NeoRS: A Neonatal Resting State fMRI Data Preprocessing Pipeline.

Resting state functional MRI (rsfMRI) has been shown to be a promising tool to study intrinsic brain functional connectivity and assess its integrity in cerebral development. In neonates, where functional MRI is limited to very few paradigms, rsfMRI was shown to be a relevant tool to explore regional interactions of brain networks. However, to identify the resting state networks, data needs to be carefully processed to reduce artifacts compromising the interpretation of results.

Using Deep Convolutional Neural Networks for Neonatal Brain Image Segmentation.

Introduction: Deep learning neural networks are especially potent at dealing with structured data, such as images and volumes. Both modified LiviaNET and HyperDense-Net performed well at a prior competition segmenting 6-month-old infant magnetic resonance images, but neonatal cerebral tissue type identification is challenging given its uniquely inverted tissue contrasts. The current study aims to evaluate the two architectures to segment neonatal brain tissue types at term equivalent age.

Recent advances in preclinical and clinical multimodal MR in the newborn brain.

Aside from injury identification, MRI of the newborn brain has given us insight into cortical and white matter development, identified windows of vulnerabilities, enabled the introduction of therapeutic hypothermia which has become the standard of care in neonatal asphyxia, and is fostering leapfrogging discoveries in the field of neuro-genetics. This article reviews the main advances in recent years in newborn brain imaging both in preclinical and clinical research.