Mapping the developing infant connectome

绘制发育中的婴儿连接组图

• 批准号: 10413004
• 负责人: VINCE D CALHOUN
• 金额: $ 44.57万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10413004
• 关键词: Address; Adult; Age-Months; Anatomy; Area; Benchmarking; Birth; Brain; Brain imaging; Characteristics; Code; Communities; Complex; Cross-Sectional Studies; Data; Data Set; Development; Developmental Process; Diffusion; Diffusion Magnetic Resonance Imaging; Disadvantaged; Functional Magnetic Resonance Imaging; Goals; Graph; Growth; Image; Individual; Infant; Infant Development; Lead; Life; Light; Longitudinal Studies; Magnetic Resonance Imaging; Maps; Mathematics; Measures; Methodology; Methods; Modeling; Neurodevelopmental Disorder; Neurosciences; Nodal; Principal Component Analysis; Property; Research; Research Project Grants; Rest; Sample Size; Sampling; Scanning; Scientific Inquiry; Shapes; Specific qualifier value; Statistical Data Interpretation; System; Techniques; Testing; Time; Variant; age group; analytical tool; base; brain research; cognitive development; cognitive function; connectome; course development; curve fitting; design; disability; frontier; graph theory; improved; individual variation; infancy; innovation; insight; longitudinal analysis; longitudinal design; method development; neural growth; neuroimaging; neuropsychiatry; novel; postnatal; postnatal development; prospective; rate of change; tool

PROJECT SUMMARY/ABSTRACT The overarching goal of this proposal is to develop new and innovative analytic tools for longitudinal infant brain research and to leverage these tools to chart the development of infant brain networks during the first 6 months of life, a period of unparalleled postnatal growth and change. Describing the developmental trajectory of brain systems during this formative period has the potential to provide groundbreaking insights into major areas of scientific inquiry, including the identification of brain systems that underlie the development of cognitive functions, the discovery of how structural and functional network specializations arise, and the identification of brain networks that contribute to neuropsychiatric illness. However, despite this potential, longitudinal studies of infant brain development are still nascent, and prevailing analytic tools—largely designed for cross-sectional analyses of adult data—are ill-suited for fully capturing fast-pace developmental processes during infancy. This proposal aims to 1) develop innovative analytic tools that are specifically designed to address challenges inherent to longitudinal infant brain research; 2) leverage these tools to examine graph theoretic measures of brain network development in typical infancy; and 3) disseminate these tools and approaches to the broader research community. Methods development will focus on two key areas: registration (the approach for transforming individual brain images to a common space) and statistical analysis of longitudinal data (the approach for constructing and analyzing growth curves of brain development). Methods development and analyses will be conducted on anatomical, diffusion tensor imaging and resting- state functional MRI data collected from infants at three longitudinal time points between birth and 6 months of age. Aim 1 of this proposal is to develop and validate a novel hierarchical, tensor-based registration approach, designed to handle the challenges associated with registering highly heterogeneous images, a characteristic of longitudinal infant data. Aim 2 will improve an already state-of-the-art approach for the analysis of longitudinal data and pioneer its application to the case of longitudinal neuroimaging data. Finally, Aim 3 will leverage these methods to produce a temporally-precise mapping of typical growth curves of brain network development in the first postnatal months, providing a benchmark against which to interpret and understand how alternate trajectories of brain development can lead to disability. These aims will help advance the frontier of studies of brain development into early infancy, a formative, and yet relatively uncharted, period of development.

项目摘要/摘要 这项提议的首要目标是为纵向婴儿开发新的和创新的分析工具 大脑研究，并利用这些工具来绘制婴儿大脑网络在前6个月的发展图 几个月的生命，一段无与伦比的后天成长和变化。描述发展轨迹 在这一形成时期的大脑系统的研究有可能提供对主要 科学研究领域，包括确定作为发展基础的大脑系统 认知功能，发现结构和功能网络专业化是如何产生的，以及 确定导致神经精神疾病的大脑网络。然而，尽管有这种潜力， 对婴儿大脑发育的纵向研究仍处于初级阶段，主流的分析工具--主要是 专为成人数据的横断面分析而设计-不适合完全捕获快节奏的发育 在婴儿期的过程。该提案旨在1)开发创新的分析工具，这些工具具体 旨在解决纵向婴儿大脑研究固有的挑战；2)利用这些工具 检验典型婴儿期大脑网络发育的图论测量；以及3)传播这些 为更广泛的研究社区提供工具和方法。方法开发将集中在两个关键领域： 配准(将单个脑图像转换到公共空间的方法)和统计分析 纵向数据(构建和分析大脑发育生长曲线的方法)。 方法在解剖、弥散张量成像和静息状态下进行发展和分析。 婴儿从出生到6个月的三个纵向时间点的状态功能MRI数据 年龄。该提案的目标1是开发和验证一种新的基于张量的分层配准方法， 旨在应对与注册高度异类映像相关的挑战，这是 纵向婴儿数据。目标2将改进一种已经是最先进的方法来分析纵向 数据，并率先将其应用于纵向神经成像数据的情况。最后，Aim 3将利用这些优势 方法制作脑网络发育典型生长曲线的时间精确图。 出生后的最初几个月，提供了一个基准来解释和理解如何交替 大脑发育的轨迹可能会导致残疾。这些目标将有助于推动研究的前沿 大脑发育到早期婴儿期，这是一个形成期，但相对未知的发育期。