Parcellating Infant Cerebral Cortex based on Developmental Patterns of Multimodal MRI

项目摘要 大规模纵向多模态婴儿脑MRI数据集的日益可用性，例如，宝宝 连接组计划（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，对理解正常生长和神经发育障碍至关重要。一 一个主要的障碍是严重缺乏计算工具，地图集和包裹皮质表面为基础的 分析具有挑战性的婴儿MRI，其通常表现出低组织对比度和区域异质性， 皮质特性的动态变化。为了填补这一空白，我们开创了一套全面的婴儿- 专门的皮质表面分析工具和图谱。我们在早期大脑发育方面的工具和发现 这是NIMH 2015-2020年战略计划的重点。然而，计算方法仍然是 缺乏婴儿皮质分区的基础上，动态脑性能从middnal多模态MRI。 包裹是各种婴儿神经成像应用中的先决条件，例如，区域定位，内部， 个体变异性研究、研究间比较、统计敏感性增强、节点定义 网络分析和特征约简，用于识别大脑疾病。因此，该项目的重点是 创造和传播新的计算工具，为人口水平和个性化的婴儿 利用多个互补脑特性的发育模式的皮层包裹，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。的 动机是多个属性的动态发展（例如，皮质厚度，折叠，扩散率， 髓鞘含量、表面积、结构和功能连接性）基本上反映了婴儿的快速 底层微结构及其连通性的变化，共同决定了 每个区域。因此，发育模式是理想的衍生不同的区域在发展，微观结构， 功能和连接性，用于早期大脑发育研究。为实现这一目标，我们提出四项具体措施。 目标。在目标1中，我们将开发一种新的方法，用于基于 多属性的发展模式，通过非线性融合异质多模态信息 从大量的婴儿身上。在目标2中，我们进一步提出了一种新的个性化包裹方法 每个婴儿的皮质表面基于其自身的多模态发育模式，从而占 受试者间差异显著。我们将利用人口层面的分割来指导个性化的 通过图形切割以迭代方式进行分割，从而产生精确的个性化分割， 很容易在个人之间进行比较。在目标3中，为了了解每个人的显著个体间变异性， 在每个分区中，我们将发现每个皮质特性的代表性区域外观模式 从一个大的婴儿群体，基于多尺度的空间-频率表征的皮质属性 通过球面小波映射。在目标4中，利用我们的工具、地图集和包裹，我们将绘制 多模态发展轨迹的每一个代表性模式的每一个属性，并调查他们的 与行为/认知评分的关系。最后，我们将免费发布我们的工具，包裹和 处理过的BCP数据公开。

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.