It is widely agreed that emotion regulation plays an important role in many psychological disorders. We make the case that emotion regulation is in fact a key transdiagnostic factor, using the Research Domain Criteria (RDoC) as an organizing framework. In particular, we first consider how transdiagnostic and RDoC approaches have extended categorical views. Next, we examine links among emotion generation, emotion regulation, and psychopathology, with particular attention to key emotion regulation stages including identification, strategy selection, implementation, and monitoring. We then propose that emotion regulation be viewed as a sixth domain in the RDoC matrix, and provide a brief overview of how the literature has used the RDoC units of analyses to study emotion regulation. Finally, we highlight opportunities for future research and make recommendations for assessing and treating psychopathology.

World Health Organization.

We identified public eating and drinking as bridge symptoms between EDs and SAD. Future research is needed to test if interventions focused on public eating and drinking might decrease symptoms of both EDs and SAD. Researchers can use this study (code provided) as an exemplar for how to use network analysis, as well as to use network analysis to conceptualize ED comorbidity and compare network structure and density across samples.

The validity of both the Social Interaction Anxiety Scale and Brief Fear of Negative Evaluation scale has been well-supported, yet the scales have a small number of reverse-scored items that may detract from the validity of their total scores. The current study investigates two characteristics of participants that may be associated with compromised validity of these items: higher age and lower levels of education. In community and clinical samples, the validity of each scale's reverse-scored items was moderated by age, years of education, or both. The straightforward items did not show this pattern. To encourage the use of the straightforward items of these scales, we provide normative data from the same samples as well as two large student samples. We contend that although response bias can be a substantial problem, the reverse-scored questions of these scales do not solve that problem and instead decrease overall validity.

Objective Network analysis allows us to identify the most interconnected (i.e., central) symptoms, and multiple authors have suggested that these symptoms might be important treatment targets. This is because change in central symptoms (relative to others) should have greater impact on change in all other symptoms. It has been argued that networks derived from cross-sectional data may help identify such important symptoms. We tested this hypothesis in social anxiety disorder. Method We first estimated a state-of-the-art regularized partial correlation network based on participants with social anxiety disorder (N = 910) to determine which symptoms were more central. Next, we tested whether change in these central symptoms were indeed more related to overall symptom change in a separate dataset of participants with social anxiety disorder who underwent a variety of treatments (N = 244). We also tested whether relatively superficial item properties (infrequency of endorsement and variance of items) might account for any effects shown for central symptoms. Results Centrality indices successfully predicted how strongly changes in items correlated with change in the remainder of the items. Findings were limited to the measure used in the network and did not generalize to three other measures related to social anxiety severity. In contrast, infrequency of endorsement showed associations across all measures. Conclusions The transfer of recently published results from cross-sectional network analyses to treatment data is unlikely to be straightforward.