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: 10400100
• 负责人: Xiaosi Gu
• 金额: $ 61.53万
• 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/10400100
• 关键词: Assertiveness; Behavioral; Belief; Borderline Personality Disorder; Brain; Categories; Characteristics; Clinical; Complex; Computer Models; Corpus striatum structure; Decision Making; Diagnosis; Diagnostic; Dimensions; Failure; Fright; Functional Magnetic Resonance Imaging; Future; Goals; Grain; Hippocampus (Brain); Human; Image; Impairment; Individual; Knowledge; Label; Lead; Life; Machine Learning; Measures; Mental disorders; Methods; Modeling; Nature; Neurosciences Research; Participant; Pathology; Patient Self-Report; Patients; Personality; Personality Disorders; Persons; Process; Property; Psychiatry; 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; base; clinically relevant; computer framework; geometric structure; indexing; neural circuit; neurobehavioral; neuroimaging; neuromechanism; novel; relating to nervous system; 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 严重的人际功能障碍是人格障碍的特征。边缘人格 例如，障碍(BPD)的特征是无法维持关系，在处理问题时缺乏灵活性 人际关系的变化，以及对控制和操纵他人的强烈需求。回避型人格障碍 相比之下，AvPD的主要特征是社交退缩和回避，以及控制感下降 在社会关系中。虽然社会神经科学近年来发展迅速，但其复杂性 人类社会互动还没有用计算模型很好地量化，特别是在应用于 人格障碍。该项目的总体目标是利用新的计算模型和范例， 结合7-特斯拉成像和大脑连接测量，以捕获潜在的神经计算 BPD、AvPD和健康对照组(HC；每组60人)的积极和动态的社会行为。具体来说， 我们将重点介绍两种模仿现实社会互动的新颖而复杂的社交行为：1)社交 可控性，对自己的社会环境施加控制的能力；2)社会导航，即 导航动态变化的社会关系。在目标1中，我们将研究计算和神经 在BPD和AVPD中使用社会交换范式的社会可控性机制 在一种新的计算框架中，他们要么可以，要么不能影响他们伴侣的货币报价。我们会 捕获关键参数，如估计的可控性()、对违规行为的敏感度()以及以下方面的信念 控制力。在目标2中，我们将确定BPD和AVPD动态社会关系的神经计算指标， 使用一种新颖的社交游戏，其中参与者与虚拟 人物。我们将设计新的测量方法，跟踪社会关系的轨迹并以几何方式 量化个人由权力和从属关系构成的二维社会空间的整体结构。在AIM 3，我们将使用最先进的机器学习方法和推导出的神经计算参数 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 严肃性。在成功完成这些目标后，该项目将提供重要的神经计算 主动性社会行为的特征以及它们可能如何在BPD和AVPD中崩溃，潜在地 为社会神经科学和计算精神病学开辟新天地，填补关键知识空白 研究。由此产生的范例、模型和发现将对广泛的个性和其他 精神障碍。因此，提出的神经计算框架可以将社会互动参数化， 为社会病理、治疗变化和患者性质提供新的量化指标- 心理治疗师的互动。