Humans are inherently social and form meaningful relationships with family members and friends. However, developing these relationships is difficult for Veterans with schizophrenia (SCZ), resulting in poor social functioning and social disability. Two components of social disability are particularly devastating: objective social disconnection (i.e., the number of social connections) and loneliness (i.e., the subjective discomfort of feeling alone). Social disconnection and loneliness contribute to numerous detrimental outcomes, including early mortality. Current evidence-based treatments are not sufficiently effective at improving social disconnection and loneliness. Understanding how these constructs operate within naturalistic environments is vital to generating novel treatments. However, social disability in SCZ has traditionally been assessed using clinician-rated interviews, which have limited specificity in understanding the complexities of real-world behavior. One goal of this proposal is to evaluate the relationship between social disconnection and loneliness with digital phenotyping via smartphones. Digital phenotyping provides an ecologically valid assessment that can elucidate the nature of social disability in real-time. Furthermore, it is crucial to elucidate the mechanisms of social disconnection and loneliness that could further inform treatments. Although poor performance-based social cognition is associated with neural activity in distinct brain regions, the extent to which neural activity observed during social cognition paradigms relates to real-time social experiences in SCZ is unknown. Thus, the second goal of this proposal is to examine associations between functional magnetic resonance imaging (fMRI) during social cognitive tasks and digital phenotyping measures. Combining these methodologies can inform us about the neurophysiological mechanisms of social disconnection and loneliness. In addition to mechanisms at the neurophysiological level, mechanisms at the social level could also contribute to social disability. The third goal of this proposal is to use Egocentric social network analysis (SNA) to examine objective metrics of social networks. These metrics will provide information on how the structure and composition of social networks impact real-time social processes. This Career Development Award (CDA-2) aims to use an innovative, multimethod approach to examine the nature and mechanisms of social disconnection and loneliness—two critical components of social disability. The knowledge gained from this study could inform cutting-edge interventions for Veterans with SCZ that improve social disability at multiple levels of analysis. This CDA will provide the applicant, Samuel J. Abplanalp, PhD, with training in the areas of (1) social cognition and social neuroscience of SCZ; (2) fMRI data processing and analysis; and (3) SNA methodology. The applicant’s career goal is to become a VA-based data scientist, working to impr...