This study investigated the neural correlates of psychotic-like experiences in youths during tasks involving inhibitory control, reward anticipation, and emotion processing. A secondary aim was to test whether these neurofunctional correlates of risk were predictive of psychotic symptoms 2 years later.
Functional imaging responses to three paradigms—the stop-signal, monetary incentive delay, and faces tasks—were collected in youths at age 14, as part of the IMAGEN study. At baseline, youths from London and Dublin sites were assessed on psychotic-like experiences, and those reporting significant experiences were compared with matched control subjects. Significant brain activity differences between the groups were used to predict, with cross-validation, the presence of psychotic symptoms in the context of mood fluctuation at age 16, assessed in the full sample. These prediction analyses were conducted with the London-Dublin subsample (N=246) and the full sample (N=1,196).
Relative to control subjects, youths reporting psychotic-like experiences showed increased hippocampus/amygdala activity during processing of neutral faces and reduced dorsolateral prefrontal activity during failed inhibition. The most prominent regional difference for classifying 16-year-olds with mood fluctuation and psychotic symptoms relative to the control groups (those with mood fluctuations but no psychotic symptoms and those with no mood symptoms) was hyperactivation of the hippocampus/amygdala, when controlling for baseline psychotic-like experiences and cannabis use.
The results stress the importance of the limbic network’s increased response to neutral facial stimuli as a marker of the extended psychosis phenotype. These findings might help to guide early intervention strategies for at-risk youths.
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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).