CRCNS Research Proposal: Coupled Learning for Anatomically and Developmentally Consistent Analysis of Macaque-Human Fetal Brain Growth

CRCNS 研究提案：耦合学习对猕猴-人类胎儿大脑生长的解剖学和发育一致性分析

• 批准号: 2011274
• 负责人: Christopher Kroenke
• 金额: $ 34.88万
• 依托单位: Oregon Health & Science University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2011274&HistoricalAwards=false
• 关键词: CRCNS; Research; Proposal; Coupled; Learning

Neurodevelopmental disorders such as autism spectrum disorder, attention deficit/hyperactivity disorder, fetal alcohol spectrum disorders, and complications associated with premature birth, impact the quality of life of affected individuals over the entire lifespan. Neuroanatomical anatomical differences between people affected by these conditions and “typically developing” individuals have been identified with magnetic resonance imaging (MRI), but the biological mechanisms leading to such differences, and their link to the disease processes, are incompletely understood. It is safe to perform MRI on pregnant women, and recent developments in the ability to account for fetal motion during image acquisition, have enabled high-resolution 3D imaging of the fetal brain. In order to better understand the developmental mechanisms that underlie the trajectory of anatomical changes observed by MRI in humans, longitudinal measurements are also collected in nonhuman primates. Human and nonhuman primates share many similarities in both brain structure and function that allow findings in one to be translated to the other. Advantages of nonhuman primate studies are that many factors that may vary between human pregnancies can be experimentally controlled, and much more detailed longitudinal imaging is possible. This research will develop computational approaches to more precisely link fetal growth between human and nonhuman brains. The work leverages unique human and nonhuman primate imaging datasets with new methods for systematically labeling the brain into corresponding sub-regions and establish closer links between developmental events. This increased precision will enhance knowledge gained from ongoing human observational studies and enable new clinical approaches to address neurodevelopmental diseases. The ability to non-invasively monitor fetal brain growth in both human and nonhuman primates using magnetic resonance imaging (MRI) provides a new opportunity to characterize brain development with longitudinal experimental designs. However, given the increased frequency with which data can be acquired, and quality of high-resolution images, an important new limitation is the inability to translate developmental time points between species at the level of precision of the acquired data. Conventional approaches for studying postnatal brain images utilize processing steps such as spatial normalization to a common anatomical coordinate frame, segmentation into tissue classes, and parcellation into known neuroanatomical regions. Adaptation of these techniques to study the developing fetal brain requires age and species-specific definitions for quantities such as transient developmental zones, or emergence of cortical gyri and sulci. This project makes use of increasingly powerful machine learning techniques and leverages the increasingly rich fetal imaging data now being collected, to extract consistent cross-species measures of brain development. An additional objective is to develop fine scale anatomically and temporally consistent definitions across species. These neuroanatomically localized definitions will then be used to quantify regional morphometric growth during fetal brain development in both species. This work contributes to the computational science and the neuroscience that supports neuroimaging studies of fetal brain development. These developments will provide a new translational resource to link anatomically and temporally specific information about brain development, both in normal growth and in clinical and animal model experiments focused on neurodevelopmental disorders.This award is being co-funded by the CISE Information and Intelligent Systems (IIS) through the CRCNA and BRAIN Programs, and the MPS Division of Mathematical Sciences (DMS) through the Mathematical Biology Program.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

神经发育障碍，如自闭症谱系障碍、注意缺陷/多动障碍、胎儿酒精谱系障碍以及与早产相关的并发症，会影响患者整个生命周期的生活质量。受这些疾病影响的人与“正常发育”个体之间的神经解剖学差异已通过磁共振成像（MRI）确定，但导致这种差异的生物学机制及其与疾病过程的联系尚不完全清楚。对孕妇进行核磁共振成像是安全的，最近在图像采集过程中胎儿运动的能力方面取得了进展，使胎儿大脑的高分辨率3D成像成为可能。为了更好地理解人类MRI观察到的解剖变化轨迹背后的发育机制，还收集了非人类灵长类动物的纵向测量数据。人类和非人类灵长类动物在大脑结构和功能上有许多相似之处，这使得在一种动物身上的发现可以转化为另一种动物。非人类灵长类动物研究的优势在于，许多可能在人类怀孕期间发生变化的因素可以通过实验来控制，并且可以进行更详细的纵向成像。这项研究将发展计算方法，以更精确地将人类和非人类大脑之间的胎儿生长联系起来。这项工作利用独特的人类和非人类灵长类动物成像数据集，采用新方法系统地将大脑标记为相应的子区域，并在发育事件之间建立更紧密的联系。这种精确度的提高将增强从正在进行的人类观察性研究中获得的知识，并使新的临床方法能够解决神经发育疾病。利用磁共振成像（MRI）无创监测人类和非人类灵长类动物胎儿大脑发育的能力，为纵向实验设计表征大脑发育提供了新的机会。然而，鉴于获取数据的频率增加和高分辨率图像的质量，一个重要的新限制是无法在获得数据的精度水平上转换物种之间的发育时间点。研究产后脑图像的传统方法利用处理步骤，如空间归一化到一个共同的解剖坐标框架，分割成组织类，并分割成已知的神经解剖区域。适应这些技术来研究发育中的胎儿大脑需要年龄和物种特定的数量定义，如短暂发展区，或皮层回和脑沟的出现。该项目利用日益强大的机器学习技术，并利用目前收集的日益丰富的胎儿成像数据，提取一致的跨物种大脑发育指标。另一个目标是发展跨物种的精细尺度解剖学和时间一致的定义。这些神经解剖学上的局部定义将用于量化两个物种胎儿大脑发育过程中的区域形态测量生长。这项工作有助于计算科学和神经科学，支持胎儿大脑发育的神经成像研究。这些发展将提供一种新的翻译资源，将正常生长和神经发育障碍的临床和动物模型实验中有关大脑发育的解剖学和时间特异性信息联系起来。该奖项由CISE信息与智能系统（IIS）通过CRCNA和BRAIN项目和MPS数学科学部（DMS）通过数学生物学项目共同资助。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。