A translational approach for modeling the cognitive substrates of depression : identifying biological predictors and neurocognitive interventions for biased cognition
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Major Depressive Disorder (MDD) is a pervasive, debilitating condition that affects roughly 16% of Americans in their lifetime. However, treatments for MDD are considered adequate in only 21% of cases. Although biological and cognitive models have significantly added to our understanding of MDD, relatively little work has been undertaken to bridge the two. In particular, the biological factors that contribute to cognitive risk factors for MDD remain largely unknown. This dissertation used a translational approach to identify the specific neurobiological underpinnings of cognitive vulnerability for MDD, with the goal of identifying neurocognitive interventions to help improve treatment outcomes. These results were built on an empirical foundation beginning with the neural substrates of Aaron Beck's cognitive model of depression, and expanded on that literature by identifying genetic factors that predict the onset of maladaptive cognitive biases. Prospective research linking specific cognitive biases to naturalistic change in dysphoric symptoms was incorporated to establish the link between biased information processing and MDD maintenance. Finally, neuroenhancement techniques, such as low-level light therapy (LLLT), were used to augment the neural substrates of attention bias modification (ABM), a form of neurocognitive intervention, in order to optimize the treatment's clinical efficacy. This line of research helps to establish cognitive biases as a causal endophenotype for MDD, explores novel augmentative treatment strategies, and advances our understanding about biomarkers that predict improved clinical outcome.