Neurocomputational mechanisms of impaired social decision-making in borderline personality

边缘型人格障碍（BPD）是一种以情感不稳定为特征的衰弱状态，

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).