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

基本信息

批准号：
10407000
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
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-07-11 至 2024-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10407000
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 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 Subgroup Surface Thick Tissues Weight base brain abnormalities brain magnetic resonance imaging computerized tools connectome critical period diffusion weighted high risk infant inter-individual variation multimodality neuroimaging novel novel strategies personalized approach population based postnatal tool

项目摘要

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式婴儿脑MRI数据集的可用性增加，例如婴儿 Connectome Project（BCP）提供了一个前所未有的机会来精确绘制动态轨迹早期大脑发育，对于理解正常生长和神经发育障碍至关重要。一个主要的障碍是基于皮质表面的计算工具，地图集和划线的严重缺乏分析挑战婴儿MRI，通常表现出低组织对比度和区域性质地的分析皮质特性的动态变化。为了填补这一空白，我们开创了一组全面的婴儿专用的皮质表面分析工具和地图集。我们关于早期大脑开发的工具和发现在NIMH的2015 - 2020年战略计划中已突出显示。但是，计算方法仍然是基于纵向多模态MRI的动态大脑特性缺乏婴儿皮层分层。分析是在各种婴儿神经影像应用中的前孔岩，例如区域定位，间个人可变性调查，研究间比较，统计灵敏度提高，节点定义网络分析和降低脑疾病的特征。因此，这个项目的重点是为人口级别和个性化婴儿创建和传播新型计算工具使用多种互补脑特性的开发模式和应用它们以更好地了解个体间的变异性和早期大脑发育。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）基本上反映了快速基础微观结构的变化及其连接性，共同决定了功能原理每个区域。因此，发育模式非常适合得出发展中不同区域，微观结构，功能和早期大脑发育研究的连通性。为了实现这一目标，我们提出了四个具体的目标。在AIM 1中，我们将开发一种基于人群水平皮质划线的新方法通过非均质多模式信息的非线性融合多种性质的发展模式来自大量婴儿。在AIM 2中，我们进一步提出了一种新颖的个性化拟态方法每个婴儿的皮质表面基于其自身的多模式发育模式，从而考虑显着的受试者间变异性。我们将利用人口级别的分析来指导个性化的通过曲线剪切以迭代方式进行分割，从而导致精确的个性化分析在个人之间很容易比较。在AIM 3中，了解每个分析区域，我们将发现每个皮质特性的代表性区域外观模式来自大型婴儿的人群，基于皮质特性的多尺度空间频率特征通过球形小波的地图。在AIM 4中，利用我们的工具，地图和分析，我们将绘制每个特性的每个代表性模式的多模式发展轨迹，并研究其与行为/认知评分的关系。最后，我们将自由发布我们的工具，分析和向公众处理了BCP数据。

项目成果

期刊论文数量（11）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Construction of Spatiotemporal Infant Cortical Surface Functional Templates.

DOI：
10.1007/978-3-030-59728-3_24
发表时间：
2020
期刊：
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
影响因子：
0
作者：
Huang Y;Wang F;Wu Z;Chen Z;Zhang H;Wang L;Lin W;Shen D;Li G;UNC/UMN Baby Connectome Project Consortium
通讯作者：
UNC/UMN Baby Connectome Project Consortium

A computational method for longitudinal mapping of orientation-specific expansion of cortical surface in infants.

婴儿皮层表面定向扩张纵向映射的计算方法