Postdoctoral Fellowship: SPRF: The Role of White Matter in Learning and Retention of Knowledge Across Academic Interruptions

About This Grant

Under the sponsorship of Dr. Franco Pestilli at The University of Texas at Austin, this postdoctoral fellowship award supports an early career scientist investigating white matter microstructure before and after a learning interruption. Education is critical for lifelong success. Understanding why some children do well while others struggle with education is critical for helping children grow in healthy and positive ways. Early math skills, in particular, can predict whether someone goes to college, chooses a STEM major, gets certain jobs, and how much money they might earn in the future. Therefore, making strides at closing the math achievement gap, in particular, could be essential for creating new pathways for children and families. Breaks in the learning process due to life events or simply education interruptions (e.g., long-term hospitalization or summer breaks) reduce academic success. Interruptions do not affect all school subjects equally. Math skills are more likely than reading skills to decline during breaks from school, such as the summer vacation, and these learning breaks are thought to play a role in the persistence of the math achievement gap. While extensive research in animal models has demonstrated the role of white matter in learning, research in human neuroscience has not yet fully specified the conditions under which similar mechanisms are necessary in humans. To date, the way the brain supports math achievement is much less studied than reading achievement. More specifically, almost no research has shown how brain connections (critical for cognitive function and development) affect math-learning and maintenance. The proposed research will provide important information about how brain white matter connections translate to help young people learn math skills and build resilience to education interruptions. Identifying brain-based risk factors for math underachievement could guide the future design of intervention programs. The current proposal will leverage multi-time-point pediatric diffusion magnetic resonance imaging (dMRI) datasets, which contain rich demographic and academic achievement data, to address a critical research gap on the role of white matter in learning—particularly during educational interruptions in youth. This project aims to fill this gap by investigating the conditions under which white matter and the brain connections it supports can predict math skills and changes in math skills over learning interruptions. The project will utilize advanced dMRI techniques and machine learning models. Two fundamental hypotheses will be studied: (1) that white matter tissue properties will be associated with children's math abilities and (2) that white matter tissues properties before a learning interruption will predict changes in math abilities. As artificial intelligence (AI) methods are developed to improve educational outcomes, understanding the fundamental brain processes that predict academic underachievement and skill loss can be used to guide personalized AI-based interventions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.