# Neural, computational and behavioral characterization of dynamic social behavior in borderline and avoidant personality disorder

> **NIH NIH R01** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2022 · $615,315

## Abstract

Project Summary
Severe impairments in interpersonal functioning are hallmarks of personality disorders. Borderline personality
disorder (BPD), for example, is characterized by inability to maintain relationships, inflexibility in dealing with
changes in relationships, and heightened needs to control and manipulate others. Avoidant personality disorder
(AvPD), in contrast, is primarily marked by social withdrawal and avoidance, as well as reduced sense of control
in social relationships. While social neuroscience has been growing rapidly in recent years, the complexity of
human social interactions has not been well quantified with computational models, particularly as applied to
personality disorders. The overarching aim of this project is to utilize novel computational models and paradigms,
combined with 7-Tesla imaging and brain connectivity measures, to capture the neural computations underlying
proactive and dynamic social behaviors in BPD, AvPD, and healthy controls (HC; n=60 per group). Specifically,
we will focus on two novel and complex social behaviors that mimic real-life social interaction: 1) social
controllability, the ability to exert control over one’s social environment and, 2) social navigation, the process of
navigating dynamically changing social relationships. In Aim 1, we will examine the computational and neural
mechanisms of social controllability in BPD and AvPD using a social exchange paradigm in which participants
either could or could not influence their partners’ monetary offers in a novel computational framework. We will
capture key parameters such as estimated controllability (), sensitivity to norm violation (), and beliefs about
control. In Aim 2, we will identify neurocomputational indices of dynamic social relationships in BPD and AvPD,
using a novel social interaction game in which participants interact and develop relationships with virtual
characters. We will devise novel measures that track the trajectories of social relationships and geometrically
quantify the overall structure of individuals’ two-dimensional social space framed by power and affiliation. In Aim
3, we will use state-of-the-art machine learning approaches and the neurocomputational parameters derived
from Aims 1 & 2 to predict each participant’s diagnosis/group label (BPD, AvPD, or HC) and patients’ symptom
severity. Upon successful completion of these aims, this project will provide important neurocomputational
characterization for proactive social behaviors and how they might break down in BPD and AvPD, potentially
breaking new grounds and filling critical knowledge gaps for social neuroscience and computational psychiatry
research. The resulting paradigms, models, and findings will be critical for a wide range of personality and other
psychiatric disorders. Thus, the proposed neurocomputational framework could parameterize social interactions,
providing novel quantitative measures of social pathology, treatment change, and the nature of pa...

## Key facts

- **NIH application ID:** 10400100
- **Project number:** 5R01MH123069-02
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** Xiaosi Gu
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $615,315
- **Award type:** 5
- **Project period:** 2021-05-01 → 2026-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10400100

## Citation

> US National Institutes of Health, RePORTER application 10400100, Neural, computational and behavioral characterization of dynamic social behavior in borderline and avoidant personality disorder (5R01MH123069-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10400100. Licensed CC0.

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