Alena U. Uus

This study uses 7 Tesla fMRI to examine BOLD responses in newborns and adults, showing depth-dependent changes in cortical activity related to development.

The paper describes the release of a large fetal fMRI dataset and presents methods for its preprocessing and analysis to study in utero brain connectivity.

The paper examines microstructural changes in fetal brain compartments before cortical folding begins, finding that anisotropy decreases in subplate and cortical plate regions prior to sulcal pit formation.

The paper shows that functional connectivity gradients in resting state networks are present in the human fetal brain from 25 weeks gestation, with spatial consistency increasing in primary sensory and motor networks.

This study uses MRI to compare brain volumes in neonates with Down syndrome to a control group, finding reduced whole brain volume in DS infants.

The paper introduces a deep learning pipeline for automated fetal brain segmentation and volumetry, showing reduced need for manual refinement and agreement with manual segmentations in clinical cases.

This study uses diffusion MRI to track thalamocortical pathway maturation in human fetal brains, identifying key neurobiological changes in the second to third trimester.

This paper proposes an automated pipeline for reconstructing fetal thorax MRI from motion-corrupted stacks across 21-36 weeks GA, using 3D CNNs and landmark-guided pose estimation.

The paper creates 3D MRI atlases of normal and abnormal fetal aortic arch anatomy and evaluates deep learning for automated multi-label segmentation.

This paper develops a multi-channel 4D atlas of neonatal brain development combining structural and diffusion MRI data, showing microstructural differences between term and preterm infants.

This paper applies unsupervised domain adaptation techniques to segment neonatal brain MRI data from a preterm population, improving segmentation accuracy and enabling analysis of cortical thickness and language outcomes.