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

项目摘要 人际功能的严重损害是人格障碍的标志。边缘型人格 例如，BPD的特征是无法维持人际关系， 关系的变化，以及控制和操纵他人的强烈需求。回避型人格障碍 相反，AvPD主要表现为社交退缩和回避，以及控制感降低 在社会关系中。虽然社会神经科学近年来发展迅速，但 人类社会的相互作用还没有很好地量化与计算模型，特别是应用于 人格障碍这个项目的首要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n=60）的主动和动态社会行为。具体地说， 我们将集中讨论两种新颖而复杂的模仿现实社会互动的社会行为：1）社会性 可控性，对一个人的社会环境施加控制的能力，2）社会导航， 驾驭动态变化的社会关系在目标1中，我们将检查计算和神经 BPD和AvPD中的社会可控性机制，使用社会交换范式，参与者 在一个新的计算框架中，他们可以或不可以影响他们的伴侣的货币报价。我们将 捕获关键参数，如估计的可控性（RISK），对规范违反的敏感性（RISK），以及对 控制在目标2中，我们将确定BPD和AvPD中动态社会关系的神经计算指标， 使用一种新颖的社交互动游戏，在该游戏中，参与者与虚拟人互动并发展关系， 字符.我们将设计新颖的措施，跟踪社会关系的轨迹， 量化个体由权力和从属关系构成的二维社会空间的整体结构。在Aim中 3，我们将使用最先进的机器学习方法和神经计算参数导出 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 严重性。在成功完成这些目标后，该项目将提供重要的神经计算 表征积极主动的社会行为，以及它们如何在BPD和AvPD中分解， 为社会神经科学和计算精神病学开辟新的领域，填补关键的知识空白 research.由此产生的范式，模型和发现将是至关重要的广泛的个性和其他 精神疾病因此，提出的神经计算框架可以参数化社会互动， 提供了新的定量措施的社会病理学，治疗的变化，和性质的病人， 心理治疗师的互动

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.