Individual Differences in Brain Development and Decision-Making

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Individual differences in neurocognitive function shape behavior throughout the lifespan influencing developmental and clinical outcomes. Computational modeling offers unique insights into these differences by enabling theories of cognitive and neural mechanisms of decision-making to be formalized in precise, testable frameworks. In this talk, I will highlight three lines of research leveraging complementary computational methods that deepen our understanding of the organization of relevant brain networks and decision-making in humans. First, I will demonstrate how unsupervised machine learning in newborn fMRI data uncovers the intrinsic organization of functional brain networks, providing insight into individual differences during the earliest stages of neurodevelopment. Next, I will show how multivariate predictive modeling paired with a "small data informs big data" approach can reveal the directional effects of sleep duration on adolescent brain connectivity development with implications for cognition and mental health. Third, I will discuss how mechanistic computational models disentangle the cognitive processes underlying adolescent delay discounting (the preference for immediate rewards over larger delayed rewards), and how these model-based mechanisms predict broader individual differences in cognition and psychopathology. Together, these lines of inquiry illustrate how computational methods enhance measurement precision and refine our theoretical understanding of neurodevelopment and impulsive decision-making. I will conclude by outlining future directions to bridge neural and behavioral data, focusing on using a formal joint modeling framework to identify neurocognitive mechanisms of decision-making relevant to adolescent substance use risk.