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

• 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
• 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

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.

项目摘要 人格障碍的标志是人格障碍的严重障碍。边缘性格 例如，疾病（BPD）的特征是无法维持关系，在处理方面的僵硬性 人际关系的变化以及高度需要控制和操纵他人的需求。回避人格障碍 （AVPD）相比 在社会关系中。尽管近年来，社会神经科学一直在迅速发展 人类的社交互动尚未通过计算模型进行量化，尤其是应用于 人格障碍。该项目的总体目的是利用新颖的计算模型和范式， 结合7-Tesla成像和大脑连接度量，以捕获基础的神经计算 BPD，AVPD和健康对照中的积极和动态社会行为（HC；每组n = 60）。具体来说， 我们将专注于模仿现实生活中社会互动的两种新颖而复杂的社会行为：1）社会 可控性，对社会环境的控制能力以及2）社会导航的过程 导航动态改变社会关系。在AIM 1中，我们将检查计算和中性 BPD和AVPD中社会可控性的机制，使用社会交流范式参与者 在一个新颖的计算框架中，要么可以或无法影响其合作伙伴的货币报价。我们将 捕获关键参数，例如估计的可控性（），对规范违规的敏感性（），并相信 控制。在AIM 2中，我们将确定BPD和AVPD中动态社会关系的神经计算指标， 使用一个新颖的社交互动游戏，参与者与虚拟的互动和发展关系 人物。我们将制定新颖的措施，以追踪社会关系的轨迹和几何轨迹 量化个人二维社会空间的总体结构，由权力和会员构成。目标 3，我们将使用最先进的机器学习方法和派生的神经计算参数 从目标1和2预测每个参与者的诊断/组标签（BPD，AVPD或HC）和患者的症状 严重程度。成功完成这些目标后，该项目将提供重要的神经计算 积极主动的社会行为以及它们如何在BPD和AVPD中分解的表征 打破新的理由并填补社会神经科学和计算精神病学的关键知识空白 研究。由此产生的范例，模型和发现对于广泛的个性和其他人物至关重要 精神疾病。这就是提出的神经计算框架可以参数化社交互动， 提供社会病理学，治疗变化和患者性质的新颖定量措施 - 心理治疗师的互动。