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Objective: Although depression and anxiety often have distinct etiologies, they frequently co-occur in adolescence. Recent initiatives have underscored the importance of developing new ways of classifying mental illness based on underlying neural dimensions that cut across traditional diagnostic boundaries. Accordingly, the aim of the study was to clarify reward-related neural circuitry that may characterize depressed-anxious youth.Method: The Boston Adolescent Neuroimaging of Depression and Anxiety Human Connectome Project tested group differences regarding subcortical volume and nucleus accumbens activation during an incentive processing task among 14- to 17-year-old adolescents presenting with a primary depressive and/or anxiety disorder (n = 129) or no lifetime history of mental disorders (n = 64). In addition, multimodal modeling examined predictors of depression and anxiety symptom change over a 6-month follow-up period.Results: Our findings highlighted considerable convergence. Relative to healthy youth, depressed-anxious adolescents exhibited reduced nucleus accumbens volume and activation following reward receipt. These findings remained when removing all medicated participants (∼59% of depressed-anxious youth). Subgroup analyses comparing anxious-only, depressed-anxious, and healthy youth also were largely consistent. Multimodal modeling showed that only structural alterations predicted depressive symptoms over time.Conclusion: Multimodal findings highlight alterations within nucleus accumbens structure and function that characterize depressed-anxious adolescents. In the current hypothesis-driven analyses, however, only reduced nucleus accumbens volume predicted depressive symptoms over time. An important next step will be to clarify why structural alterations have an impact on reward-related processes and associated symptoms. [ABSTRACT FROM AUTHOR]

Recently, the U-Net-like convolutional neural network architecture composed of encoder, decoder, and symmetric skip-connection has shown impressive performance and has quickly become the de-facto standard in road segmentation tasks. In this letter, a novel Wide-Range Attention Unit (WRAU) is introduced and incorporated into a densely-connected U-Net architecture which we term the Wide-Range Attention U-Net (WRAU-Net). New architecture achieves a wide-range channel-wise attention mechanism by two major contributions. Firstly, we add a partially dense-connections branch to the U-Net architecture to aggregate informative feature map channels with multiple spatial resolutions from different levels of encoder. Secondly, before encoding back into the network, the WRAU is employed at the cluster node of partially dense-connections to learn to weight aggregated feature map channels by a three-layer convolutional network. We evaluate our WRAU-Net on the Massachusetts road segmentation benchmark. The cross-validation results show that the employment of the proposed attention mechanism to classic U-Net architecture gives a tangible boost to test accuracy when compared to U-Net itself and another two state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

The article provides information about the neuroscience research of professor Eve Marder of Biology Modulation of Neural Networks at Brandeis University in Waltham, Massachusetts. It mentions about the people who have contributed to her interests in investigating the mechanisms of neurotransmitting and neural network in lobsters and other crustaceans. In her quest for finding the answers of her question regarding why the brain requires several neurotransmitter, Marder has also observed her fellow neuroscientists examining the nicotinic acetylcholine receptor.