NCS-FO: Collaborative Research: A Mechanistic Model of Cognitive Control

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Cognitive control is the ability to guide our thoughts and actions in accord with our internal intentions. It enables us to make good decisions, balance options, choose appropriate behaviors and inhibit inappropriate behaviors. Yet our understanding of how cognitive control works in the brain is critically lacking. The research outlined in this proposal will address this outstanding problem by developing and validating a mechanistic model to explain the fundamental principles enabling cognitive control. This problem is of urgent national interest and clinical relevance: greater understanding of how brain structure gives rise to cognitive control may be critical for the development of earlier and more effective treatments of the many neuropsychiatric disorders where cognitive control deficits are present. In addition, this project will create new research opportunities for undergraduate and graduate students in neuroscience, network theory, data sciences, and mathematics. The investigators will integrate the research into undergraduate and graduate teaching activities, providing a powerful bridge between theoretical and experimental applications for students at the University of Pennsylvania and the University of California at Riverside, one of America's most ethnically diverse research-intensive institutions. The investigators will also incorporate this material in extensive community and educational outreach efforts, in addition to translating this knowledge to mental health clinics. In this research project, the investigators seek to develop, validate, and test a mechanistic theory of cognitive control. They postulate that the regulation of cognitive function is driven by a network-level control process akin to those utilized in technological, cyberphysical, and social systems. Their approach is grounded in network control theory, a relatively new subdiscipline of control and dynamical systems. In contrast to the descriptive statistics of graph theory, network control theory offers a principled mathematical modeling framework to inject energy into a networked system leading to a predictable alteration in the system's dynamics. Traditionally applied to mechanical and technological systems, this field builds on notions of structural controllability to ask specific questions about the difficulty of the control task and how to design realistic control strategies in finite time, with limited energy resources. The work will (i) develop a network-based theory of cognitive control informed by neuroimaging data, (ii) validate a network-based theory of cognitive control using data-informed computational models, (iii) define how network structure impacts individual differences in cognitive control performance in adults undergoing cognitive training, and (iv) release a publicly available toolbox for network controllability analysis. These theories and tools are the result of a truly integrated and cross-disciplinary approach to cognitive control, which blends the engineering and data sciences with empirical methodologies in neuroscience.