Computational Diffusion MRI for Studying Early Human Brain Development

计算扩散磁共振成像在人脑早期发育研究中的应用 摘要 在生命的最初几年，人脑在结构和功能上都是动态发展的。很多神经病患者- 在大脑早期的这一关键时期，视功能障碍与正常发育的异常有关。 发展。纵向婴儿MRI数据的可用性不断提高，例如通过Baby获取的数据 Connectome Project(BCP)为精确绘制早期大脑发育图提供了前所未有的机会 以了解正常和异常增长的轨迹。需要专用的计算工具来 准确处理和分析婴儿磁共振图像，这些图像通常显示出动态的异质变化 跨越时间。这个项目的目标是为大脑研究人员配备有效的研究计算工具 早期发育的人脑组织微结构和脑白质扩散途径 核磁共振检查。 我们提出了三个目标。在目标1中，我们将开发计算工具来有效地估计白质 通过弥散纤维束成像研究婴儿大脑中的通路。我们将应对跨区域跟踪的挑战 由于发育中的大脑正在进行髓鞘形成，扩散各向异性较低。我们的工具将允许适当的 表征复杂的白质通路模式，如扇形和弯曲。这将允许解决 在现有的fi-BER算法中普遍存在的回转偏差问题流水线终止优势. 只盯着旋转的王冠，不盯着凹陷的银行。我们的工具将允许追踪皮质-皮质和皮质下皮质 大脑皮层覆盖更均匀的通路。在目标2中，我们将开发微结构分析方法-- 不会被复杂的fi和fi组合混淆的ods，例如交叉、弯曲、分枝、接吻和 Fning，允许更准确和特异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地点收集的磁共振数据扩散的技术 大数据时代非常常见的站点，需要进行协调，以缓解站点间的负面影响 可变性。与现有的协调导出量(如分数各向异性)的方法不同，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 连通性将在随后计算，以进行一致的分析。我们还将开发深度学习工具，用于 多贝壳数据预测，使得用不同贝壳数量采集的扩散磁共振数据能够得到协调。 该项目的成功完成将使神经科学界拥有更好的计算工具 使用弥散磁共振成像绘制人脑的标准早期发育图。开发的工具还将 能够对受神经发育障碍影响的儿童进行脑部定量检查。

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.