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

University Of Pennsylvania, Philadelphia PA

Investigators

Abstract

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.