Subthreshold depression and regional brain volumes in young community adolescents
Journal of the American Academy of Child & Adolescent Psychiatry, 54, 832-840, 2015
Neuroimaging findings have been reported in regions of the brain associated with emotion in both adults and adolescents with depression, but few studies have investigated whether such brain alterations can be detected in adolescents with subthreshold depression, a condition at risk for major depressive disorder. In this study, we searched for differences in brain structure at age 14 years in adolescents with subthreshold depression and their relation to depression at age 16 years.
High-resolution structural magnetic resonance imaging was used to assess adolescents with self-reported subthreshold depression (n = 119) and healthy control adolescents (n = 461), all recruited from a community-based sample. Regional gray and white matter volumes were compared across groups using whole-brain voxel-based morphometry. The relationship between subthreshold depression at baseline and depression outcome was explored using causal mediation analyses to search for mediating effects of regional brain volumes.
Adolescents with subthreshold depression had smaller gray matter volume in the ventromedial prefrontal and rostral anterior cingulate cortices and caudates, and smaller white matter volumes in the anterior limb of internal capsules, left forceps minor, and right cingulum. In girls, but not in boys, the relation between subthreshold depression at baseline and high depression score at follow-up was mediated by medial–prefrontal gray matter volume.
Subthreshold depression in early adolescence might be associated with smaller gray and white matter volumes in regions of the frontal–striatal–limbic affective circuit, and the occurrence of depression in girls with subthreshold depression might be influenced by medial–prefrontal gray matter volume. However, these findings should be interpreted with caution because of the limitations of the clinical assessment methods.
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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).