Brain functional connectivity maps have been utilized to learn about differences of brain activation patterns between disease groups via clustering voxels based on their connectivity patters.
This project develops general frameworks for estimating associations of brain connectivity maps with predictors of interest after controlling for confounders using a statistical modeling approach that allows for using the special structure of covariance outcomes for improved parameter estimation.
An important contribution of this work is the extension of the model to high dimensional settings as the connectivity maps based on fMRI are often large. The proposed framework will be used to learn about functional organization of the brain during an adaptation learning task in a functional MRI study focusing on visual-motor connectivity changes during the task.