Harmonizing and Archiving of Large-scale Infant Neuroimaging Data

项目摘要 出生后的第一年是结构和功能发育的一个异常活跃和关键的时期 人类大脑的。许多神经发育障碍都是大脑发育异常的结果。 在这个阶段。美国国立卫生研究院资助的几项研究最近获得并发布了大规模的婴儿大脑核磁共振 国家心理健康数据档案(NDA)中的数据集，导致超过3,000个公众可用 婴儿核磁共振从多个成像地点进行扫描。对这些婴儿大脑大数据的联合分析无疑将 改善我们对正常早期大脑发育和神经发育障碍的有限理解 增强了统计能力和再现性。然而，这些多站点的数据处理和协调 由于处理和分析婴儿磁共振的挑战，婴儿磁共振图像仍然没有出现在公众面前 图像，通常显示极低的组织对比度，组织内强度变化大，以及 区域--非均质动态变化。为了解决这一关键问题，该项目的目标是 全面处理、协调、发现和归档大规模、多站点的公共婴儿MRI数据集，以 通过利用我们为婴儿量身定做的优势，显著推进早期大脑发育研究 计算工具和进一步开发先进的机器学习技术。在目标1中，我们将 通过采用我们建立和最近改进的婴儿-广泛处理大规模婴儿MRI数据集- 专用的基于皮质表面的计算工具，并进一步开发了一个深度球面神经网络 制作的皮质属性图的质量控制。这将产生质量有保证的多个顶点地图 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 回旋度、曲率和扩散率。在目标2中，消除与不同扫描仪相关的现场效果和 成像方案，同时保留生物关联，我们将协调计算的皮质 通过利用我们的地表到地表循环一致的生成器，从AIM 1中的多站点数据生成属性地图 基于球形U网的对抗性网络(S2SGAN)，不需要旅行对象(配对数据) 跨站点。为了进一步提高效率，并学习更健壮的特征表示在整个多 站点数据，我们建议扩展S2SGAN以使用单一的 发电机。在目标3中，利用协调地图的信息性增长模式和梯度信息 在目标2中，我们将通过利用多个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被存放 进入NDA、NITRC和GitHub，显著加快早期大脑发育研究的步伐。

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.