Identifying social computational phenotypes in mental health - PROJECT SUMMARY The ability to learn about other people and make social decisions is critical for successfully navigating our complex social environment and the consequences of failure can be severe (e.g., loss of employment, damage to relationships, etc.). Impairment in the social processes that support this ability is a key component of dysfunction across many mental health disorders. However, there has been a lack of systematic, data-driven research on the relationship between socio-cognitive impairment and mental illness. Moreover, what research exists has been limited in the social processes it surveys, disorder-focused, and population-specific, in contrast to efforts to reconstrue mental health within a multidimensional set of transdiagnostic spectra (e.g., the NIMH research domain criteria; RDoC, and the Hierarchical Taxonomy of Psychopathology; HiTOP). Computational Psychiatry has had great success using computational approaches to decompose behavioral indicators of mental disorder into their component processes and identify computational phenotypes. However, this research has focused primarily on nonsocial learning and decision-making computations. A critical next step is the systematic data-driven exploration of how mental health spectra map onto social computational phenotypes. Using large-scale data-driven approaches in combination with behavioral, computational, psychological assessment, and neuroimaging (fMRI) methods in healthy adults (ages 18-35), we will systematically investigate the social-cognitive aspects of individual differences in mental health with the goal of identifying social computational phenotypes (SCPs) that correspond to distinct transdiagnostic mental health spectra. Across two aims, participants will complete a battery of learning and decision-making tasks assessing three levels of learning (nonsocial learning, trait learning (i.e., social with no Theory-of-Mind; ToM), and ToM learning), and three feedback types (informational, monetary, social), two dimensions of great relevance to mental health. We will also quantify individual variation along multiple transdiagnostic mental health spectra using the HiTOP. Employing a rigorous data-driven approach, we will first conduct a large-scale (N=1000) online exploratory study to identify mental health-related SCPs (Aim 1a). We then will conduct a confirmatory study (N=500) in which we target the three most robust SCPs identified in Aim 1a, assessing replicability and quantifying out-of-sample predictive power (Aim 1b). Finally, we will adapt our task design for model-based fMRI (N=55; Aim 2) to probe the neural correlates of the single-most most robust SCP identified in Aim 1b. The results of this work will provide a much-needed foundation for understanding the relationship between social learning and decision-making and mental health, as well as insights into the etiology of psychopathology, and novel targets for treatment. Importantly, this project will provide cutting-edge interdisciplinary training in behavioral, computational, and neuroimaging methods for undergraduates in the PI’s laboratory, fulfilling the goals of the R15 mechanism.
