Publications by authors named "M Styner"

Background: Angelman syndrome (AS), a severe neurodevelopmental disorder resulting from the loss of the maternal UBE3A gene, is marked by changes in the brain's white matter (WM). The extent of WM abnormalities seems to correlate with the severity of clinical symptoms, but these deficits are still poorly characterized or understood. This study provides the first large-scale measurement of WM volume reduction in children with AS.

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Background: Down syndrome (DS) is the most common congenital neurodevelopmental disorder, present in about 1 in every 700 live births. Despite its prevalence, literature exploring the neurobiology underlying DS and how this neurobiology is related to behavior is limited. This study fills this gap by examining cortical volumes and behavioral correlates in school-age children with DS.

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Article Synopsis
  • Poor prenatal sleep quality in mothers is linked to negative outcomes for both the mother and infant, including changes in brain development and increased anxiety-like behaviors.
  • A study involving 116 mother-infant pairs used surveys to assess maternal sleep quality and MRI scans to examine neonatal brain development, specifically focusing on the uncinate fasciculus.
  • Results showed that poorer maternal sleep during pregnancy correlated with higher levels of white matter in infants, which then related to greater infant negative emotionality, indicating that maternal sleep is an important environmental factor influencing child development.
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Reproducibility of neuroimaging research on infant brain development remains limited due to highly variable protocols and processing approaches. Progress towards reproducible pipelines is limited by a lack of benchmarks such as gold standard brain segmentations. Addressing this core limitation, we constructed the Baby Open Brains (BOBs) Repository, an open source resource comprising manually curated and expert-reviewed infant brain segmentations.

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We propose Gumbel Noise Score Matching (GNSM), a novel unsupervised method to detect anomalies in categorical data. GNSM accomplishes this by estimating the scores, i.e.

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