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: | Minerva Carrasquillo |
| Institution: | Mayo Clinic |
| Department: | Neuroscience |
| Country: | |
| Proposed Analysis: | We propose to utilize ADNI genetic and neuroimaging data to obtain intracerebral hemorrhage GWAS results that can be used for validation of our cerebrovascular GWAS studies. |
| Additional Investigators | |
| Investigator's Name: | Joseph Reddy |
| Proposed Analysis: | We propose to utilize ADNI genetic and neuroimaging data to obtain intracerebral hemorrhage GWAS results that can be used for validation of our cerebrovascular GWAS studies. |
| Investigator's Name: | Nilufer Ertekin-Taner |
| Proposed Analysis: | We propose to utilize ADNI genetic and neuroimaging data to obtain intracerebral hemorrhage GWAS results that can be used for validation of our cerebrovascular GWAS studies. |
| Investigator's Name: | Mariet Allen |
| Proposed Analysis: | We propose to utilize ADNI genetic and neuroimaging data to obtain intracerebral hemorrhage GWAS results that can be used for validation of our cerebrovascular GWAS studies. |
| Investigator's Name: | Zachary Quicksall |
| Proposed Analysis: | We propose to utilize ADNI genetic and neuroimaging data to obtain intracerebral hemorrhage GWAS results that can be used for validation of our cerebrovascular GWAS studies. |
| Investigator's Name: | Xue Wang |
| Proposed Analysis: | We hypothesized there are distinct patterns of blood transcriptomics/proteomics alterations in Alzheimer’s (AD) patients in comparison to healthy controls. Further, we hypothesized there are AD biological subtypes that are associated with different AD phenotypes and/or cognitive scores, and that these subtypes might be identified from blood transcriptomics/proteomics patterns. To test these hypotheses, we will apply machine learning (ML) techniques to train blood transcriptomics/proteomics datasets and classify them into AD or control groups. In brief, we will select genes and use their transcriptomics/proteomics levels to train, validate and test ML models to classify AD and control samples. We will investigate the top contributing genes in the top (i.e. most accurate) ML models to identify possible transcriptomics/proteomics patterns that are different between AD and control samples. We will also apply ML techniques to group AD samples and further test if different AD groups have different AD phenotypes and/or cognitive scores. |
| Investigator's Name: | Cheng Zhang |
| Proposed Analysis: | To apply systems biology approaches to analyze these dataset for better understanding the network properties and complexities. |
| Investigator's Name: | Hu Li |
| Proposed Analysis: | To apply systems biology approaches to analyze these dataset for better understanding the network properties and complexities. |
