Abstract: Introduction: Research on cigarette smokers suggests cognitive and behavioral impairments.
However, much remains unclear how the functional neurobiology of smokers is influenced by
nicotine state. Therefore, we sought to determine which state, be it acute nicotine abstinence or
satiety, would yield the most robust differences compared with nonsmokers when assessing
neurobiological markers of nicotine dependence.
Methods: Smokers (N = 15) and sociodemographically matched nonsmokers (N = 15) were
scanned twice using a repeated-measures design. Smokers were scanned after a 24-hour nicotine
abstinence and immediately after smoking their usual brand cigarette. The neuroimaging battery
included a stop-signal task of response inhibition and pseudocontinuous arterial spin labeling to
measure cerebral blood flow (CBF). Whole-brain voxel-wise analyses of covariance were carried
out on stop success and stop fail Stop-Signal Task contrasts and CBF maps to assess differences
among nonsmokers, abstinent smokers, and satiated smokers. Cluster correction was performed
using AFNI’s 3dClustSim to achieve a significance of p < .05.
Results: Smokers exhibited higher brain activation in bilateral inferior frontal gyrus, a brain region
known to be involved in inhibitory control, during successful response inhibitions relative to nonsmokers.
This effect was significantly higher during nicotine abstinence relative to satiety. Smokers
also exhibited lower CBF in the bilateral inferior frontal gyrus than nonsmokers. These hypoperfusions
were not different between abstinence and satiety.
Conclusions: These findings converge on alterations in smokers in prefrontal circuits known to be
critical for inhibitory control. These effects are present, even when smokers are satiated, but the
neural activity required to achieve performance equal to controls is increased when smokers are
in acute abstinence.
Implications: Our multimodal neuroimaging study gives neurobiological insights into the cognitive
demands of maintaining abstinence and suggests targets for assessing the efficacy of therapeutic
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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).