Abstract: The processing of reward and reinforcement learning seems to be important determinants of pain chronicity. However, reward processing is already altered early in life and if this is related to the development of pain symptoms later on is not known. The aim of this study was first to examine whether behavioural and brain-related indicators of reward processing at the age of 14 to 15 years are significant predictors of pain complaints 2 years later, at 16 to 17 years. Second, we investigated the contribution of genetic variations in the opioidergic system, which is linked to the processing of both, reward and pain, to this prediction. We used the monetary incentive delay task to assess reward processing, the Children's Somatization Inventory as measure of pain complaints and tested the effects of 2 single nucleotide polymorphisms (rs1799971/rs563649) of the human µ-opioid receptor gene. We found a significant prediction of pain complaints by responses in the dorsal striatum during reward feedback, independent of genetic predisposition. The relationship of pain complaints and activation in the periaqueductal gray and ventral striatum depended on the T-allele of rs563649. Carriers of this allele also showed more pain complaints than CC-allele carriers. Therefore, brain responses to reward outcomes and higher sensitivity to pain might be related already early in life and may thus set the course for pain complaints later in life, partly depending on a specific opioidergic genetic predisposition.
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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).