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.

了解儿童早期学习是如何发生的，有可能改变我们对 人类学习和我们建造智能机器的方法，但在儿童早期是关键的窗口 保持抽样不足，因此提供的关于学习的洞察力很少。一个基本原则和 人类学习中长期存在的问题是神经对视觉字母的特化过程 而数字处理出现在一年级。这一知识对于解决公共卫生问题至关重要 关注阅读和数学素养，因为一年级的字母和数字知识是 对未来阅读和数学能力的最强预测者，以及在阅读和数学方面落后的孩子 在小学，成年后可能会经历医疗和经济上的不稳定。这个项目雇用了 理解儿童学习的多层次方法将支持在以下方面的关键进步 几个学科，包括人类和人工学习，发展和认知神经科学， 教育神经科学、神经成像方法、计算机视觉和广泛的学习科学。这个 第一个目标是创建和分发来自芝麻街剧集的大量图像，这些图像被注释为 教育内容，如字母和数字，以及其他常见对象类别。形象 语料库将是第一个捕捉儿童学习者的视觉统计数据的语料库，并可用于训练不同的 人工学习架构，以更好地理解人类的学习。第二个目标是收集， 对密集的大脑结构和功能的纵向MRI数据集进行预处理和分发 一年级期间的多个时间点。密集的纵向MRI数据集将提供 对芝麻街语料库中的图像进行实验测量的大脑反应将是有益的 用于理解人类的学习，并具有适当的规模来约束人工学习体系结构。 第三个目标是评估作为学习的字母和数字的选择性神经处理的出现 发生在学校的第一年。这个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特化出现的过程，即 电机系统在新兴的专业化。了解脑功能变化的时间进程 早期学习中的结构对于开发对长期生活结果的准确预测至关重要 并用于识别具有极大可塑性的敏感窗口，以优化干预时间线。