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 出生后的头几年是结构和功能发育的一个异常动态和关键时期 人类大脑。许多神经发育障碍是大脑发育异常的结果 在这个阶段。几个NIH资助的研究最近获得并发布了大规模的婴儿大脑MRI 美国国家心理健康研究所数据档案（NDA）中的数据集，导致超过3,000个公开可用的 多个成像部位的婴儿MRI扫描。联合分析这些婴儿大脑的大数据， 通过以下方式提高我们对规范早期大脑发育和神经发育障碍的有限了解 提高了统计能力和可重复性。然而，这些多站点的处理和协调数据 由于在处理和分析婴儿MR方面的挑战，婴儿MR图像仍然在医学上缺失 图像，其通常表现出极低的组织对比度、大的组织内强度变化，以及 区域异质性动态变化。为了解决这一关键问题，本项目的目标是 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， 通过利用我们为婴儿量身定制的 计算工具和进一步开发先进的机器学习技术。在目标1中，我们 广泛处理大规模的婴儿MRI数据集，采用我们建立和最近改进的婴儿- 专用的基于皮层表面的计算工具，并进一步开发一个深球形神经网络， 制作的皮质特性图的质量控制。这将导致质量保证的顶点明智的地图， 生物学上不同的皮质特性，例如，皮质厚度，表面积，髓鞘含量，脑沟深度，局部 旋转、曲率和扩散率。在目标2中，为了消除与不同扫描仪相关的现场影响， 成像协议，同时保留生物关联，我们将协调计算的皮层 通过利用我们的表面到表面周期一致性生成， 对抗网络（S2 SGAN）基于球形U网，不需要移动对象（配对数据） 跨站点。为了进一步提高效率，并在整个多特征表示中学习更鲁棒的特征表示， 站点数据，我们建议扩展S2 SGAN，使用单个 生成器.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 目标2中的多个皮层特性，我们将通过利用多视图来发现不同的皮层区域， 以数据驱动的方式进行非负矩阵分解，而不对参数进行任何假设 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存放在 NDA、NITRC和GitHub，以显著加快早期大脑发育研究的步伐。