Abstract: Despite an increased understanding of nicotine addiction, there is a scarcity of research comparing the neural correlates of non‐drug reward between smokers and ex‐smokers. Long‐term changes in reward‐related brain functioning for non‐drug incentives may elucidate patterns of functioning that potentially contribute to ongoing smoking behaviour in current smokers. Similarly, examining the effects of previous chronic nicotine exposure during a period of extended abstinence may reveal whether there are neural correlates responsible for non‐drug reward processing that are different from current smokers. The current study, therefore, sets out to examine the neural correlates of reward and loss anticipation, and their respective outcomes, in smokers, ex‐smokers and matched controls using a monetary incentive delay task during functional magnetic resonance imaging. Here, we report that in the absence of any significant behavioural group differences, both smokers and ex‐smokers showed a significantly greater activation change in the lateral orbitofrontal/anterior insular cortex compared with smokers when anticipating both potential monetary gains and losses. We further report that ex‐smokers showed a significantly greater activation change in the ventral putamen compared with both controls and smokers and in the caudate compared with controls during the anticipation of potential monetary losses only. The results suggest that smoking may sensitize striato‐orbitofrontal circuitry subserving motivational processes for loss avoidance and reward gain in nicotine addiction.
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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).