CAREER: Linking Graph Topology of Learned Information to Behavioral Variability via Dynamics of Functional Brain Networks

众所周知，学习关系数据的能力对人类生活至关重要。通过学习音节和单词之间的关系，或者科学和数学概念，我们产生语言，形成词汇知识，发展身体直觉，练习逻辑推理，并获得我们工作中的专业知识。总的来说，这些关系数据可以被描述为一个图，其中节点可能代表音节或概念，边可能代表共享内容或条件概率。然而，这种图的组织如何影响我们学习数据或影响学习的神经过程的能力还远未可知。在这个项目中，PI 将使用网络科学作为数学框架，在其中研究人类对关系模式的学习，并回答数学或自然意义上的复杂图形是否或多或少难以学习，或者需要不同的神经过程的问题。为了产生更广泛的影响，这些努力融入了将 STEM 转变为 STEAM 的艺术，这是一项最新的国际创新，可以改善内容和科学推理的长期保留。该计划的目标是（i）创建一个当地社区 - 从学龄前儿童到成年人 - 他们通常受到尖端科学的启发，并且更具体地欣赏自然信息中的网络架构概念以及他们的大脑学习该信息的能力，（ii）培养本科生和研究生接受网络科学和神经科学之间跨学科边界的培训，以解决超越国界的关键和及时的科学问题，（iii）开发包含这些及时研究问题的课程材料，以及（iv）完善和发布教材为国际和全球合作者以及公众制定这些目标。 PI 通过对 STEM 领域的女性和代表性不足的少数族裔进行广泛指导，以及在服务不足的费城市中心学校中开展教育推广工作来补充这些努力。特别是，PI 将采用三管齐下的方法，采用 (i) 基于网络科学的工程工具来系统地定义具有可分离拓扑的关系信息的图集合，(ii) 行为研究以确定哪些图拓扑更容易或更难学习，(iii) 功能神经成像识别学习个体差异的预测因素。探索性工作旨在将在这些领域获得的知识转化为科学概念的指导学习。在这项提案中，PI 汇集了她在理论物理和网络科学方面的背景、她在多模式人类神经成像方面的专业知识、她目前的交叉工程和认知神经科学的研究项目，以及她最近开发的从人脑功能连接动态预测学习个体差异的方法，以确定关系信息的图形拓扑如何映射到由可分离的神经生理过程产生的人类学习行为的个体差异。

The ability to learn relational data is critical to human life as we know it. By learning the relationships between syllables and words, or scientific and mathematical concepts, we produce language, form lexical knowledge, develop physical intuition, exercise logical deduction, and attain expertise in our line of work. Collectively, these relational data can be described as a graph in which nodes might represent syllables or concepts, and edges might represent shared content or conditional probabilities. Yet, how the organization of such a graph impacts our ability to learn the data or the neural processes that affect learning is far from understood. In this project the PI will use network science as a mathematical framework within which she will study the human learning of relational patterns, and answer the question of whether graphs that are complex in the mathematical or naturalistic senses are more or less difficult to learn, or require different neural processes. To facilitate broader impacts, these efforts incorporate art to transform STEM to STEAM, a recent international innovation that improves long-term retention of content and scientific reasoning. The goals of this program are (i) to create a local community - from preschoolers to adults - who are generally inspired by cutting edge science, and who more specifically appreciate the concepts of network architectures in natural information and in their brain?s ability to learn that information, (ii) to produce undergraduate and graduate students trained at the interdisciplinary boundary between network science and neuroscience to address critical and timely scientific questions that transcend national boundaries, (iii) to develop course material that incorporates these timely research questions, and (iv) to polish and release teaching materials developed in these aims to international and global collaborators, and to the public. The PI complements these efforts with extensive mentorship for women and underrepresented minorities in STEM fields, and with educational outreach efforts in under-served inner-city Philadelphia schools.In particular the PI will use a 3-pronged approach that employs (i) engineering-based tools from network science to systematically define graph ensembles of relational information with dissociable topologies, (ii) behavioral studies to determine which graph topologies are easier or harder to learn, and (iii) functional neuroimaging to identify predictors of individual differences in learning. Exploratory work seeks to translate the knowledge gained in these areas to instructed learning of scientific concepts. In this proposal, the PI brings together her background in theoretical physics and network science, her expertise in multimodal human neuroimaging, her current research program intersecting engineering and cognitive neuroscience, and her recently developed methods to predict individual differences in learning from the dynamics of human brain functional connectivity to determine how the graph topology of relational information maps to individual differences in human learning behavior as produced by dissociable neurophysiological processes.