with AD. Next, cognitive test scores available in the dataset would be used to assess the relationship between these connectivity measures and cognitive function. Further investigation would be conducted to identify whether certain brain networks are more strongly associated with specific cognitive domains. To investigate the predictive value of these measures, this analysis would examine the relationship between baseline connectivity and the rate of disease progression in individuals with MCI and AD. Specifically, statistical methods would be used to model the association between baseline connectivity measures and the likelihood of conversion from MCI to AD, as well as the rate of cognitive decline in those with AD. Additionally, genetic information available in the dataset would be considered in this analysis to explore the potential role of genetic factors in the relationship between brain network connectivity, cognitive function, and the progression of AD. The findings of this study could contribute to a deeper understanding of the neural mechanisms underlying AD and inform the development of early intervention strategies. This analysis could also help identify neuroimaging markers that may aid in the early diagnosis and prediction of AD and monitoring of disease progression. |
| Additional Investigators |
