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

> **NIH VA IK2** · VA GREATER LOS ANGELES HEALTHCARE SYSTEM · 2024 · —

## Abstract

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...

## Key facts

- **NIH application ID:** 10921837
- **Project number:** 1IK2CX002729-01A1
- **Recipient organization:** VA GREATER LOS ANGELES HEALTHCARE SYSTEM
- **Principal Investigator:** Samuel Joseph Abplanalp
- **Activity code:** IK2 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2024
- **Award amount:** —
- **Award type:** 1
- **Project period:** 2024-09-01 → 2029-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10921837

## Citation

> US National Institutes of Health, RePORTER application 10921837, A Multimethod Approach of Social Disconnection in Schizophrenia: Leveraging Digital Phenotyping, Social Network Analyses, and Neuroimaging (1IK2CX002729-01A1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10921837. Licensed CC0.

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