Modeling psychopathology in high-dimensional vector space using the high-dimensional symptom space (HDSS) model can operationalize precision psychiatry in US adolescents

Abstract

Symptoms of psychopathology vary across people, limiting inferences about origins and treatments of disorders for any one person. The high-dimensional symptom space (HDSS) model offers a novel framework for understanding psychopathology by representing symptoms as vectors within a multidimensional space. Unlike traditional categorical and dimensional models, HDSS uses geometric distances to empirically characterize a person’s unique experience of symptoms, with the option to integrate social and cultural factors for more precise, personalized treatments. Using data from the adolescent brain and cognitive development (ABCD) study, we demonstrate that HDSS preserves individual specificity, effectively captures dynamic trajectories of psychological distress, and accommodates clinical heterogeneity. Results indicate that HDSS distances correspond to symptom severity and capture nuanced patterns of psychological distress over time, offering a comprehensive and individualized understanding of psychopathology. This model allows for a person-centered understanding of psychopathology, highlighting unique symptom patterns and their evolution over time. HDSS represents a significant advancement in personalized psychological care, providing a data-driven framework for understanding psychopathology symptoms, and implementing effective interventions.