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.

项目总结/摘要 这项建议的首要目标是开发新的和创新的分析工具，纵向婴儿 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 几个月的生命，一个无与伦比的产后成长和变化的时期。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突破性的见解， 科学研究的领域，包括确定大脑系统的发展基础， 认知功能，发现结构和功能网络专业化是如何产生的，以及 识别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起步阶段， 设计用于成人数据的横截面分析-不适合完全捕捉快节奏的发展 婴儿期的过程。该提案旨在：1）开发创新的分析工具， 旨在解决纵向婴儿大脑研究固有的挑战; 2）利用这些工具， 检查典型婴儿期大脑网络发展的图论测量; 3）传播这些 工具和方法，以更广泛的研究界。方法制定将侧重于两个关键领域： 配准（将单个大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）。 方法的发展和分析将进行解剖，扩散张量成像和休息- 从婴儿出生到6个月大的三个纵向时间点收集的功能性MRI数据， 年龄该提案的目标1是开发和验证一种新的分层的、基于张量的配准方法， 旨在应对与配准高度异构图像相关的挑战，这是 纵向婴儿数据。目标2将改进一个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，目标3将利用这些 方法来产生一个时间精确映射的典型增长曲线的大脑网络的发展， 产后第一个月，提供了一个基准，以解释和理解如何交替 大脑发育的轨迹会导致残疾这些目标将有助于推进研究的前沿， 大脑发育到婴儿早期，一个形成的，但相对未知的发展时期。