Mechanistic arbitration between candidate dimensions of psychopathology

As an alternative to the Diagnostic and Statistical Manual of Mental Disorders, transdiagnostic approaches that identify latent dimensions of psychopathology through factor analysis have gained prominence in recent years. A key critique of these approaches, however, is that they are performed at the level of symptoms only. This begs the question: are these dimensions truly more valid predictors of external outcomes than existing alternatives? Are there other ways, that are more data-driven, which can allow us to refine our definitions of clinical phenotypes? We tested this idea empirically, conducting a large-scale meta-scientific comparison of thousands of competing factor solutions that allowed us to determine if the latent structure underlying the covariation of psychiatric symptoms has robust and specific cognitive correlates. In nine independent datasets, comprising N=7565 individuals including patients about to start mental health treatment, healthy individuals, paid and unpaid participants, a broad set of age ranges and cognitive task variants that measured model-based planning and metacognition, we found that factors with the best fit to cognition were those derived from a first-order factor analysis on the maximal number of theoretically informed self-report symptoms available. These factors (‘Compulsivity and Intrusive Thought’ and ‘Anxious-depression’) performed better than thousands of engineered alternatives and performed twice as well as traditional questionnaire total scores. Crucially, this unsupervised approached based on symptom correlation only performed on-par with a partial least squares analysis, a supervised approach to deriving factors based on cognition. These results provide evidence that unsupervised factor analysis of psychiatric symptoms is a viable method for rethinking how we define mental health and illness, affording clear opportunities for enhancing our understanding of specific underlying mechanistic processes.