Harmonizing and Archiving of Large-scale Infant Neuroimaging Data

大规模婴儿神经影像数据的协调和归档

• 批准号: 10189251
• 负责人: Gang Li
• 金额: $ 62.7万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10189251
• 关键词: 2 year old; 5 year old; Address; Adopted; Adult; Age-Months; Archives; Area; Atlases; BRAIN initiative; Big Data; Biological; Biological Preservation; Birth; Brain; Cerebral cortex; Child; Code; Data; Data Set; Deposition; Development; Diffuse; Ensure; Exhibits; Feedback; Funding; Goals; Growth; Human; Image; Imaging technology; Infant; Institutes; Intuition; Joints; Learning; MRI Scans; Machine Learning; Magnetic Resonance Imaging; Maps; Methods; Myelin; National Institute of Mental Health; Network-based; Neurodevelopmental Disorder; Pattern; Positioning Attribute; Process; Property; Protocols documentation; Quality Control; Reproducibility; Resolution; Risk; Scanning; Series; Site; Source Code; Strategic Planning; Structure; Surface; Techniques; Thick; Tissues; Travel; Twin Multiple Birth; United States National Institutes of Health; Variant; autism spectrum disorder; base; brain abnormalities; computational atlas; computerized tools; connectome; cortex mapping; critical period; data archive; data harmonization; deep learning; design; imaging study; improved; infancy; neural network; neuroimaging; novel; postnatal; response; secondary analysis; tool

Project Abstract The first postnatal years are an exceptionally dynamic and critical period of structural and functional development of the human brain. Many neurodevelopmental disorders are the consequence of abnormal brain development during this stage. Several NIH-funded studies have recently acquired and released large-scale infant brain MRI datasets in the National Institute of Mental Health Data Archive (NDA), leading to over 3,000 publically-available infant MRI scans from multiple imaging sites. Joint analysis of these big data of infant brains will undoubtedly improve our limited understanding of normative early brain development and neurodevelopmental disorders with boosted statistical power and reproducibility. However, the processed and harmonized data of these multi-site infant MR images still remain publically absent, due to the challenges in processing and analyzing infant MR images, which typically exhibit extremely low tissue contrast, large within-tissue intensity variations, and regionally-heterogeneous dynamic changes. To address this critical issue, the goal of this project is to comprehensively process, harmonize, discover and archive large-scale, multi-site public infant MRI datasets to significantly advance early brain development studies, by taking advantage of our infant-tailored computational tools and further developing advanced machine learning techniques. In Aim 1, we will extensively process large-scale infant MRI datasets by adopting our established and recently-improved infant- dedicated cortical surface-based computational tools and further develop a deep spherical neural network for quality control of produced cortical property maps. This will lead to quality-ensured vertex-wise maps of multiple biologically-distinct cortical properties, e.g., cortical thickness, surface area, myelin content, sulcal depth, local gyrification, curvature and diffusivity. In Aim 2, to remove site effects associated with different scanners and imaging protocols and meanwhile preserve biological associations, we will harmonize the computed cortical property maps from multi-site data in Aim 1 by leveraging our surface-to-surface cycle-consistent generative adversarial networks (S2SGAN) based on the spherical U-Net, without requiring traveling subjects (paired data) across sites. To further increase the efficiency and learn more robust feature representation in the whole multi- site data, we propose to extend S2SGAN to jointly harmonize all multi-site cortical property maps using a single generator. In Aim 3, leveraging the informative growth patterns and gradient information of the harmonized maps of multiple cortical properties in Aim 2, we will discover distinct cortical regions, by capitalizing on multi-view nonnegative matrix factorization in a data-driven manner, without making any assumption on the parametric forms of growth patterns. All our processed data, results, computational tools, and source codes will be deposited into NDA, NITRC, and GitHub to significantly accelerate the pace of early brain development studies.

项目摘要 出生后的前几年是结构和功能发展的异常动态和关键时期 人类大脑。许多神经发育障碍是脑发育异常的结果 在此阶段。 NIH资助的几项研究最近已获得并释放了大规模的婴儿脑MRI 美国国家心理健康数据档案馆（NDA）的数据集，导致3,000多个公开可用 来自多个成像位点的婴儿MRI扫描。毫无疑问，对婴儿大脑的这些大数据的联合分析将 提高我们对规范早期大脑发展和神经发育障碍的有限理解 增强统计能力和可重复性。但是，这些多站点的处理和协调数据 由于处理和分析婴儿MR的挑战，婴儿MR图像仍然在公开缺席。 图像通常表现出极低的组织对比度，较大的内部内部强度变化，并且 区域性的动态变化。为了解决这个关键问题，该项目的目标是 全面处理，协调，发现和存档大规模的多站点公共婴儿MRI数据集 通过利用我们的婴儿训练，可以显着提高早期大脑发育研究 计算工具并进一步开发高级机器学习技术。在AIM 1中，我们将 通过采用我们已建立且已改良的婴儿 - 广泛处理大规模婴儿MRI数据集 专用皮质表面的计算工具，并进一步开发一个深层的球形神经网络 生产的皮质特性图的质量控制。这将导致质量确定的顶点图 生物赋予的皮质特性，例如皮质厚度，表面积，髓磷脂含量，沟深度，局部 旋转，曲率和扩散率。在AIM 2中，要删除与不同扫描仪相关的现场效应， 成像协议并保留生物学关联，我们将协调计算的皮质 通过利用我们的地表到表面周期的生成剂，来自AIM 1中多站点数据的属性图 基于球形U-NET的对抗网络（S2SGAN），无需旅行主体（配对数据） 跨站点。为了进一步提高效率并学习整个多的多特征表示 站点数据，我们建议扩展S2SGAN，以共同使用一个单一的多站点皮质图来协调所有多站点的皮质图 发电机。在AIM 3中，利用统一图的信息丰富的增长模式和梯度信息 在AIM 2中的多个皮质特性中，我们将通过利用多视图来发现不同的皮质区域 以数据驱动的方式进行非负矩阵分解，而无需对参数进行任何假设 生长模式的形式。我们所有处理的数据，结果，计算工具和源代码都将存放 进入NDA，NITRC和GitHub，以显着加速早期大脑发育研究的速度。