Continuous longitudinal atlas construction for the study of brain development

摘要： 在围产期，大脑皮层发育迅速。在 为了表征这种复杂的过程，需要时空地图集。目前， 然而，婴儿大脑皮质表面图谱的可用性有限。的 适用于这一群体的强大工具的开发工作明显滞后， 由于难以从不依从的新生儿中获得数据， 以及在发育过程中显示的对比度和几何形状的快速变化， 婴儿。随着大型纵向脑成像研究的出现，如ESTA/UMN婴儿 连接组项目（BCP）和健康大脑和儿童发育（HBCD）研究，新 需要算法解决方案来有效地处理这些数据集。在拟议 项目，我们打算开发一个FreeSurfer兼容的管道，将建立一个一致的， 随着时间的推移，围产期皮层的无偏表示和一套工具，当与 标准的临床MRI采集，将能够计算准确和稳健的球面 发育中的大脑的沿着表示以及详细的宏观结构注释。等 然后，表面将被用来表征健康的大脑发育在头十年的 生活对于我们的临床应用，我们将重点关注早期生活逆境对大脑的影响 发展，以及对解开矛盾的发现，大脑异常，在非常 早产儿，如他们的前颞叶和后颞叶的回旋复杂性。这 工作将在MGH/哈佛/麻省理工学院Martinos生物医学成像中心进行， 在当地合作者收集的数据集上，NIH资助的公开可用的大规模数据集 以及由华盛顿大学的同事组成的临床队列。这种努力 将使我们能够利用尖端的新生儿成像和计算算法 开发专业知识，试图提供足够强大和准确的计算工具， 用于未来的临床研究

Abstract: There is rapid development of the cerebral cortex that takes place during the perinatal period. In order to characterize this complex process spatio-temporal atlases are needed. At present, however, there is limited availability of cortical surface atlases for the infant brain. The development of robust tools applicable to this population has been significantly lagging compared to those introduced for adults given the difficulty of obtaining data from non-compliant neonates and toddlers and the rapid change in contrast and geometry displayed during development in infants. With the advent of large longitudinal brain imaging studies, such as the UNC/UMN Baby Connectome Project (BCP) and HEALthy Brain and Child Development (HBCD) Study, novel algorithmic solutions are needed to efficiently process these data sets. During the proposed project, we intend to develop a FreeSurfer-compatible pipeline that will establish a consistent and unbiased representation of the perinatal cortex over time and a set of tools that, when used with standard clinical MRI acquisitions, will enable the computation of accurate and robust spherical representations of the developing brain along with detailed macrostructural annotations. Such surfaces then will be used to characterize healthy brain development over the first ten years of life. For our clinical application, we will focus on the effects of early life adversity on brain development as well as on disentangling contradictory findings about brain abnormalities in very preterm infants, such as gyrification complexity in their anterior and posterior temporal lobes. This work will be performed at the MGH/Harvard/MIT Martinos Center for Biomedical Imaging relying on datasets collected by local collaborators, NIH-funded publicly available large scale data sets as well as a clinical cohort assembled by colleagues at the Washington University. Such an effort will allow us to take advantage of cutting-edge neonatal imaging and computational algorithm development expertise in an attempt to deliver computational tools robust and accurate enough for future clinical studies