The Research Domain Criteria framework: transitioning from dimensional systems to integrating neuroscience and psychopathology

Latent classes of maladaptive personality traits exhibit differences in social processing

OBJECTIVE

Social processing (SP) deficits manifest across numerous mental disorders. However, this research has been plagued by heterogeneity and a piecemeal approach whereby skills are examined in isolation rather than as part of an integrated cognitive system. Here, we combined two dimensional frameworks of psychopathology to address these limitations.

METHOD

We utilized the Alternative Model for Personality Disorders (AMPD) to distill trait-related heterogeneity within a community sample (n = 200), and the Research Domain Criteria (RDoC) 'Systems for Social Processes' to comprehensively assess SP. We first applied latent class analyses (LCA) to derive AMPD-based groups and subsequently contrasted the performance of these groups on a SP test battery that we developed to align with the RDoC SP constructs.

RESULTS

Our LCA yielded four distinct subgroups. The recognizable trait profiles and psychopathological symptoms of these classes suggested they were clinically meaningful. The subgroups differed in their SP profiles: one displayed deficits regarding the self, a second displayed deficits in understanding others, a third displayed more severe deficits including affiliation problems, whilst the fourth showed normal performance.

CONCLUSIONS

Our results support the link between clusters of maladaptive personality traits and distinctive profiles of SP deficits, which may inform research on disorders involving SP dysfunctions.

A data-driven method for identifying shorter symptom criteria sets: the case for DSM-5 alcohol use disorder

BACKGROUND

Although the DSM is a widely used diagnostic guide, lengthy criteria sets can be problematic and provide the primary motivation to identify short-forms. Using the 11 diagnostic criteria provided by the DSM-5 for alcohol use disorder (AUD), the present study develops a data-driven method to systematically identify subsets and associated cut-offs that yield diagnoses as similar as possible to use all 11 criteria.

METHOD

Relying on data from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC-III), our methodology identifies diagnostic short-forms for AUD by: (1) maximizing the association between the sum scores of all 11 criteria with newly constructed subscales from subsets of criteria; (2) optimizing the similarity of AUD prevalence between the current DSM-5 rule and newly constructed diagnostic short-forms; (3) maximizing sensitivity and specificity of the short-forms against the current DSM-5 rule; and (4) minimizing differences in the accuracy of the short-form across chosen covariates. Replication is shown using NESARC-Wave 2.

RESULTS

More than 11 000 diagnostic short-forms for DSM-5 AUD can be created and our method narrows down the optimal choices to eight. Results found that 'Neglecting major roles' and 'Activities given up' could be dropped with practically no change in who is diagnosed (specificity = 100%, sensitivity ⩾ 99.6%) or the severity of those diagnosed (κ = 0.97).

CONCLUSIONS

With a continuous improvement model adopted by the APA for DSM revisions, we offer a data-driven tool (a SAS Macro) that identifies diagnostic short-forms in a systematic and reproducible way to help advance potential improvements in future DSM revisions.