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.