Brain extraction, or skull-strip**, is an essential data preprocessing step for machine
learning approaches to brain MRI analysis. Currently, there are limited extraction algorithms …

Predictive modeling potentially increases the reproducibility and generalizability of
neuroimaging brain-phenotype associations. Yet, the evaluation of a model in another …

Structural and functional connectomes undergo rapid changes during the third trimester and
the first month of postnatal life. Despite progress, our understanding of the developmental …

The functional brain connectome is highly dynamic over time. However, how brain
connectome dynamics evolves during the third trimester of pregnancy and is associated with …

Recent work suggests that machine learning models predicting psychiatric treatment
outcomes based on clinical data may fail when applied to unharmonized samples …

Infancy is the time of the most rapid brain development and may be one of the most critical
times for psychiatric risk. During infancy, the human brain reaches peak synaptic density (1 …

Automated fetal brain extraction from full-uterus MRI is a challenging task due to variable
head sizes, orientations, complex anatomy, and prevalent artifacts. While deep-learning …

While strong associations of structural magnetic resonance imaging (sMRI) with preterm
birth and post-menstrual age (PMA) have been reported, such associations for functional …

Predictive modeling potentially increases the reproducibility and generalizability of
neuroimaging brain-phenotype associations. Yet, the evaluation of a model in another …

In recent days, fetal health care has become more precious to giving birth to a child. To
maintain the good health of the fetus, the mother needs proper observation and treatment …