Abstract: Objective: White matter microstructure alterations have recently been associated with adolescence depressive episodes, but it is unknown whether they predate depression. We investigated whether subthreshold-depression in adolescence is associated with white matter microstructure variations and whether they relate to depression outcome.
Method: Adolescents with subthreshold-depression (n=96) and healthy controls (n=336), drawn from a community-based cohort, were compared using diffusion tensor imaging and whole-brain tractbased spatial statistics (TBSS) at age 14 to assess white matter microstructure. They were followedup at age 16 to assess depression. Probabilistic tractography was used to reconstruct white matter streamlines from the TBSS analysis resulting regions, and along bundles implicated in emotion regulation, the uncinate fasciculus and the cingulum. We searched for mediating effects of white matter microstructure on the relationship between baseline subthreshold-depression and depression at follow-up, and then explored the specificity of the findings.
Results: Lower fractional anisotropy (FA) and higher radial diffusivity were found in the anterior corpus callosum in the adolescents with subthreshold-depression. Tractography analysis showed that they also had lower FA in the right cingulum streamlines, along with lower FA and higher mean diffusivity in tracts connecting the corpus callosum to the anterior cingulate cortex. The relation between baseline subthreshold-depression and follow-up depression was mediated by FA values in the latter tracts, and lower FA values in those tracts distinctively predicted higher individual risk for depression.
Conclusions: Early FA variations in tracts projecting from the corpus callosum to the anterior cingulate cortex might denote higher risk of transition to depression in adolescents.
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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).