Drivers of contraceptive non-use among women and men who are trying to get pregnant.

This study examines an inconsistency between an attitude and a behaviour: non-use of contraception among people who are trying to get pregnant. More than one in four people in that situation report using contraception 'sometimes' or 'always' and consequently face the risk of pregnancy. We test three potential explanations: acceptability of having (further) children; perceived low pregnancy risk; and perceived social pressure.

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.

Mutations associated with neuropsychiatric conditions delineate functional brain connectivity dimensions contributing to autism and schizophrenia.

16p11.2 and 22q11.2 Copy Number Variants (CNVs) confer high risk for Autism Spectrum Disorder (ASD), schizophrenia (SZ), and Attention-Deficit-Hyperactivity-Disorder (ADHD), but their impact on functional connectivity (FC) remains unclear.

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.