Abstract
The Diagnostic and Statistical Manual of Mental Disorders (DSM) has been the standard for diagnosing psychiatric disorders in the United States. Yet, evidence has suggested that symptoms in psychiatric disorders are not restricted to the boundaries between DSM categories, implicating an underlying latent transdiagnostic structure of psychopathology. Here, we applied an importance-guided machine learning technique for model selection to item-level data from self-reported instruments contained within the Consortium for Neuropsychiatric Phenomics dataset. From 578 questionnaire items, we identified a set of phenotypic features which consisted of 85 items that were shared across diagnoses of schizophrenia (SCZ), bipolar disorder (BD), and attention deficit/hyperactivity disorder (ADHD). A transdiagnostic classifier trained on the shared phenotypic features reliably distinguished the patient group as a whole from healthy controls (classification AUC = 0.95) and only 10 items were needed to attain the performance level of AUC being 0.90. A sum score created from the items produced high separability between patients and healthy controls (Cohen’s d = 2.85), and it outperformed predefined sum scores and sub-scores within the instruments (Cohen’s d ranging between 0.13 and 1.21). The shared phenotypic features comprised both symptom domains (e.g. dysregulated mood, attention deficits, and impaired reward processing) and personality traits (e.g. neuroticism, impulsivity, and extraversion). Moreover, by comparing these features with those that were most predictive of a single patient category, we can describe the unique features for each patient group superimposed on the transdiagnostic feature structure. Overall, our results reveal a latent transdiagnostic phenotypic structure shared across SCZ, BD, and ADHD and present a new perspective to understand insights offered by self-report psychiatric instruments.