Yi Zhao

This study uses fast fMRI to analyze physiological oscillations of lateral ventricular volume in adults, finding age-related ventricular enlargement and increased fluctuation magnitude during drowsiness.

This paper develops a deep learning framework combining high-resolution MRI and spatial transcriptomics to create a whole-brain mouse cell type atlas at 10 micrometer resolution.

This study develops an automatic method to segment cerebral arteries and superior sagittal sinus from fMRI data, showing high reproducibility across age groups.

This paper introduces regression models for fMRI connectivity analysis that account for covariates like network labels and regional characteristics, showing they explain more variation than geographic or homotopic distances.

The paper analyzes the effect of transcranial direct-current stimulation on functional connectivity using distributional analysis of correlation densities in PPA patients.

The paper uses a novel covariance regression approach to identify functional brain networks associated with ADHD, age, sex, and response control in children.

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.