Computational Diffusion MRI for Studying Early Human Brain Development

用于研究人类早期大脑发育的计算扩散 MRI 摘要 在生命的最初几年，人类大脑的结构和功能都在动态发展。许多神经发育 视神经疾病与早期大脑发育关键时期的正常生长异常有关 发展。纵向婴儿 MRI 数据的可用性不断增加，例如通过婴儿获得的数据 连接组项目 (BCP) 为精确绘制早期大脑发育图表提供了前所未有的机会 轨迹，以了解规范和异常的增长。需要专用的计算工具 准确处理和分析通常表现出动态异质变化的婴儿 MR 图像 跨越时间。该项目的目标是为大脑研究人员配备有效的计算工具来进行研究 使用扩散研究早期发育的人类大脑的组织微观结构和白质通路 核磁共振成像。 我们提出三个目标。在目标 1 中，我们将开发有效估计白质的计算工具 通过扩散纤维束描记术观察婴儿大脑中的通路。我们将应对跨区域追踪的挑战 由于发育中的大脑中持续的髓鞘形成，具有低扩散各向异性。我们的工具将允许正确 复杂白质通路模式的表征，例如扇形和弯曲。这将允许解决 回旋偏差问题在现有纤维束成像算法中普遍存在，纤维流线终止了主要 南特在环冠但不是脑沟岸。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 不受复杂纤维配置（例如交叉、弯曲、分支、接吻和交叉）的影响 扇形，可以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，我们将开发允许在多个位置收集扩散 MRI 数据的技术。 大数据时代非常常见的站点需要进行协调，以减轻站点间的负面影响 可变性。与协调派生量（例如分数各向异性）的现有方法不同，我们的方法 可以直接应用于扩散加权图像，允许基于微观结构和 随后计算连接性以进行一致的分析。我们还将开发深度学习工具 多壳数据预测，以便协调不同数量壳收集的扩散 MRI 数据。 该项目的成功完成将为神经科学界提供计算工具，以更好地 使用扩散 MRI 绘制人类大脑早期规范发育的图表。开发的工具还将 能够对受神经发育障碍影响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.