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 出生后最初几年是结构和功能发育异常活跃和关键的时期 人类大脑的。许多神经发育障碍是大脑发育异常的结果 在这个阶段。 NIH 资助的几项研究最近获得并发布了大规模婴儿脑部 MRI 国家心理健康研究所 (NDA) 中的数据集，导致超过 3,000 个公开可用的数据集 来自多个成像部位的婴儿 MRI 扫描。对这些婴儿大脑大数据的联合分析无疑将 提高我们对规范性早期大脑发育和神经发育障碍的有限理解 提高了统计功效和再现性。然而，这些多站点的处理和协调数据 由于处理和分析婴儿 MR 方面的挑战，婴儿 MR 图像仍然不公开 图像，通常表现出极低的组织对比度、大的组织内强度变化，以及 区域异质动态变化。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、多站点公共婴儿 MRI 数据集，以 通过利用我们为婴儿量身定制的技术，显着推进早期大脑发育研究 计算工具并进一步开发先进的机器学习技术。在目标 1 中，我们将 通过采用我们建立的和最近改进的婴儿- 专用的基于皮质表面的计算工具，并进一步开发深度球形神经网络 生成的皮质属性图的质量控制。这将导致多个有质量保证的顶点映射 生物学上不同的皮质特性，例如皮质厚度、表面积、髓磷脂含量、脑沟深度、局部 回转率、曲率和扩散率。在目标 2 中，消除与不同扫描仪相关的场地影响 成像协议，同时保留生物关联，我们将协调计算的皮质 利用我们的地对地循环一致生成模型，从目标 1 中的多站点数据中绘制属性图 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在整个多模型中学习更鲁棒的特征表示 站点数据，我们建议扩展 S2SGAN，以使用单个站点共同协调所有多站点皮质属性图 发电机。在目标 3 中，利用协调地图的信息丰富的增长模式和梯度信息 在目标 2 中，我们将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，无需对参数做任何假设 增长模式的形式。我们所有处理过的数据、结果、计算工具和源代码都将被存放 进入 NDA、NITRC 和 GitHub，显着加快早期大脑发育研究的步伐。