Kimberly J. Hemmerling

The paper identifies a subgroup of individuals with autism characterized by elevated free-water levels and social difficulties using multimodal MRI clustering.

This paper evaluates a PCA-based denoising method for spinal cord fMRI using a noise ROI, finding that it may not consistently improve group-level activation maps compared to physiological recording-derived regressors.

This paper introduces an fMRI-based method to map spinal cord vascular reactivity, showing distinct gray matter regions associated with arterial supply territories.

The paper introduces EPISeg, a deep learning model for automated spinal cord segmentation on gradient-echo EPI data, showing improved performance over existing methods.

This paper develops methods to estimate cerebrovascular reactivity (CVR) amplitude and delay in breath-hold fMRI when PETCO2 data quality is low, using rescaled RVT and deep learning-predicted PETCO2.

This study investigates spinal cord fMRI activation during hand-grasping tasks, finding lateralized activity in C5-C8 with differences based on analysis methods and experimental parameters.

This study examines how variability in manually contoured spinal cord masks affects fMRI co-registration and activation mapping, finding that rater and dataset differences significantly impact spatial normalization and signal contrast.