Abstract: Addiction to cigarettes presents with considerable health risks and induces high costs on healthcare resources. While the majority of cigarette smokers endorse the desire to quit, only a small percentage of quit attempts lead to full abstinence. Failure to achieve abstinence may arise from maladaptive reactivity in fronto-striatal regions that track positive and negative valence outcomes, thus biasing the choice to smoke in the presence of alternative, non-drug reinforcement. Alternatively, long-term nicotine abstinence may reveal neural substrates of adaptive valence outcome processing that promote and maintain smoking cessation. The present study set out to examine the neural correlates of operant response outcomes in current smokers, ex-smokers and matched controls using a monetary incentive delay task during functional MRI. Here we report that compared to controls, both current smokers and ex-smokers showed significantly less activation change in the left amygdala during positive response outcomes, and in the anterior cingulate cortex, during both positive and negative response outcomes. Ex-smokers, however, demonstrated significantly greater activation change compared to smokers and controls in the right amygdala during negative response outcomes. Activation change in the anterior cingulate cortex and middle frontal gyrus of smokers was significantly negatively correlated with nicotine dependence and cigarette pack-years. These results suggest a pattern of shared and divergent reactivity in current smokers and ex-smokers within corticolimbic regions that track both positive and negative operant response outcomes. Exaggerated adaptive processing in ex-smokers may promote long-term smoking cessation through amplified negative valence outcome monitoring.
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Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.
Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.
Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.
Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).