Parcellating Infant Cerebral Cortex based on Developmental Patterns of Multimodal MRI

基于多模态 MRI 发育模式的婴儿大脑皮层分区

• 批准号: 9906913
• 负责人: Gang Li
• 金额: $ 38.88万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-11 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9906913
• 关键词: Accounting; Address; Adopted; Adult; Appearance; Area; Atlases; Behavioral; Brain; Brain Diseases; Cerebral cortex; Child; Cognition; Cognition Disorders; Cognitive; Consensus; Data; Data Set; Development; Diagnosis; Diffuse; Early Intervention; Ensure; Exhibits; Free Will; Frequencies; Functional Magnetic Resonance Imaging; Goals; Graph; Growth; Growth Disorders; High birth weight infant; Human; Individual; Individual Differences; Infant; Investigation; Magnetic Resonance Imaging; Maps; Measurement; Methods; Motivation; Myelin; National Institute of Mental Health; Neurodevelopmental Disorder; Pathway Analysis; Pattern; Population; Process; Property; Reproducibility; Rest; Statistical sensitivity; Strategic Planning; Structure; Subgroup; Surface; Thick; Tissues; Weight; base; brain abnormalities; computerized tools; connectome; critical period; diffusion weighted; high risk infant; inter-individual variation; multimodality; neuroimaging; novel; novel strategies; personalized approach; population based; postnatal; tool

Project Abstract The increasing availability of large-scale longitudinal multimodal infant brain MRI datasets, e.g., the Baby Connectome Project (BCP), provides an unprecedented opportunity to precisely chart the dynamic trajectories of early brain development, essential for understanding normative growth and neurodevelopmental disorders. A major barrier is the critical lack of computational tools, atlases and parcellations for cortical surface-based analysis of the challenging infant MRI, which typically exhibits low tissue contrast and regionally-heterogeneous, dynamic changes of cortical properties. To fill this gap, we have pioneered a comprehensive set of infant- dedicated cortical surface analysis tools and atlases. Our tools and discoveries on early brain development have been highlighted in NIMH’s 2015-2020 Strategic Plan. However, computational approaches are still lacking for infant cortical parcellation based on the dynamic brain properties from longituidnal multimodal MRI. Parcellation is a prerequsite in a wide variety of infant neuroimaging applications, e.g., region localization, inter- individual variability investigation, inter-study comparison, statistical sensitivity boosting, node definition for network analysis, and feature reduction for identificaiton of brain disorders. Hence, this project is focused on creating and disseminating novel computational tools for both population-level and individualized infant cortical parcellation utilizing developmental patterns of multiple complementary brain properties, and applying them to better understanding of inter-individual variability and early brain development. The motivation is that the dynamic development of multiple properties (e.g., cortical thickness, folding, diffusivity, myelin content, surface area, structural and functional connectivity) in infants essentially reflects the rapid changes of underlying microstructures and their connectivity, which jointly determine the functional principle of each region. Hence, developmental patterns are ideal for deriving distinct regions in development, microstructure, function, and connectivity for early brain development studies. To achieve this goal, we propose four specific aims. In Aim 1, we will develop a novel method for population-level cortical parcellation based on developmental patterns of multiple properties, by nonlinear fusion of heterogeneous multimodal information from a large population of infants. In Aim 2, we further propose a novel approach for individualized parcellation of each infant’s cortical surfaces based on its own multimodal developmental patterns, thus accounting for remarkable inter-subject variability. We will leverage the population-level parcellation to guide the individualized parcellation in an iterative manner via graph cuts, thus leading to precise individualized parcellations that are easily comparable across individuals. In Aim 3, to understand the remarkable inter-individual variability in each parcellated region, we will discover the representative regional appearance patterns of each cortical property from a large infant population, based on multi-scale spatial-frequency characterizations of cortical property maps via spherical wavelets. In Aim 4, leveraging our tools, atlases, and parcellations, we will chart the multimodal developmental trajectories for each representative pattern of each property and investigate their relationships with behavioral/cognitive scores. Finally, we will freely release our tools, parcellations and the processed BCP data to the public.

项目摘要 大规模纵向多模态婴儿脑 MRI 数据集（例如婴儿）的可用性不断增加 连接组项目（BCP）为精确绘制动态轨迹提供了前所未有的机会 早期大脑发育的过程，对于理解正常生长和神经发育障碍至关重要。 主要障碍是严重缺乏基于皮质表面的计算工具、地图集和分区 对具有挑战性的婴儿 MRI 进行分析，该 MRI 通常表现出低组织对比度和区域异质性， 为了填补这一空白，我们开创了一套全面的婴儿- 我们关于早期大脑发育的专用皮质表面分析工具和图集。 NIMH 的 2015-2020 年战略计划中已经强调了这一点，但是计算方法仍然存在。 缺乏基于纵向多模态 MRI 动态大脑特性的婴儿皮质分区。 分区是各种婴儿神经成像应用的先决条件，例如区域定位、内部 个体差异调查、研究间比较、统计敏感性提升、节点定义 因此，该项目的重点是网络分析和特征缩减以识别大脑疾病。 为人口水平和个性化婴儿创建和传播新颖的计算工具 利用多种互补大脑特性的发育模式进行皮质分割，以及 应用它们来更好地理解个体间的差异和早期大脑发育。 动机是多种特性的动态发展（例如，皮质厚度、折叠、扩散率、 婴儿的髓磷脂含量、表面积、结构和功能连接）本质上反映了婴儿的快速发育 底层微观结构及其连接性的变化，共同决定了功能原理 因此，发育模式对于导出发育、微观结构、 为了实现这一目标，我们提出了四个具体的建议。 在目标 1 中，我们将开发一种基于群体水平皮质分区的新方法。 通过异构多模态信息的非线性融合，形成多种属性的发展模式 在目标 2 中，我们进一步提出了一种个性化分割的新方法。 每个婴儿的皮质表面基于其自身的多模式发育模式，从而解释 我们将利用人口层面的分区来指导个体化。 通过图形切割以迭代方式进行分区，从而产生精确的个性化分区 在目标 3 中，了解每个人之间显着的个体差异。 分割区域，我们将发现每个皮质属性的代表性区域外观模式 来自大量婴儿群体，基于皮质特性的多尺度空间频率特征 在目标 4 中，利用我们的工具、地图集和分区，我们将绘制地图。 每个属性的每个代表性模式的多模态发展轨迹并研究它们 最后，我们将自由发布我们的工具、分区和 向公众处理 BCP 数据。