NCS-FO: Collaborative Research: Understanding Individual Differences in Cognitive Performance: Joint Hierarchical Bayesian Modeling of Behavioral and Neuroimaging Data

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Understanding the complex determinants of individual health and wellbeing is critical for the promotion and maintenance of a healthy world population. Wellbeing may be understood not only as the absence of physical and mental illness but also as the quality of life and optimal functioning of individuals. It is well known that individuals vary tremendously in terms of cognitive abilities and dispositions, as seen from performance on high-order cognitive tasks, decision-making preferences, and emotional competencies. However, the neural underpinnings of much of this variability are poorly understood: It is unclear how individual differences in brain structure and function across tasks and processes are linked to abilities and competencies. This project explores a mathematical and computational framework for investigating a large-sample neuroimaging and behavioral dataset in order to improve our understanding of individual differences in cognitive performance. An ultimate goal of the project is to predict individual cognitive performance in novel, real-world situations based on observed (past) behavioral and neuroimaging data and contribute to the understanding of cognitive health and wellbeing of individuals. The project will also offer many training opportunities for the next generation of scientists. The technical approach will build on and integrate recent advances in cognitive science, neuroscience, statistics, and machine learning. Statistical models will integrate data from both brain imaging and behavioral tests to generate predictions that otherwise may not be possible with a single source of data. The research will go beyond establishing and explaining individual differences to predicting individual cognitive performance in a variety of tasks.