Publications by authors named "D Ben Bashat"
Alpha-synuclein (αS) aggregation is a widely regarded hallmark of Parkinson's disease (PD) and can be detected through synuclein amplification assays (SAA). This study investigated the association between cerebrospinal fluid (CSF) radiological measures in 41 PD patients (14 iPD, 14 GBA1-PD, 13 LRRK2-PD) and 14 age-and-sex-matched healthy controls. Quantitative measures including striatal binding ratios (SBR), whole-brain and deep gray matter volumes, neuromelanin-MRI (NM-MRI), functional connectivity (FC), and white matter (WM) diffusion-tensor imaging (DTI) were calculated.
View Article and Find Full Text PDFBackground: The American Academy of Pediatrics advises that the nutrition of preterm infants should target a body composition similar to that of a fetus in utero. Still, reference charts for intrauterine body composition are missing. Moreover, data on sexual differences in intrauterine body composition during pregnancy are limited.
View Article and Find Full Text PDFBackground: Cytomegalovirus (CMV) is the most common intrauterine infection and may be associated with unfavorable outcomes. While some CMV-infected fetuses may show gross or subtle brain abnormalities on MRI, their clinical significance may be unclear. Conversely, normal development cannot be guaranteed in CMV-infected fetuses with normal MRI.
View Article and Find Full Text PDFPlacental-related fetal growth restriction, resulting from placental dysfunction, impacts 3-5% of pregnancies and is linked to elevated risk of adverse neurodevelopmental outcomes. In response, the fetus employs a mechanism known as brain-sparing, redirecting blood flow to the cerebral circuit, for adequate supply to the brain. In this study we aimed to quantitatively evaluate disparities in gyrification and brain volumes among fetal growth restriction, small for gestational age and appropriate-for gestational-age fetuses.
View Article and Find Full Text PDFArticle Synopsis
- The study aims to develop an automated method to quantitatively assess fetal brain gyrification using standard 2D MR imaging, instead of relying on subjective visual assessments.
- It involves analyzing imaging data of 162 fetuses—134 controls and 28 with lissencephaly or polymicrogyria—to calculate various gyrification parameters and differentiate between normal and abnormal conditions.
- Results indicate significant changes in gyrification with gestational age for normal fetuses, as well as reductions in lissencephaly and polymicrogyria cases, with machine learning algorithms effectively classifying these conditions.