CRCNS: Dense longitudinal neuroimaging to evaluate learning in childhood

CRCNS：密集纵向神经影像评估儿童学习情况

• 批准号: 10835136
• 负责人: Sophia Vinci-Booher
• 金额: $ 33.05万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-11 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10835136
• 关键词: 6 year old; 7 year old; Address; Adult; Algorithms; Architecture; Artificial Intelligence; Awareness; Biological Markers; Brain; Brain scan; Categories; Child; Childhood; Cognitive; Communities; Computer Vision Systems; Data; Data Set; Development; Digit structure; Discipline; Education; Educational Intervention; Educational workshop; Emotional; Face; Functional Magnetic Resonance Imaging; Future; Goals; Growth; Human; Image; Intervention; Investigation; Knowledge; Learning; Letters; Life; Magnetic Resonance Imaging; Mathematics; Measures; Medical; Methodology; Methods; Motor; Neurosciences; Outcome; Process; Public Health; Reading; Research; Research Infrastructure; Resources; Risk; Role; Sampling; Schools; Science; Sesame - dietary; Software Tools; Structure; System; Techniques; Time; Training; Visual; cognitive neuroscience; computational neuroscience; computer science; deep learning model; early childhood; elementary school; experience; falls; first grade; hands-on learning; human model; insight; literacy; mathematical ability; mathematical learning; neural; neuroimaging; neuroinformatics; reading ability; response; social; statistics; timeline

Understanding how learning occurs in early childhood has the potential to transform our understanding of human learning and our approach to building intelligent machines, yet critical windows in early childhood remain under-sampled and consequently provide little insight concerning learning. One fundamental and long-standing question in human learning is the process by which neural specialization for visual letter and digit processing emerges in the first grade. This knowledge is critical for addressing public health concerns related to reading and math literacy because first-grade letter and digit knowledge are the strongest predictors of future reading and math abilities, and children who fall behind in reading and math in elementary school will likely experience medical and financial instability as adults. This project employs a multi-level approach to understanding learning in childhood that will support critical advancements in several disciplines, including human and artificial learning, developmental and cognitive neuroscience, educational neuroscience, neuroimaging methods, computer vision, and learning sciences broadly. The first aim is to create and distribute a large corpus of images from Sesame Street episodes annotated for educational content, such as letters and digits, as well as for other common object categories. The image corpus will be the first to capture the visual statistics of child learners and can be used to train different artificial learning architectures to better understand human learning. The second aim is to collect, preprocess, and distribute a dense longitudinal MRI dataset of brain structure and function sampled at multiple time points throughout the first grade year. The dense longitudinal MRI dataset will provide experimentally measured brain responses to images from the Sesame Street corpus that will be of benefit for understanding human learning and of appropriate scale for constraining artificial learning architectures. The third aim is to evaluate the emergence of selective neural processing for letters and digits as learning occurs throughout the first year of schooling. This aim will address an open question in human learning concerning the process by which neural specialization for letters and digits emerges, namely the role of the motor system in emerging specialization. Understanding the time course of changes in brain function and structure during early learning is critical for developing accurate predictors of long-term life outcomes and for identifying sensitive windows of great plasticity to optimize intervention timelines.

了解幼儿期学习如何发生有可能改变我们对学习的理解 人类学习和我们构建智能机器的方法，但幼儿期的关键窗口 样本仍然不足，因此无法提供有关学习的见解。一项基本的和 人类学习中长期存在的问题是视觉字母的神经专门化的过程 数字处理在一年级就开始出现。这些知识对于解决公共卫生问题至关重要 与阅读和数学素养相关的担忧，因为一年级的字母和数字知识是 未来阅读和数学能力的最强预测因素，以及阅读和数学落后的孩子 上小学时，成年后可能会经历医疗和财务不稳定。本项目聘用 理解童年学习的多层次方法，将支持儿童时期的关键进步 多个学科，包括人类和人工学习、发展和认知神经科学、 教育神经科学、神经影像方法、计算机视觉和广泛的学习科学。这 第一个目标是创建并分发芝麻街剧集中的大量图像，并注释为 教育内容，例如字母和数字，以及其他常见对象类别。图像 语料库将第一个捕获儿童学习者的视觉统计数据，并可用于训练不同的 人工学习架构可以更好地理解人类学习。第二个目标是收集， 预处理并分发在以下位置采样的大脑结构和功能的密集纵向 MRI 数据集 整个一年级的多个时间点。密集的纵向 MRI 数据集将提供 通过实验测量大脑对芝麻街语料库图像的反应，这将是有益的 用于理解人类学习，并具有适当的规模来约束人工学习架构。 第三个目标是评估字母和数字选择性神经处理作为学习的出现 发生在整个学校教育的第一年。这一目标将解决人类学习中的一个悬而未决的问题 关于字母和数字的神经专门化出现的过程，即 新兴专业中的运动系统。了解大脑功能变化的时间过程 早期学习期间的结构对于开发长期生活结果的准确预测因子至关重要 以及识别具有巨大可塑性的敏感窗口以优化干预时间表。