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: | Alexander McLain |
| Institution: | University of South Carolina |
| Department: | Department of Epidemiology and Biostatistics |
| Country: | |
| Proposed Analysis: | I am a Biostatistician who is interesting in developing methods for the analysis of outcomes with high-dimensional predictors. Within this space, the current research I'm conducting is methods for high-dimensional mixed effects models which may contain interactions between imaging modalities and treatment or environmental variables. We have completed similar research in the normal linear regression model, but we are working towards developing models that are robust to the normality assumption. I do not have a particular target outcome of interest. Our research can consider any continuous outcomes that are of scientific interest, especially longitudinally measured outcomes. I will be interested in using genetic data and/or imaging data as the covariates (predictors). Again, longitudinal covariates will be of interest for some projects but are not a requirement. To start, we are interested in analyzing PET-imaging outcomes, e.g., FDG and AV45, and determining how whole-genome sequencing data contributes to these outcomes and which gene expression markers are predictive. In the future, our methods will explore characterizing what types of brains will lead to healthier brain function with age. For example, we would be interested in analyzing longitudinal outcomes from the MoCa battery (or similar) and seeing what areas of the brain are related to good outcome trajectories. |
| Additional Investigators |
