Neurocomputational mechanisms of impaired social decision-making in borderline personality

边缘人格社会决策受损的神经计算机制

• 批准号: 10307834
• 负责人: Michael Nelson Hallquist
• 金额: $ 43.73万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-06-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10307834
• 关键词: Neurocomputational; mechanisms; impaired; social; decision

Borderline Personality Disorder (BPD) is a debilitating condition characterized by affective instability, interpersonal dysfunction, and impulsive and self-harming behaviors. Individuals with BPD often make disadvantageous decisions in response to negative interpersonal events, yet little is known about decision processes that precipitate maladaptive behaviors. The past two decades of neuroscience research has provided overwhelming evidence that decision-making can be understood in terms of Pavlovian and goal- directed computational systems that are implemented in specific cortical-striatal-limbic circuits. Supported by our preliminary data, we propose that in BPD, the cingulo-opercular network’s role in goal-directed learning is vulnerable to disruptions by social-emotional cues that exert Pavlovian influences on decision-making. Although BPD has historically been diagnosed in adults, symptoms often emerge in adolescence and their severity may peak in early adulthood. The maturation of the cingulo-opercular network from adolescence to early adulthood underlies developmental improvements in the integration of motivationally salient cues with goal-directed behavior. We will test the hypothesis that in BPD, both approach- and avoidance-related Pavlovian computations dominate the cingulo-opercular network via the phylogenetically old pathway from the central nucleus of the amygdala to the nucleus accumbens core, which underpins emotion-driven Pavlovian responses. The proposed case-control study will characterize abnormalities in Pavlovian and goal-directed decision-making in 49 young adults with BPD symptoms compared to 49 matched individuals with social anxiety disorder and 49 healthy controls. Studying these processes in early adulthood is essential because BPD symptoms change rapidly during this period, which may reflect neurodevelopmental maturation of emotion- and decision-related circuits. At the behavioral level, we will characterize participants using a decision battery and corresponding hierarchical Bayesian reinforcement learning (RL) models that span social and nonsocial contexts (Aim 1). We will link decision signals, particularly the effects of social cues on goal-directed learning, with their neurocomputational correlates using Bayesian RL models and model-based fMRI analyses (Aim 2). Finally, to characterize separable circuits involved in maladaptive Pavlovian computations in BPD, we will conduct a high-resolution resting-state fMRI study of the integration of the cingulo-opercular network with specific limbic and striatal regions (Aim 3). Altogether, our computational psychiatry approach builds on the unique strengths of our investigative team in BPD and neurodevelopment (Hallquist), Bayesian methodology (Oravecz), and decision neuroscience (Hallquist, Dombrovski). This work aligns well with the NIMH Strategic Plan for Research objectives to describe the neural circuits underlying mental illness (Strategy 1.1) and to identify biomarkers and behavioral indicators that predict change in illness (Strategy 2.2).

边缘性人格障碍（BPD）是一种令人衰弱的疾病，其特征是情感不稳定， 人际功能障碍以及冲动和自我伤害行为。患有BPD的人经常使 响应负面人际关系事件的不利决定，但对决策知之甚少 处理宝贵的适应不良行为。过去二十年的神经科学研究已经 提供了压倒性的证据，表明可以从帕夫洛维亚人和目标来理解决策 - 在特定的皮质 - 纹状体 - 边缘电路中实现的定向计算系统。支持 我们的初步数据，我们建议在BPD中，Cingulo-Obercular网络在目标指导学习中的作用是 很容易受到社会情感线索的破坏，这些线索对决策产生了影响。 尽管BPD历史上已被诊断出在成年人中，但青少年及其症状经常出现 严重程度可能在成年初达到顶峰。从青少年到 成年初期的发展是发展动机突出线索与 目标指导的行为。我们将检验以下假设：在BPD中，无论是接近和避免与避免有关的假设 帕夫洛维亚的计算通过从系统发育的旧途径中占主导地位 杏仁核的中央核与伏隔核的核心，该核心是情绪驱动的Pavlovian的基础 回答。拟议的病例对照研究将表征帕夫洛维亚和目标定向的异常 49名患有BPD症状的年轻人的决策，相比之下，有49名与社会的匹配的人 焦虑症和49个健康对照。在成年初期研究这些过程至关重要，因为 在此期间，BPD症状迅速变化，这可能反映 情绪和决策有关的圈子。在行为层面，我们将使用决定来表征参与者 电池和相应的分层贝叶斯强化学习（RL）模型，这些模型涵盖了社交和 非社交环境（目标1）。我们将链接决策信号，特别是社会线索对目标定向的影响 学习，使用贝叶斯RL模型和基于模型的fMRI分析，其神经计算与他们的神经计算相关 （目标2）。最后，为了表征BPD中涉及不良适应性帕夫洛维亚计算的单独电路，我们 将对Cingulo-obercular网络的整合进行高分辨率休息状态fMRI研究 特定的边缘和纹状体区域（AIM 3）。总的来说，我们的计算精神病学方法建立在 BPD和神经发育中的调查团队的独特优势（Hallquist），贝叶斯方法论 （Oravecz）和决策神经科学（Hallquist，Dombrovski）。这项工作与NIMH的战略相吻合 研究对象的计划来描述精神疾病的神经回路（策略1.1）和 确定预测疾病变化的生物标志物和行为指标（策略2.2）。