Neurocomputational mechanisms of impaired social decision-making in borderline personality

交界性人格障碍(BPD)是一种以情感不稳定为特征的衰弱状态， 人际功能障碍，以及冲动和自我伤害行为。患有BPD的人通常会 应对负面人际事件的不利决策，但对决策知之甚少 催生不适应行为的过程。在过去二十年的神经科学研究中， 提供了压倒性的证据，表明决策可以用巴甫洛夫和目标来理解- 在特定的皮质-纹状体-边缘回路中实现的定向计算系统。支持： 我们的初步数据表明，在BPD中，扣带回网络在目标定向学习中的作用是 容易受到对决策施加巴甫洛夫式影响的社会情绪线索的干扰。 尽管BPD在历史上是在成年人身上诊断出来的，但症状通常出现在青春期和他们的 严重程度可能在成年初期达到顶峰。从青春期到青春期扣带盖网络的成熟 成年期早期是发展进步的基础，在动机显著的线索与 目标导向型行为。我们将检验这样的假设：在BPD中，接近和回避都与 巴甫洛夫计算通过系统发育古老的途径主导了扣带-盖网络 杏仁核的中央核到伏隔核的核心，它支撑着情绪驱动的巴甫洛夫 回应。拟议的病例对照研究将描述巴甫洛夫畸形和目标导向的异常。 49名患有BPD症状的年轻人与49名匹配的社交障碍患者的决策能力比较 焦虑症和49名健康对照。在成年初期研究这些过程是至关重要的，因为 在此期间，BPD症状变化迅速，这可能反映了BPD的神经发育成熟。 情绪和决策相关的回路。在行为级别，我们将使用决策来描述参与者 电池和相应的层次化贝叶斯强化学习(RL)模型，跨越社交和 非社会背景(目标1)。我们将把决策信号联系起来，特别是社交线索对目标导向的影响 使用贝叶斯RL模型和基于模型的功能磁共振分析的学习及其神经计算相关性 (目标2)。最后，为了刻画BPD中非自适应巴甫洛夫计算所涉及的可分电路，我们 我将进行一项高分辨率静息状态fMRI研究，研究扣带盖网络与 特定的边缘和纹状体区域(目标3)。总之，我们的计算精神病学方法建立在 我们的研究团队在BPD和神经发育(Hallquist)、贝叶斯方法方面的独特优势 和决策神经科学(Hallquist，Dombrovski)。这项工作与NIMH战略非常一致 计划研究目标，以描述精神疾病潜在的神经回路(策略1.1)和 确定预测疾病变化的生物标志物和行为指标(战略2.2)。

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).