Jana Maria Hutter

This study uses in vivo tractography to examine neonatal white matter pathways related to hypothalamic and reward functions, linking early-life factors to later neurodevelopmental and obesity outcomes.

The paper presents a 3D digital atlas of the human fetal brain at 21 weeks gestation, revealing molecular signatures of cortical expansion linked to neurogenesis timing and evolutionary boundaries.

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.

The paper identifies a stable structural connectome fingerprint in preterm neonates that can distinguish individuals more effectively than functional connectomes.

This paper uses machine learning to predict neonatal brain age and clinical risk from structural connectomes, finding thalamocortical connections are key predictors of developmental outcomes.

The paper uses neonatal cortical connectivity data to predict 18-month developmental outcomes, finding that structural covariance networks predict social-emotional performance.

This study examines how gestational age at birth affects structural brain development in healthy term-born infants and links these changes to neurodevelopmental outcomes at 18 months.

This study examines how preterm birth affects cortical development in infants at term-equivalent age using MRI, finding altered microstructure and morphology with individual variability.

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.

The paper develops normative brain volumetric development curves for term-born infants and compares preterm infants to these curves, identifying individual deviations linked to clinical factors and neurocognitive outcomes.

The paper links cortical morphology at birth to gene expression patterns in the fetal brain and shows preterm birth disrupts this relationship.

The paper optimizes multi-shell diffusion MRI acquisition parameters using a data-driven approach, resulting in a specific protocol for neonatal imaging.

The paper develops cortical profiles in neonatal brains using MRI to track morphometric and microstructural changes, revealing how network similarity evolves over time.

This paper introduces a method for fetal diffusion MRI reconstruction with intensity correction, enabling detailed analysis of brain connectivity and microstructure in-utero.

The paper introduces a fully automated pipeline for neonatal cortical surface reconstruction from MRI, achieving high accuracy in 85% of cases.

A linearly scaling groupwise method jointly estimates mean brain shape and longitudinal change across 275 neonates (35 to 44 weeks post-menstrual age), yielding a temporally consistent morphological atlas.

This paper introduces a new regularization method for cortical surface alignment in brain imaging, improving the robustness and biological interpretability of multi-modal data alignment.