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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

基本信息

批准号：
10669749
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
项目类别：
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/10669749
关键词：
5 year old Address Adult Appearance Atlases Award Birth Brain Brain Diseases Cerebral cortex Childhood Communities Computer software Data Data Set Dedications Development Diffusion Magnetic Resonance Imaging Dimensions Documentation Dropout Evolution Exhibits Failure Functional Magnetic Resonance Imaging Funding Goals Growth Human 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 Thinness Time Universities aging brain analysis pipeline brain magnetic resonance imaging computerized tools connectome contrast imaging critical period deep learning design empowerment image registration imaging modality improved longitudinal analysis multimodality predictive tools tool trait usability white matter

项目摘要

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.

生命第一年人脑发育的纵向图谱抽象的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。上一个项目期间在推进对衰老进行纵向大脑分析的强大工具方面取得了巨大成功脑。在这次更新中，我们寻求进一步推进强大的计算工具，以实现全面的纵向研究早期发育大脑变化的特征。这符合我们创建用于纵向绘制整个人类生命周期中大脑进化的计算工具。需要的工具该项目中开发的软件将允许对纵向体积数据和皮质数据进行统一和并发分析表面，促进关键时期动态和空间异质结构变化的映射大脑发育时期。该项目开发的工具将专门用于研究人类生命最初几年的大脑，在结构和功能上都经历着动态发展。我们将利用通过以下方式提供的 MRI 数据婴儿连接组项目 (BCP)，涉及 500 名儿科受试者，从出生到五岁进行扫描。这 BCP 的结果将告诉神经科学家正常的健康生长是什么样子，并有助于发现脑部疾病的最早表现。为了充分受益于这个独特的数据集，专用的计算工具需要对婴儿 MR 图像进行精确处理和分析，这些图像通常表现出动态异质性随着时间的推移发生新的变化。然而，迄今为止开发的大多数计算工具主要集中在成人受试者，应用于婴儿 MRI 时不可靠。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来对 MRI 数据进行多方面分析，包括体积和白质/软脑膜表面，将在公共空间进行，以便更全面地了解早期正在发育的大脑。我们的工具将明确考虑 MR 图像外观的快速变化，这在生命的第一年。与针对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性，我们的工具将允许联合体积表面在一致的纵向空间中进行分析。通过提取两者的信息来提高配准精度实体对于检测发育中大脑的细微变化至关重要，大脑越薄，体积就越小。大脑皮层。在目标 2 中，我们将为早期开发生成纵向、多模态和全脑分区图。脑。将大脑细分为连贯的区域是宏观映射大脑空间的重要一步本质上异质的变化以及空间和拓扑组织的检查。我们的方法将允许描述分区随时间演变的特征，同时保持时间地块的一致性和主体间对应关系。在目标 3 中，我们将开发能够预测缺失 MRI 数据的技术，以提高提高统计功效的不完整数据。缺失数据是纵向研究中常见且不可避免的问题由于受试者退出或扫描失败而导致的研究，尤其是涉及婴儿的研究。为了解决这个问题，我们将开发深度学习技术来纵向预测缺失的成像数据。该项目的成功完成将为神经科学界提供更多计算工具使用 MRI 精确绘制人类大脑早期规范发育的图表。作为该项目的一部分，我们将提供第一套时间密集的表面体积图集，以捕获关键的发育特征因此对于量化可能偏离正常大脑发育的情况至关重要。