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.

项目摘要 仅在美国，每年就有50万婴儿出生的早产（<37周妊娠），将他们放在 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的风险增加，任何单个孩子的残疾可能性 目前不能仅根据临床风险因素来准确预测，从而限制了我们有效的能力 目标疗法并制定新的干预措施。先前的神经影像学研究已将早产与 电机系统的开发破坏了，包括运动皮层，丘脑，基底神经节和 小脑和相关的白质区域，包括call体（CC）和皮质脊髓区（CST）。 虽然这些区域之间的异常结构和功能连通性与较差有关 马达结果，尚未以纵向人群的方式对此进行调查 个性化的结果预测。这项研究建议使用多种高级神经影像学方式 从统计上模拟电机系统中新生儿结构和功能连接性的变化如何 预测出生的儿童的儿童运动结果（VPT；妊娠30周）。这 调查将利用独特的，高价，前瞻性的纵向队列（目前正在研究） 通过R01 MH113570），其中包括175名VPT儿童，包括41个脑瘫和68个 运动不足。我们为这些儿童收集了最新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。他们还接受了两者的标准化测试 在2、5和9/10年的总体运动功能和总体运动功能，在评估波中保留率> 80％。 在本研究的三个目标中，将创建潜在的生长曲线模型并比较确定 电机系统功能连接以及CC和CST微结构的个人级别的预测能力，均 单独并结合使用10岁的运动轨迹。这个项目都将进步 我们有能力预测各个早产儿童进入中年童年并建立申请人的能力 在研究环境中，神经影像学，纵向数据分析和科学沟通的技能 在这些领域清晰的专业知识。在此过程中，她将精通 进一步了解我们对早期大脑发育与高风险中残疾之间关系的理解 人群。她还准备不仅要进行强大的实验工作，而且还要照顾 对于神经发育障碍的患者，同时有效地整合了她的研究与残疾 倡导。这将为申请人成为成功的医师科学家和孩子铺平道路 神经科医生为神经发育障碍儿童创造更好的结果。