Individual Motor Outcome Prediction in Preterm Children Using Neonatal Neuroimaging

使用新生儿神经影像学预测早产儿的个体运动结果

• 批准号: 10474294
• 负责人: Peppar Elizabeth Pei-pei Cyr
• 金额: $ 3.27万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10474294
• 关键词: 10 year old; 37 weeks gestation; Accounting; Advocacy; Age; Anatomy; Anisotropy; Area; Award; Basal Ganglia; Brain; Brain region; Cerebellum; Cerebral Palsy; Child; Childhood; Clinical; Communication; Corpus Callosum; Corticospinal Tracts; Data; Data Analyses; Development; Diffusion; Diffusion Magnetic Resonance Imaging; Environment; Face; Functional Magnetic Resonance Imaging; Growth; Individual; Infant; Infant Development; Intervention; Investigation; Life; Link; Literature; Long-Term Effects; Longitudinal cohort; MRI Scans; Magnetic Resonance Imaging; Measures; Medical; Methodology; Methods; Modality; Modeling; Modification; Motor; Motor Cortex; Movement; Neonatal; Neurodevelopmental Disability; Neurodevelopmental Impairment; Neurologist; Newborn Infant; Outcome; Patient Care; Performance; Phenotype; Physicians; Population; Pregnancy; Premature Birth; Process; Research; Resolution; Rest; Risk; Risk Factors; Scientist; Standardization; Statistical Models; System; Testing; Thalamic structure; Training; United States; Work; base; brain magnetic resonance imaging; career; child battery; clinical risk; cohort; design; disability; early childhood; experience; high risk; high risk population; imaging modality; improved; improved outcome; individual patient; middle childhood; motor deficit; motor impairment; neonatal brain; neuroimaging; outcome prediction; personalized predictions; predictive modeling; prospective; retention rate; skills; social; standardize measure; success; targeted treatment; white matter; white matter injury

Project Summary Each year in the United States alone, 500,000 infants are born preterm (<37 weeks gestation), putting them at increased risk for neurodevelopmental disabilities, including cerebral palsy and other motor impairments. While specific clinical populations are known to be at increased risk, the likelihood of disability for any individual child cannot currently be accurately predicted based upon clinical risk factors alone, limiting our ability to effectively target therapies and develop new interventions. Prior neuroimaging studies have linked preterm birth to disrupted development of the motor system, encompassing the motor cortex, thalamus, basal ganglia, and cerebellum and associated white matter tracts including the corpus callosum (CC) and corticospinal tract (CST). While aberrant structural and functional connectivity across these regions have been associated with poorer motor outcomes, this has not been investigated across childhood in longitudinal cohorts in a way that allows for individualized outcome prediction. This study proposes to use multiple advanced neuroimaging modalities to statistically model how changes in neonatal structural and functional connectivity within the motor system can predict childhood motor outcomes in children born very preterm (VPT; <30 weeks' gestation). This investigation will leverage a unique, highly valuable, prospective, longitudinal cohort (currently being studied through R01 MH113570) that includes 175 VPT children, including 41 with cerebral palsy and 68 with other motor deficits. We collected state-of-the-art neonatal neuroimaging data for these children, including high- resolution anatomic, functional, and diffusion data. They have also undergone standardized testing of both fine and gross motor function at ages 2, 5, and 9/10 years, with retention rates >80% across assessment waves. Across the three aims of this study, latent growth curve models will be created and compared to determine the individual-level predictive ability of motor system functional connectivity and CC and CST microstructure, both individually and in combination, on motor trajectories through age 10 years. This project would both advance our ability to predict outcomes for individual preterm children into middle childhood and build the applicant's skills in neuroimaging, longitudinal data analysis, and scientific communication in a research environment with clear expertise in these areas. In the process, she would become proficient in the methods necessary for furthering our understanding of the relationships between early brain development and disability in high-risk populations. She would also become prepared to undertake not only strong experimental work, but also care for patients with neurodevelopmental disabilities while effectively integrating her research with disability advocacy. This would pave the way for the applicant to become a successful physician-scientist and child neurologist creating better outcomes for children with neurodevelopmental disabilities.

项目概要 仅在美国，每年就有 500,000 名婴儿早产（妊娠 <37 周），使他们处于 神经发育障碍的风险增加，包括脑瘫和其他运动障碍。尽管 已知特定的临床人群面临更高的风险，任何个别儿童残疾的可能性 目前无法仅根据临床风险因素进行准确预测，限制了我们有效预测的能力 靶向治疗并开发新的干预措施。先前的神经影像学研究已将早产与 运动系统的发育受到干扰，包括运动皮层、丘脑、基底神经节和 小脑和相关的白质束，包括胼胝体 (CC) 和皮质脊髓束 (CST)。 虽然这些区域的结构和功能连接异常与较差有关 运动结果，这还没有在纵向队列中以允许的方式在整个童年时期进行调查 个性化结果预测。本研究建议使用多种先进的神经影像学方法 统计模型运动系统内新生儿结构和功能连接的变化如何影响 预测极早产儿（VPT；妊娠<30周）的儿童运动结局。这 调查将利用一个独特的、非常有价值的、前瞻性的、纵向的队列（目前正在研究 通过 R01 MH113570），其中包括 175 名 VPT 儿童，其中 41 名患有脑瘫，68 名患有其他疾病 运动缺陷。我们为这些儿童收集了最先进的新生儿神经影像数据，包括高 分辨率解剖、功能和扩散数据。他们还经过了精细的标准化测试 2、5 岁和 9/10 岁时的粗大运动功能，整个评估波次的保留率 >80%。 针对本研究的三个目标，将创建并比较潜在增长曲线模型，以确定 运动系统功能连接以及 CC 和 CST 微观结构的个体水平预测能力 单独和组合，研究 10 岁之前的运动轨迹。该项目将同时推进 我们有能力预测个别早产儿进入童年中期的结果，并建立申请人的 研究环境中的神经影像、纵向数据分析和科学交流技能 这些领域的明确专业知识。在这个过程中，她会熟练掌握必要的方法。 加深我们对高危人群早期大脑发育与残疾之间关系的理解 人口。她还将做好准备，不仅要承担强有力的实验工作，还要承担护理工作。 为患有神经发育障碍的患者提供帮助，同时有效地将她的研究与残疾结合起来 倡导。这将为申请人成为一名成功的医生科学家和儿童铺平道路 神经科医生为患有神经发育障碍的儿童创造更好的结果。