Predicting long-term outcomes in preterm infants using multimodal neuroimaging techniques and environmental factors

使用多模式神经影像技术和环境因素预测早产儿的长期结局

• 批准号: 10507663
• 负责人: Elveda Gozdas
• 金额: $ 13.21万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-10 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10507663
• 关键词: 2 year old; Adolescence; Affect; Age; Architecture; Attention; Behavioral; Biological; Biophysics; Birth; Brain; Brain region; Cognitive; Data; Development; Diffusion Magnetic Resonance Imaging; Early Intervention; Education; Environmental Risk Factor; Epidemic; Goals; Grain; Healthcare; Household; Human; Imaging Techniques; Income; Infant; Intervention; Knowledge; Language; Life; Life Cycle Stages; Link; Magnetic Resonance Imaging; Measures; Mental disorders; Methods; Modeling; Molecular; Morphology; Motor; Neonatal; Neurites; Neurocognitive; Neurodevelopmental Impairment; Neurodevelopmental Problem; Occupations; Outcome; Parental Ages; Pattern; Play; Predictive Factor; Premature Birth; Premature Infant; Process; Property; Research; Role; Shapes; Structural defect; Structure; Survivors; System; Techniques; Testing; Time; Tissues; brain tissue; cognitive function; connectome based predictive modeling; design; diagnostic tool; early detection biomarkers; effective therapy; experience; family structure; gray matter; high risk; imaging biomarker; improved; insight; maternal depression; multimodal neuroimaging; neonatal brain; neonatal period; nervous system disorder; network architecture; neuroimaging; novel; novel strategies; predictive marker; predictive modeling; premature; preventive intervention; quantitative imaging; white matter

Project Summary/Abstract The outlined proposal expands the previous research in preterm infant brains by integrating multiple advanced neuroimaging methods, including novel quantitative magnetic resonance imaging (MRI) techniques and environmental factors to predict long-term neurodevelopmental issues. Preterm infants have a high rate of long-term neurodevelopmental impairments that often require additional health care and intervention. Recent advances in neuroimaging have provided great insight into the patterns of specific alterations in preterm infants. However, the studies are limited to demonstrate how brain function and structure and their interaction relate to or explain later neurodevelopmental outcomes in preterm infants. Further, while recent studies showed that microstructural tissue properties using recently developed quantitative MRI techniques (multi-shell diffusion tensor imaging, quantitative T1, qT1) are more sensitive and reliable to define underlying neurological diseases and, thus, it is crucial to investigate the microstructural tissue properties during the neonatal period to fully understand how these measures are related to preterm birth and predict later cognitive problems in preterm infants. The overarching aims of this project are designed to use neonatal multimodal neuroimaging techniques, including qT1, for the first time, combining with environmental factors using advanced computational approaches to define early biomarkers of later preterm neurodevelopmental outcomes. We hypothesize that the links between structural-functional brain networks are significantly altered, and when combined, will provide an exclusive prediction on later neurodevelopmental outcomes. Similarly, we will test the working hypothesis preterm infants will have abnormal white and grey matter microstructures, and these patterns will be correlated with the neurodevelopmental problems. Furthermore, we will explore the environmental factors contributing to later cognitive issues that will play an essential role in predicting later neurodevelopmental problems in preterm infants. The proposed research results will provide an early diagnostic tool that could inform the treatments and implementation of preventative interventions before any cognitive problem emerges. It also has an important impact on identifying behavioral targets to improve the life course outcomes in preterm infants.

项目摘要/摘要 概述的方案通过整合多个先进的 神经成像方法，包括新的定量磁共振成像(MRI)技术和 预测长期神经发育问题的环境因素。早产儿有很高的发病率 长期的神经发育损伤，通常需要额外的医疗保健和干预。近期 神经成像的进展为早产的特殊改变模式提供了更多的洞察力。 婴儿。然而，这些研究仅限于展示大脑的功能和结构以及它们之间的相互作用 与早产儿后来的神经发育结局有关或解释的。此外，虽然最近的研究 使用最近开发的定量MRI技术(多层壳)显示微结构组织属性 弥散张量成像，定量T1，QT1)更敏感和可靠地确定潜在的神经 因此，研究新生儿期的微结构组织特性是至关重要的 充分了解这些测量与早产的关系，并预测以后的认知问题 早产儿。该项目的总体目标是设计使用新生儿多模式神经成像 技术，包括QT1，首次与环境因素相结合，使用先进的 定义早产晚期神经发育结局的早期生物标记物的计算方法。我们 假设结构-功能大脑网络之间的联系显著改变，以及何时 结合在一起，将提供对未来神经发育结果的独家预测。同样，我们将测试 工作假设早产儿会有异常的白质和灰质微结构，而这些 模式将与神经发育问题相关。此外，我们还将探讨 环境因素导致后来的认知问题，这些问题将对后来的预测起到至关重要的作用 早产儿的神经发育问题。拟议的研究结果将为早期的研究提供 一种诊断工具，可在任何情况下告知治疗和预防性干预的实施情况 认知问题就出现了。它对确定改善生活的行为目标也有重要影响 早产儿的病程结局。