Measuring cortical plate and subplate thickness in the human fetal brain from magnetic resonance images

项目概要/摘要 在胎儿脑中，皮质板（CP）厚度被认为与脑内细胞的数量和大小有关。 柱、堆积密度、皮质内髓鞘和突触，以及与 丘脑和皮质传入神经的数量以及皮质-皮质连接的数量。皮质估计 出生后的MRI厚度极大地促进了我们对人类大脑发育的理解， 认知功能和各种脑部疾病的发病和进展。然而，我们的知识和 由于缺乏可用的技术， 自动测量来自胎儿脑MRI的区域CP和SP厚度。与儿童或成人的大脑相比， 胎儿脑的尺寸小得多，并且具有不同的图像对比度。胎儿脑部MRI显示有效性较低 分辨率，并且遭受导致伪影的头部运动。因此，提取准确的CP具有挑战性 和SP区域，并且在CP和SP表面之间限定几何上适当的厚度。本研究 将开发一个全自动管道，使用多部位胎儿MRI提取区域CP和SP厚度 数据集。我们将发展CP和SP分割的方法，并识别脑沟 使用深度卷积神经网络的脑脊液区域。基于精确的分割， 将开发一种针对胎脑优化和专门化的可变形模型方法来提取CP SP表面。CP和SP厚度将根据所有 CP和SP表面。我们将使用不同的成像子集进行可靠性和敏感性测试， 通过移动CP和SP边界创建相同的主题和人工数据。然后我们将定义增长 妊娠18 - 37周正常发育（TD）胎儿所有皮质区CP和SP厚度的比率 周（GW）。我们假设CP和SP厚度的增长率、最大SP厚度和/或 TD胎儿不同皮质区域的CP厚度的最大生长GW是不同的。的 脑异常（多小脑回和体发育不全）胎儿CP和SP厚度的增长 胼胝体）与TD胎儿的生长进行统计学比较。皮质发育畸形， 皮质-皮质连接可能导致多小脑回畸形胎儿CP和SP厚度的改变 胼胝体发育不全。这项研究将为一种新型生物标志物奠定基础，该生物标志物可以导致 更深入地了解子宫内大脑发育正常和改变的机制。我们的方法 将使用基于网络的神经图像计算平台公开分发， 这将使得胎儿CP和SP厚度分析的更多临床应用成为可能。

PROJECT SUMMARY/ABSTRACT In the fetal brain, cortical plate (CP) thickness is thought to be related to the number and size of cells within a column, packing density, intracortical myelin, and synapses, and subplate (SP) thickness associated with the number of thalamic and cortical afferents and the amount of cortico-cortical connections. Estimation of cortical thickness postnatally with MRI has contributed greatly to our understanding of human brain development and cognitive function and disease onset and progression in various brain disorders. However, our knowledge and research of human in utero CP and SP thickness remains limited due to the lack of available techniques that automatically measure regional CP and SP thickness from fetal brain MRI. Compared to child or adult brains, fetal brains are much smaller in size and have different image contrast. Fetal brain MRI shows lower effective resolution and suffers from head motion which causes artifacts. Thus, it is challenging to extract accurate CP and SP regions and define geometrically appropriate thickness between the CP and SP surfaces. This study will develop a fully automatic pipeline to extract regional CP and SP thickness using multi-site fetal MRI datasets. We will develop the method for CP and SP segmentation with the identification of sulcal cerebrospinal fluid regions using deep convolutional neural networks. Based on the accurate segmentation, a deformable model method that is optimized and specialized for fetal brains will be developed to extract the CP and SP surfaces. CP and SP thickness will be measured based on vertex-wise correspondence between all CP and SP surfaces. We will perform reliability and sensitivity tests using different imaging subsets within the same subject and artificial data created by moving the CP and SP boundary. We will then define the growth rate of CP and SP thickness in all cortical regions in typically developing (TD) fetuses from 18 to 37 gestational weeks (GW). We hypothesize that the growth rate of CP and SP thickness, the maximum SP thickness, and/or the maximum growth GW of CP thickness will be variable across different cortical areas in TD fetuses. The growth of CP and SP thickness in fetuses with cerebral abnormalities (polymicrogyria and agenesis of corpus callosum) will be statistically compared to the growth of TD fetuses. Malformations of cortical development and cortico-cortical connections may result in altered growth of CP and SP thickness in fetuses with polymicrogyria and agenesis of corpus callosum. This study will lay the foundation for a novel biomarker that can lead to greater insight into the mechanisms of normal and altered in utero brain development. Our methods developed from the proposed study will be publicly distributed using a web-based neuroimage computation platform, which will enable more clinical applications of fetal CP and SP thickness analysis.