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

正如我们所知，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，或科学和数学概念，我们创造语言，形成词汇知识，发展物理直觉，锻炼逻辑推理，并获得我们工作领域的专业知识。总的来说，这些关系数据可以被描述为一个图，其中节点可能表示音节或概念，而边可能表示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的能力，或者影响学习的神经过程，还远未被理解。在这个项目中，PI将使用网络科学作为数学框架，在其中她将研究人类对关系模式的学习，并回答在数学或自然意义上复杂的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。为了促进更广泛的影响，这些努力结合艺术将STEM转化为STEAM，这是最近的一项国际创新，可以提高内容的长期保留和科学推理。这个项目的目标是(i)创建一个本地社区——从学龄前儿童到成年人——他们通常受到前沿科学的启发，并且更具体地欣赏自然信息和大脑中网络架构的概念。（ii）培养在网络科学和神经科学之间的跨学科边界训练的本科生和研究生，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）完善和发布针对这些目标开发的教学材料，国际和全球合作者，以及公众。作为这些努力的补充，PI为STEM领域的女性和代表性不足的少数族裔提供了广泛的指导，并在服务不足的费城市中心学校开展了教育推广工作。特别是，PI将使用三管齐下的方法，采用(i)基于网络科学的工程工具，系统地定义具有可分离拓扑的关系信息的图集成，（ii）行为研究，确定哪些图拓扑更容易或更难学习，以及（iii）功能神经成像，以识别学习中的个体差异的预测因子。探索性工作旨在将在这些领域获得的知识转化为对科学概念的指导学习。在这份提案中，PI汇集了她在理论物理和网络科学方面的背景，她在多模态人类神经成像方面的专业知识，她目前的研究项目交叉工程和认知神经科学，她最近开发了一种方法来预测人类大脑功能连接的动态学习中的个体差异，以确定关系信息的图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中的个体差异。

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.