Xi Luo

This paper develops a semiparametric method for estimating shared eigenvectors in multiple covariance matrices and applies it to identify brain networks in fMRI data.

This paper introduces a whole-brain regression method to identify individual and group variations in functional connectivity by linking covariates to matrix variations using group ICA and covariate assisted principal regression.

This paper introduces a method to regress covariance matrices on covariates and applies it to identify brain network changes related to age and sex.

This paper introduces TDCCA, a method for analyzing multilevel time series data, and shows it detects temporal changes and improves feature extraction in fMRI studies.