Abstract: Through unbiased transcriptomics and multiple molecular tools, transient downregulation of the Orthodenticle homeobox 2 (OTX2) gene was recently causatively associated with the development of depressive-like behaviors in a mouse model of early life stress. The analyses presented in this manuscript test the translational applicability of these findings by examining peripheral markers of methylation of OTX2 and OTX2-regulated genes in relation to measures of depression and resting-state functional connectivity data collected as part of a larger study examining risk and resilience in maltreated children. The sample included 157 children between the ages of 8 and 15 years (χ = 11.4, SD = 1.9). DNA specimens were derived from saliva samples and processed using the Illumina 450 K beadchip. A subset of children (N = 47) with DNA specimens also had resting-state functional MRI data. After controlling for demographic factors, cell heterogeneity, and three principal components, maltreatment history and methylation in OTX2 significantly predicted depression in the children. In terms of the imaging data, increased OTX2 methylation was found to be associated with increased functional connectivity between the right vmPFC and bilateral regions of the medial frontal cortex and the cingulate, including the subcallosal gyrus, frontal pole, and paracingulate gyrus—key structures implicated in depression. Mouse models of early stress hold significant promise in helping to unravel the mechanisms by which child adversity confers risk for psychopathology, with data presented in this manuscript supporting a potential role for OTX2 and OTX2-related (e.g., WNT1, PAX6) genes in the pathophysiology of stress-related depressive disorders in children.
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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).