Stress reactivity moderates the association between early experiences of unpredictability and emotional problems in adolescents.

Researchers have documented that exposure to different kinds of psychosocial stressors can lead to emotional difficulties and, further, that heightened reactivity to stress can moderate these associations. Recently, investigators have distinguished among threat, deprivation, and unpredictability as different dimensions of early life stress (ELS). It is not clear, however, whether reactivity in specific stress response systems functions as a diathesis to lead to emotional difficulties following exposure to these dimensions of ELS.

Exposure to diesel-related particulate matter, cortisol stress responsivity, and depressive symptoms in adolescents.

Exposure to air pollution is associated with higher risk for psychopathology; however, the mechanisms underlying this association are not clear. Dysfunction of the hypothalamic-pituitary-adrenal (HPA) axis in response to stress has been implicated in depression. Here, we estimated annual exposure to particulate matter (PM) from diesel emissions in 170 9- to 15-year-old adolescents (56 % female) using their residential addresses and data from nearby monitoring sites.

Predicting Task Activation Maps from Resting-State Functional Connectivity using Deep Learning.

Recent work has shown that deep learning is a powerful tool for predicting brain activation patterns evoked through various tasks using resting state features. We replicate and improve upon this recent work to introduce two models, BrainSERF and BrainSurfGCN, that perform at least as well as the state-of-the-art while greatly reducing memory and computational footprints. Our performance analysis observed that low predictability was associated with a possible lack of task engagement derived from behavioral performance.