Abstract: To analyze the involvement of different brain regions in behavioral inhibition and impulsiveness, differences in activation were investigated in fMRI data from a response inhibition task, the stop‐signal task, in 1709 participants. First, areas activated more in stop‐success (SS) than stop‐failure (SF) included the lateral orbitofrontal cortex (OFC) extending into the inferior frontal gyrus (ventrolateral prefrontal cortex, BA 47/12), and the dorsolateral prefrontal cortex (DLPFC). Second, the anterior cingulate and anterior insula (AI) were activated more on failure trials, specifically in SF versus SS. The interaction between brain region and SS versus SF activations was significant (P = 5.6 * 10−8). The results provide new evidence from this “big data” investigation consistent with the hypotheses that the lateral OFC is involved in the stop‐related processing that inhibits the action; that the DLPFC is involved in attentional processes that influence task performance; and that the AI and anterior cingulate are involved in emotional processes when failure occurs. The investigation thus emphasizes the role of the human lateral OFC BA 47/12 in changing behavior, and inhibiting behavior when necessary. A very similar area in BA47/12 is involved in changing behavior when an expected reward is not obtained, and has been shown to have high functional connectivity in depression.
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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).