Longitudinal Mapping of Human Brain Development in the First Years of Life

生命第一年人脑发育的纵向图谱

• 批准号: 10491702
• 负责人: Pew-Thian Yap
• 金额: $ 49.02万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-15 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10491702
• 关键词: Address; Adult; Age-Years; Appearance; Atlases; Award; Birth; Brain; Brain Diseases; Cerebral cortex; Childhood; Communities; Computer software; Data; Data Set; Development; Diffusion Magnetic Resonance Imaging; Dimensions; Documentation; Dropout; Evolution; Exhibits; Failure; Functional Magnetic Resonance Imaging; Funding; Goals; Growth; Human; Ice; Image; Infant; Joints; Libraries; Life; Longevity; Longitudinal Studies; Magnetic Resonance Imaging; Maps; Measurement; Methods; Minnesota; Neurodevelopmental Disorder; Neurosciences; North Carolina; Outcome; Pattern; Request for Proposals; Research Personnel; Research Support; Sample Size; Sampling; Scanning; Statistical Data Interpretation; Structure; Surface; Techniques; Time; Universities; aging brain; analysis pipeline; base; brain magnetic resonance imaging; computerized tools; connectome; contrast imaging; critical period; deep learning; design; image registration; imaging modality; improved; longitudinal analysis; multimodality; predictive tools; tool; trait; usability; white matter

Longitudinal Mapping of Human Brain Development in the First Years of Life Abstract This proposal requests continued funding support for research at the University of North Carolina at Chapel Hill to develop computational tools for quantifying longitudinal structural changes in the human brain. The previous project period has been extremely successful in advancing robust tools for longitudinal brain analysis of the aging brain. In this renewal, we seek to further advance robust computational tools for comprehensive longitudinal characterization of changes in the early developing brain. This is in line with our long-term goal of creating computational tools for longitudinal charting of brain evolution across the entire human lifespan. The tools to be developed in this project will allow unified and concurrent analysis of longitudinal volumetric data and cortical surfaces, facilitating the mapping of dynamic and spatially heterogeneous structural changes during a critical period of brain development. The tools developed in this project will be tailored to studying the human brain in the first few years of life, which undergoes dynamic development in both structure and function. We will utilize the MRI data made available via the Baby Connectome Project (BCP), involving 500 pediatric subjects scanned from birth to five years of age. The outcome of BCP will inform neuroscientists what normal healthy growth looks like and facilitate discovery of the earliest manifestations of brain disorders. To fully benefit from this unique dataset, dedicated computational tools are needed for accurate processing and analysis of baby MR images, which typically exhibit dynamic heteroge- neous changes across time. However, most computational tools developed to date have been mostly focused on adult subjects and are unreliable when applied to baby MRI. We propose to address this gap with three aims: In Aim 1, we will develop computational tools to allow multifaceted analysis of MRI data, including volumes and white-matter/pial surfaces, to be carried out in common spaces for a more holistic understanding of the early developing brain. Our tools will explicitly consider the rapid changes in MR image appearances that are typical in the first year of life. Unlike conventional methods that are designed for either image volumes or cortical surfaces, resulting in inconsistencies and loss of sensitivity to subtle changes, our tools will allow joint volume-surface analysis in consistent longitudinal spaces. Improving registration accuracy by drawing information from both entities is critical for detecting subtle changes in the developing brain, which is significantly smaller with a thinner cerebral cortex. In Aim 2, we will generate longitudinal, multimodal, and whole-brain parcellation maps for the early developing brain. Subdivision of the brain into coherent regions is an essential step in the macroscopic mapping of spa- tially heterogeneous changes and in the examination of spatial and topological organization. Our approach will allow the characterization of the evolution of parcellation across time and at the same time maintain temporal consistency and inter-subject correspondences of the parcels. In Aim 3, we will develop techniques that will allow prediction of missing MRI data to increase the usability of incomplete data for improving statistical power. Missing data is a common and inevitable problem in longitudinal studies due to subject dropouts or failed scans, especially in studies involving infants. To address this problem, we will develop deep learning techniques for longitudinal prediction of missing imaging data. Successful completion of this project will empower the neuroscience community with computational tools for more precise charting of the normative early development of the human brain using MRI. As part of this project, we will deliver the first set of temporally-dense surface-volumetric atlases that will capture key developmental traits and are therefore critical for quantification of possible deviation from normal brain development.

人类出生前几年大脑发育的纵向分布图 摘要 该提案要求继续为北卡罗来纳大学教堂山分校的研究提供资金支持 开发用于量化人脑纵向结构变化的计算工具。上一次 项目期在为老龄化的纵向大脑分析提供强大的工具方面取得了极大的成功 大脑。在这次更新中，我们寻求进一步推进全面纵向的强大计算工具 描述发育早期大脑的变化。这与我们的长期目标是创造 计算工具，用于纵向绘制整个人类生命周期的大脑进化图表。将要使用的工具 在该项目中开发将允许对纵向体积数据和大脑皮层进行单fi和并发分析 表面，便于绘制关键时期动态和空间异质结构变化的地图 大脑发育的时期。 在这个项目中开发的工具将被量身定做，以研究生命中最初几年的人脑，这是fi 在结构和功能上都经历了动态的发展。我们将利用通过以下途径提供的MRI数据 婴儿连接计划涉及500名儿科受试者，扫描范围从出生到fi5岁。这个 BCP的结果将告诉神经科学家正常健康的生长是什么样子，并促进发现 脑部疾病的最早表现。为了让fit充分受益于这一独特的数据集、专用计算工具 需要对婴儿磁共振图像进行准确的处理和分析，这些图像通常表现出动态的异质性- 随着时间的推移，神经会发生变化。然而，到目前为止开发的大多数计算工具主要集中在 成人受试者，当应用于婴儿核磁共振时是不可靠的。我们建议通过三个目标来解决这一差距： 在目标1中，我们将开发计算工具，以允许对磁共振数据进行多方面分析，包括体积和 白质/软膜表面，在公共空间进行，以更全面地了解早期 发育中的大脑。我们的工具将明确考虑典型的MR图像外观的快速变化 fi生命的第一年。与为图像体积或皮质表面设计的传统方法不同， 导致不一致和对细微变化失去敏感度，我们的工具将允许连接体积-表面 在一致的纵向空间中分析。通过从两者中提取信息来提高配准精度 实体对于检测发育中的大脑的细微变化至关重要，大脑明显变小变薄(fi) 大脑皮层。 在目标2中，我们将为早期发育生成纵向、多模式和全脑分区图 大脑。将大脑细分为连贯的区域是宏观绘制Spa图的关键一步。 在空间和拓扑组织上的检查和空间异质性变化。我们的方法将 允许表征分割随时间的演变，同时保持时间 地块的一致性和主体间的对应关系。 在目标3中，我们将开发允许预测丢失的MRI数据的技术，以提高 提高统计能力的不完整数据。数据缺失是纵向数据分析中常见且不可避免的问题 由于受试者辍学或扫描失败而导致的研究，特别是在涉及婴儿的研究中。为了解决这个问题， 我们将开发深度学习技术，用于丢失成像数据的纵向预测。 该项目的成功完成将为神经科学界提供更多的计算工具 使用核磁共振技术精确绘制人类大脑正常早期发育的图表。作为该项目的一部分，我们将 提供fi第一套时间致密的表面体积地图集，将捕获关键的发育性状 因此对可能偏离正常大脑发育的fi定量是至关重要的。