Neurobehavioral Mechanisms of Emotion Regulation in Depression Across the Adult Lifespan

Depressive Disorder, Major

The aim of this study is to test a model of demographic (age, sex), clinical, cognitive, and neurocircuitry predictors of emotion regulation ability and long-term depressive symptoms.

Emotion regulation capacities are crucial for sustaining mental health in the face of cumulative stressors over one's lifetime. Although it is well documented that some emotion regulation abilities are preserved or even improved in healthy aging, little is known about why regulatory deficits persist in older adults who suffer from depression. Treatments for major depressive disorder (MDD) focus on remediating affective dysregulation processes that confer risks for disability, poor quality of life, and morbidity into late life. Theoretical perspectives on emotional aging propose myriad lifespan changes that potentially impact regulatory capacities, including structural and functional integrity of dorsal attentional and ventral affective processing pathways, cognitive status, and use of specific regulatory strategies, among others. However, there is a dearth of empirical evidence to indicate which combination of these factors critically interacts with depressive symptoms to impact emotional dysregulation in older adults, when these factors become important across the course of the adult lifespan, which strategies they apply to, and whether they can predict future depression status. Thus, the goal of this specific application is to test a comprehensive model of age-related changes to brain circuitry, neurocognitive performance, and social support as predictors of emotion regulation abilities and depressive symptoms in individuals with and without MDD. Reappraisal and distraction are the emotion regulation strategies of primary interest. Models will be evaluated using primarily a series of linear (multiple) regression models focusing on between-subject effects/comparisons (age, MDD status, etc.) and the emotion regulation outcomes separately for reappraisal and distraction processes. As an extension of these models we will perform Structural Equation Modeling (SEM) type modeling to summarize the liability dimensions underlying the specific domains of depression \[BDI scores measuring depression severity; lifetime duration of depressive episode(s)\], and neural measures of dorsal attention network functioning \[gPPI connectivity between dlPFC and amygdala; task-based activation during distraction in dACC, dlPFC, and inferior parietal lobe; DTI FA measure in SLF II\] and affective network functioning \[gPPI connectivity between vlPFC and amygdala; task-based activation during reappraisal in vmPFC, vlPFC, and amygdala; DTI FA measure of UF\]. The SEM will be especially useful in predicting the future depression that will be assessed at one-year follow up, where the predicted (best linear unbiased predictors-BLUPS) values of lower-dimensional latent traits, along with emotion regulation outcomes, can be used as predictors for future depression. Moreover, hierarchical modeling structures can be imposed on latent traits conditionally on a shared latent trait describing associations among several latent traits thus further reducing underlying dimensionality and simplifying computations. This single trait can be thought as a cumulative effect of all latent traits and can be used a single index of uncertainty in predicting future depression symptom severity.

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