Computational Diffusion MRI for Studying Early Human Brain Development

人脑早期发育的计算扩散MRI研究 摘要 在生命的最初几年，人类大脑在结构和功能方面都在动态发展。许多神经发育- 在早期大脑发育的关键时期， 发展纵向婴儿MRI数据的可用性日益增加，例如通过婴儿MRI获得的数据。 连接组计划（BCP）为精确绘制早期大脑发育图提供了前所未有的机会。 轨迹，以了解正常和异常的增长。需要专用的计算工具， 准确处理和分析婴儿MR图像，这些图像通常表现出动态的异质变化 穿越时空该项目的目标是为大脑研究人员提供有效的计算工具， 在组织微结构和使用扩散的白色物质通路方面， 核磁共振 我们提出三个目标。在目标1中，我们将开发有效估计白色物质的计算工具 通过弥散纤维束描记术观察婴儿大脑中的神经通路我们将应对在各个地区进行跟踪的挑战， 由于发育中的脑中正在进行的髓鞘形成而具有低的扩散各向异性。我们的工具将允许适当的 表征复杂的白色物质路径模式，如扇形和弯曲。这将有助于解决 在现有的纤维束成像算法中普遍存在的旋转偏差问题， 在脑回冠而不是沟岸上生长。我们的工具将允许追踪皮质-皮质和皮质-皮质下 皮层覆盖更均匀的通路。在目标2中，我们将开发微观结构分析方法， 不受复杂纤维形态（如交叉、弯曲、分支、吻状）混淆的物体， 扇形，允许更准确和具体地表征早期组织微结构的变化 大脑发育在目标3中，我们将开发技术，使扩散MRI数据收集在多个 协调在大数据时代非常常见的网站，以减轻网站间的负面影响 可变性与现有的方法，协调派生的数量，如分数各向异性，我们的方法， 可以直接应用于扩散加权图像，允许基于微观结构的测量， 随后计算连通性以进行一致性分析。我们还将开发深度学习工具， 多壳数据预测，以便可以协调使用不同数量壳收集的扩散MRI数据。 该项目的成功完成将使神经科学界能够使用计算工具更好地 利用弥散磁共振成像绘制人类大脑的正常早期发育图。开发的工具还将 能够对患有神经发育障碍的儿童进行定量脑部检查。

Computational Diffusion MRI for Studying Early Human Brain Development Abstract In the first years of life, the human brain develops dynamically in both structure and function. Many neurodevel- opmental disorders are associated with aberrations from normative growth during this critical period of early brain development. The increasing availability of longitudinal baby MRI data, such as those acquired through the Baby Connectome Project (BCP), affords unprecedented opportunity for precise charting of early brain developmental trajectories in order to understand normative and aberrant growth. Dedicated computational tools are needed for accurate processing and analysis of baby MR images, which typically exhibit dynamic heterogeneous changes across time. The goal of this project is to equip brain researchers with computational tools effective for studying the early developing human brain in terms of tissue microstructure and white matter pathways using diffusion MRI. We propose three aims. In Aim 1, we will develop computational tools for effective estimation of white matter pathways in the baby brain via diffusion tractography. We will tackle the challenge of tracking through regions with low diffusion anisotropy owing to ongoing myelination in the developing brain. Our tools will allow proper characterization of complex white matter pathway patterns such as fanning and bending. This will allow solving the gyral bias problem ubiquitous in existing tractography algorithms with fiber streamlines terminating predomi- nantly at gyral crowns but not sulcal banks. Our tools will allow tracing of cortico-cortical and cortico-subcortical pathways with more uniform coverage of the cortex. In Aim 2, we will develop microstructural analysis meth- ods that are unconfounded by complex fiber configurations, such as crossing, bending, branching, kissing, and fanning, allowing more accurate and specific characterization of changes in tissue microarchitecture during early brain development. In Aim 3, we will develop techniques that will allow diffusion MRI data collected at multiple sites, which are very common in the era of big data, to be harmonized to mitigate the negative effects of inter-site variability. Unlike existing methods that harmonize derived quantities such as fractional anisotropy, our method can be applied directly to the diffusion-weighted images, allowing measurements based on microstructure and connectivity to be subsequently computed for consistent analysis. We will also develop deep learning tools for multi-shell data prediction so that diffusion MRI data collected with different numbers of shells can be harmonized. Successful completion of this project will empower the neuroscience community with computational tools to better chart the normative early development of the human brain using diffusion MRI. The developed tools will also enable quantitative brain examinations of children who are affected by neurological developmental disorders.