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

边缘型和回避型人格障碍动态社会行为的神经、计算和行为特征

基本信息

批准号：
10579939
负责人：
Xiaosi Gu
金额：
$ 58.29万
依托单位：
ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-05-01 至 2026-02-28
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10579939
关键词：
Assertiveness Behavioral Belief Borderline Personality Disorder Brain Characteristics Classification Clinical Complex Computer Models Corpus striatum structure Decision Making Diagnosis Diagnostic Dimensions Failure Fright Functional Magnetic Resonance Imaging Future Goals Grain Hippocampus Human Image Impairment Individual Knowledge Label Life Machine Learning Measures Mental disorders Methods Modeling Nature Neurosciences Research Participant Pathology Patient Self-Report Patients Personality Personality Disorders Persons Process Property Psychiatry Reaction Research Research Domain Criteria Resolution Severities Signal Transduction Social Behavior Social Change Social Controls Social Environment Social Interaction Social Planning Structure Subgroup Symptoms Withdrawal Work clinically relevant computer framework geometric structure indexing neural neural circuit neurobehavioral neuroimaging neuromechanism novel social social anxiety social deficits social learning social neuroscience social relationships social space supervised learning two-dimensional unsupervised learning virtual

项目摘要

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 patient- psychotherapist interactions.

项目总结