Using Hybrid Modeling to Determine the Latent Structure of Psychopathology

Van Dam et al. ( ) took an innovative, data-driven (as opposed to top-down, diagnostically driven) approach to elucidate psychiatric phenotypes and related differences in functional brain connectivity in a large sample of adults. This work attempted to clarify one of the major problems in both clinical practice and research—namely, high comorbidity of DSM diagnoses. The authors’ intention was to move away from DSM-5 labels, and their results using a hierarchical clustering approach showed distinctions between individuals with internalizing and externalizing symptoms, similar to the approach advocated by the Hierarchical Taxonomy of Psychopathology consortium (R. Kotov, M.D., et al., The Hierarchical Taxonomy of Psychopathology [HiTOP]: A dimensional alternative to traditional nosologies [unpublished data], 2016). The hierarchical clustering approach used by Van Dam et al. ( ) illuminated groups and subgroups (referred to as “clusters”) comprising typically and atypically functioning individuals that cut across DSM-5 disorders, as well as several functional connectivity differences between the two largest groups. It is important to recognize that the authors could have chosen other data-driven statistical approaches. The hierarchical clustering approach that was used relied on the assumption of an underlying “hierarchy,” and also assumed that individuals fit into specific clusters based on their phenotype profile. Essentially, the approach still categorized individuals using phenotype profile instead of DSM diagnosis. In using such an approach, the authors lost the ability to determine whether a categorical, dimensional, or true hybrid structure best fit the data. We describe several benefits of alternative data-driven modeling strategies below.