Maximilian Pietsch

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.

This paper introduces a new analytical framework called matnets to study fetal brain connectivity, revealing that high-level associative areas develop functional connections before birth.

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 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.

dStripe corrects slice artefacts in diffusion MRI using a constrained neural network to reduce interslice inconsistencies without biasing data.

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

MRtrix3 provides an open-source C++11 and Python software framework, command-line suite, and OpenGL 3.3 viewer for fast multi-dimensional medical image processing and diffusion MRI analysis.