Abstract: Adolescence is a common time for initiation of alcohol use and alcohol use disorders. Importantly, the neuro‐anatomical foundation for later alcohol‐related problems may already manifest pre‐natally, particularly due to smoking and alcohol consumption during pregnancy. In this context, cortical gyrification is an interesting marker of neuronal development but has not been investigated as a risk factor for adolescent alcohol use. On magnetic resonance imaging scans of 595 14‐year‐old adolescents from the IMAGEN sample, we computed whole‐brain mean curvature indices to predict change in alcohol‐related problems over the following 2 years. Change of alcohol use‐related problems was significantly predicted from mean curvature in left orbitofrontal cortex (OFC). Less gyrification of OFC was associated with an increase in alcohol use‐related problems over the next 2 years. Moreover, lower gyrification in left OFC was related to pre‐natal alcohol exposure, whereas maternal smoking during pregnancy had no effect. Current alcohol use‐related problems of the biological mother had no effect on offsprings' OFC gyrification or drinking behaviour. The data support the idea that alcohol consumption during pregnancy mediates the development of neuro‐anatomical phenotypes, which in turn constitute a risk factor for increasing problems due to alcohol consumption in a vulnerable stage of life. Maternal smoking during pregnancy or current maternal alcohol/nicotine consumption had no significant effect. The OFC mediates behaviours known to be disturbed in addiction, namely impulse control and reward processing. The results stress the importance of pre‐natal alcohol exposure for later increases in alcohol use‐related problems, mediated by structural brain characteristics.
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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).