Abstract: Background and Aims
Dysfunction in brain regions underlying impulse control, reward processing and executive function have been associated previously with adolescent alcohol misuse. However, identifying pre‐existing neurobiological risk factors, as distinct from changes arising from early alcohol‐use, is difficult. Here, we outline how neuroimaging data can identify the neural predictors of adolescent alcohol‐use initiation and misuse by using prospective longitudinal studies to follow initially alcohol‐naive individuals over time and by neuroimaging adolescents with inherited risk factors for alcohol misuse.
A comprehensive narrative of the literature regarding neuroimaging studies published between 2010 and 2016 focusing on predictors of adolescent alcohol use initiation and misuse.
Prospective, longitudinal neuroimaging studies have identified pre‐existing differences between adolescents who remained alcohol‐naive and those who transitioned subsequently to alcohol use. Both functional and structural grey matter differences were observed in temporal and frontal regions, including reduced brain activity in the superior frontal gyrus and temporal lobe, and thinner temporal cortices of future alcohol users. Interactions between brain function and genetic predispositions have been identified, including significant association found between the Ras protein‐specific guanine nucleotide releasing factor 2 (RASGRF2) gene and reward‐related striatal functioning.
Neuroimaging predictors of alcohol use have shown modest utility to date. Future research should use out‐of‐sample performance as a quantitative measure of a predictor's utility. Neuroimaging data should be combined across multiple modalities, including structural information such as volumetrics and cortical thickness, in conjunction with white‐matter tractography. A number of relevant neurocognitive systems should be assayed; particularly, inhibitory control, reward processing and executive functioning. Combining a rich magnetic resonance imaging data set could permit the generation of neuroimaging risk scores, which could potentially yield targeted interventions.
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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).