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: 2011088
• 负责人: Colin Studholme
• 金额: $ 54.14万
• 依托单位: University of Washington
• 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 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2011088&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、注意力缺陷/多动障碍、胎儿酒精谱系障碍和与早产相关的并发症，影响受影响个体整个生命周期的生活质量。受这些条件影响的人和“典型发育”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未完全了解。对孕妇进行MRI是安全的，并且最近在图像采集期间解释胎儿运动的能力方面的发展已经实现了胎儿大脑的高分辨率3D成像。为了更好地理解在人类中通过MRI观察到的解剖学变化轨迹的基础上的发育机制，还在非人灵长类动物中收集了纵向测量结果。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，这使得一个发现可以被翻译到另一个。非人类灵长类动物研究的优势在于，人类怀孕期间可能发生变化的许多因素可以通过实验控制，并且可以进行更详细的纵向成像。这项研究将开发计算方法，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，采用新方法将大脑系统地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。这种增加的精度将提高知识从正在进行的人类观察性研究，使新的临床方法来解决neurodevelopmental diseases.The能力，非侵入性监测胎儿大脑生长在人类和非人类灵长类动物使用磁共振成像（MRI）提供了一个新的机会，以表征大脑发育的纵向实验设计。然而，考虑到数据获取频率的增加以及高分辨率图像的质量，一个重要的新限制是无法在所获取数据的精确度水平上转换物种之间的发育时间点。用于研究出生后脑图像的常规方法利用处理步骤，诸如对共同解剖坐标系的空间归一化、分割成组织类别以及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异性的定义数量，如短暂的发育区，或出现皮质脑回和脑沟。该项目利用越来越强大的机器学习技术，并利用目前收集的越来越丰富的胎儿成像数据，提取一致的跨物种大脑发育指标。另一个目标是开发精细尺度的解剖学和时间上一致的定义跨物种。这些神经解剖学本地化的定义，然后将被用来量化区域形态测量生长在两个物种的胎儿大脑发育过程中。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影像学研究。这些发展将提供一个新的翻译资源，以联系解剖学和时间上的具体信息，大脑发育，无论是在正常生长和临床和动物模型实验集中在神经发育障碍。这个奖项是共同资助的CISE信息和智能系统（IIS）通过CRCNA和脑计划，MPS Division of Mathematical Sciences（DMS）该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查进行评估，被认为值得支持的搜索.

项目成果

Nests of dividing neuroblasts sustain interneuron production for the developing human brain.

• DOI: 10.1126/science.abk2346
• 发表时间: 2022-01-28
• 期刊: Science (New York, N.Y.)
• 作者: Paredes MF;Mora C;Flores-Ramirez Q;Cebrian-Silla A;Del Dosso A;Larimer P;Chen J;Kang G;Gonzalez Granero S;Garcia E;Chu J;Delgado R;Cotter JA;Tang V;Spatazza J;Obernier K;Ferrer Lozano J;Vento M;Scott J;Studholme C;Nowakowski TJ;Kriegstein AR;Oldham MC;Hasenstaub A;Garcia-Verdugo JM;Alvarez-Buylla A;Huang EJ
• 通讯作者: Huang EJ