Yuan-Chiao Lu

Volumetric analysis of magnetic resonance brain images is often complicated by variations in scanner hardware, software, and acquisition settings. Over the past several years, there has been an increase in the use of retrospective harmonization techniques for addressing these variations. This research evaluates three image harmonization methods—neuroCombat (a statistical batch correction tool), DeepHarmony (a supervised deep learning method based on image-to-image translation), and HACA3 (an unsupervised deep learning image translation approach). The study focuses on their effectiveness in achieving consistent brain volume measurements across differing T1-weighted acquisitions (GRE and MPRAGE) and their ability to detect simulated atrophy changes in the same acquisitions. While all three methods notably enhance the consistency of regional brain volumes compared with unharmonized images, HACA3 demonstrated the lowest measurement variation in terms of absolute volume difference percentage (AVDP) across all brain regions (0.9). In the atrophy simulation experiments, HACA3 consistently achieved the smallest AVDPs across most unchanged brain regions, while DeepHarmony showed significant improvements in several regions, and neuroCombat exhibited higher variability. Additionally, using neuroCombat with training data effectively detected hippocampal atrophy, whereas without training, neuroCombat could not differentiate between images with and without atrophy, highlighting a potential limitation in its ability to detect subtle brain volume changes when training data are unavailable. In most metrics, HACA3 was found to be the most effective for harmonizing MRI data, followed by DeepHarmony, with neuroCombat showing more measurement variability but still offering improvements over unharmonized data.

Background: Elevated maternal psychological distress during pregnancy is linked to adverse outcomes in offspring. The potential effects of intensified levels of maternal distress during the COVID-19 pandemic on the developing fetal brain are currently unknown. Methods: We prospectively enrolled 202 pregnant women: 65 without known COVID-19 exposures during the pandemic who underwent 92 fetal MRI scans, and 137 pre-pandemic controls who had 182 MRI scans. Multi-plane, multi-phase single shot fast spin echo T2-weighted images were acquired on a GE 1.5 T MRI Scanner. Volumes of six brain tissue types were calculated. Cortical folding measures, including brain surface area, local gyrification index, and sulcal depth were determined. At each MRI scan, maternal distress was assessed using validated stress, anxiety, and depression scales. Generalized estimating equations were utilized to compare maternal distress measures, brain volume and cortical folding differences between pandemic and pre-pandemic cohorts. Results: Stress and depression scores are significantly higher in the pandemic cohort, compared to the pre-pandemic cohort. Fetal white matter, hippocampal, and cerebellar volumes are decreased in the pandemic cohort. Cortical surface area and local gyrification index are also decreased in all four lobes, while sulcal depth is lower in the frontal, parietal, and occipital lobes in the pandemic cohort, indicating delayed brain gyrification. Conclusions: We report impaired fetal brain growth and delayed cerebral cortical gyrification in COVID-19 pandemic era pregnancies, in the setting of heightened maternal psychological distress. The potential long-term neurodevelopmental consequences of altered fetal brain development in COVID-era pregnancies merit further study.

Background: The American family structure has changed in the past few decades due to a rise in the divorce rate and unmarried women with children. Research suggests a salary disparity between men and women, especially for those women after pregnancy. However, these studies were confined to individuals within traditional families, and there is a lack of information of income disparity and poverty status between single mothers and fathers. The current study explored the disparities in single-parent families based on the household income and the poverty status using a set of nationwide censor data. Methods: The current study used data from the 2011 and 2013 Panel Study of Income Dynamics (N = 1135). Multivariate regression models were used in the analysis. Results: The demographic characteristics of the weighted population showed that taxable income, total income, and poverty status were higher for single fathers than mothers, while non-work income was higher for single mothers than fathers. Single mothers were much more likely to be at the crisis category than single fathers. Multivariate analyses showed that gender, age, marital status, years of experience, and geographic region had effects on taxable income, and only gender, marital status, and region had effects on poverty status. Conclusions: The results suggest that vulnerable group of single mothers was acknowledged according to income and poverty status. Age, marital status, years of experience, and region would be the critical factors for predicting the income and poverty status for single parenthood.