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Articles by Yangming Ou
Predicting early school-age neurocognitive outcomes from maternal nutrition and sociodemographic factors in a pilot machine learning study
Abstract Maternal diet plays an important role in child brain development. However, existing studies mostly focus on dietary nutrients in isolation, neglecting interactions among nutrients. Further, most studies have focused on preterm infants, leaving little knowledge on term-born infants who account for > 90% of all newborns. Moreover, beyong nutrition, it is important to consider the role of infant factors, family factors, and social determinants of health when assessing outcomes.
Family protective service involvement and reduced infant cortical grey matter volume
1 INTRODUCTION Childhood maltreatment is a significant problem worldwide. In the United States in 2021, more than seven million allegations of maltreatment, including abuse, neglect, or sex trafficking, were made to Child Protective Services (CPS) (U.S. Department of Health & Human Services (HHS), 2023). Of these, 2.05 million were investigated and 600,000 children were found to be victims of maltreatment, representing a victimization rate of 8.1 per 1000 children.
Enhancing Neurocognitive Outcome Prediction in Congenital Heart Disease Patients: The Role of Brain Age Biomarkers and Beyond
Abstract This paper aimed to investigate the predictive power of combining demographic, socioeconomic, and genetic factors with a brain MRI-based quantified measure of accelerated brain aging (referred to as deltaAGE) for neurocognitive outcomes in adolescents and young adults with Congenital Heart Disease (CHD). Our hypothesis posited that including the brain age biomarker (deltaAGE) would enhance neurocognitive outcome predictions compared to models excluding it.
Challenges of implementing computer-aided diagnostic models for neuroimages in a clinical setting - npj Digital Medicine
Abstract Advances in artificial intelligence have cultivated a strong interest in developing and validating the clinical utilities of computer-aided diagnostic models. Machine learning for diagnostic neuroimaging has often been applied to detect psychological and neurological disorders, typically on small-scale datasets or data collected in a research setting.
Characterization of the effects of outliers on ComBat harmonization for removing inter-site data heterogeneity in multisite neuroimaging studies
1. Introduction With the rapid development of neuroimaging techniques and information technology, the utilization of big data is becoming a trend in neuroimaging studies in recent years for several reasons (Poline et al., 2012). First, large sample sizes provide higher statistical power and are more likely to produce reliable results.
Can deep learning predict human intelligence from structural brain MRI?
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Publisher Correction: Probing tumor microenvironment in patients with newly diagnosed glioblastoma during chemoradiation and adjuvant temozolomide with functional MRI
Correction to: Scientific Reports https://doi.org/10.1038/s41598-018-34820-x, published online 20 November 2018 In the original version of this Article, the author K. Ina Ly was incorrectly indexed. This error has now been corrected. Author information Affiliations Stephen E. and Catherine Pappas Center for Neuro-Oncology, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA K. Ina Ly , Scott R. Plotkin , Tracy T. Batchelor & Elizabeth R.
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