Facets of Emotion Regulation and Emotional Disorder Symptom Dimensions: Differential Associations and Incremental Validity in a Large Clinical Sample.

A growing literature suggests robust associations between dimensions of emotion regulation and emotional disorder psychopathology. However, limited research has investigated associations of emotion regulation dimensions across several emotional disorders (transdiagnostic associations), or the incremental validity of emotion regulation versus the higher-order construct of neuroticism. The current study used exploratory structural equation modeling and a large clinical sample (N = 1,138) to: (a) develop a multidimensional emotion regulation measurement model, (b) evaluate the differential associations between latent emotion regulation dimensions and five latent emotional disorder symptom dimensions (social anxiety, depression, agoraphobia/panic, obsessions/compulsions, generalized worry), and (c) determine the incremental contribution of emotion regulation in predicting symptom dimensions beyond neuroticism. The best-fitting measurement model of emotion regulation included four dimensions: Problematic Responses, Poor Recognition/Clarity, Negative Thinking, and Emotional Inhibition/Suppression. Although many zero-order associations between the four latent emotion regulation dimensions and five latent symptom dimensions were significant, few associations remained significant in a structural regression model that included neuroticism. Specifically, Negative Thinking and Problematic Responses incrementally predicted depression symptoms, while Emotional Inhibition/Suppression predicted both social anxiety and depression symptoms. Associations between neuroticism and the emotional disorder dimensions were similar regardless of whether the emotion regulation dimensions were held constant. These results suggest that self-reported emotion regulation dimensions are associated with the severity and expression of a range of emotional disorder symptoms, but that some emotion regulation dimensions have limited incremental validity after accounting for general emotional reactivity. Studies of emotion regulation should assess neuroticism as a key covariate.

Developing algorithms to predict adult onset internalizing disorders: An ensemble learning approach.

A growing literature is utilizing machine learning methods to develop psychopathology risk algorithms that can be used to inform preventive intervention. However, efforts to develop algorithms for internalizing disorder onset have been limited. The goal of this study was to utilize prospective survey data and ensemble machine learning to develop algorithms predicting adult onset internalizing disorders. The data were from Waves 1-2 of the National Epidemiological Survey on Alcohol and Related Conditions (n = 34,653). Outcomes were incident occurrence of DSM-IV generalized anxiety, panic, social phobia, depression, and mania between Waves 1-2. In total, 213 risk factors (features) were operationalized based on their presence/occurrence at the time of or before Wave 1. For each of the five internalizing disorder outcomes, super learning was used to generate a composite algorithm from several linear and non-linear classifiers (e.g., random forests, k-nearest neighbors). AUCs achieved by the cross-validated super learner ensembles were in the range of 0.76 (depression) to 0.83 (mania), and were higher than AUCs achieved by the individual algorithms. Individuals in the top 10% of super learner predicted risk accounted for 37.97% (depression) to 53.39% (social anxiety) of all incident cases. Thus, the algorithms achieved acceptable-to-excellent prediction accuracy with a high concentration of incident cases observed among individuals predicted to be highest risk. In parallel with the development of effective preventive interventions, further validation, expansion, and dissemination of algorithms predicting internalizing disorder onset/trajectory could be of great value.