A Multimethod Approach of Social Disconnection in Schizophrenia: Leveraging Digital Phenotyping, Social Network Analyses, and Neuroimaging

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 improve the social disability experienced by Veterans with serious mental illness. The training outlined in this CDA application will lay the groundwork for the applicant to develop an independent research program within the VA focused on applying

innovative methodologies to understand and improve social disability in SCZ. The VISN 22 Mental Illness Research, Education, and Clinical Center (MIRECC) at the VA Greater Los Angeles Healthcare System (GLA), and the University of California, Los Angeles (UCLA) provides an excellent environment and infrastructure to

complete the proposed study. The primary mentor will be Michael F. Green, PhD, the Director of the VISN 22 MIRECC Treatment Unit and the VA RR&D Center on Enhancing Community Integration for Homeless Veterans (THRIVe), and an established researcher in the field of SCZ and social cognition. The applicant will

also receive specialized training from an expert in fMRI methodology (Robert Welsh, PhD) and a renowned scholar in SNA methodology (Mark Handcock, PhD).