Modeling learning under volatility in Borderline Personality Disorder

项目总结/摘要 这是一个以病人为导向的职业发展建议，旨在为候选人提供先进的 培训，保护研究时间，指导研究经验。本研究的目的是关注边缘 人格障碍（BPD），这是一种严重的精神疾病，影响2-6%的人， 增加自杀风险。目前的治疗方法疗效有限，FDA也没有- 批准的治疗BPD的药物候选人的长期职业目标是改善BPD的治疗， 通过使用计算精神病学更好地预测预后， 最佳治疗匹配 为了继续朝着这个目标前进，候选人提出了一个详细的培训计划，包括专家培训。 指导和三个监督的研究目标。培训计划旨在让候选人获得 在神经成像，计算建模和学习理论的专业知识。候选人还将在她的基础上 在临床研究和治疗边缘型人格障碍方面有着坚实的基础， 数据分析的统计方法。导师团队在临床精神病学方面拥有广泛的专业知识 研究，fMRI研究设计和分析，以及学习的计算建模。他们将提供 候选人的资源和监督需要推进她的研究目标。 这些研究利用了计算精神病学的最新进展。通过三个假设驱动 研究目的，受试者行为和大脑活动将在互动社会学习任务中进行测试。 这些目标测试了BPD中波动性（当环境不可预测时）下的学习， BPD+PTSD、PTSD和创伤暴露的健康对照。目的1测试前扣带皮层的作用 信号波动性，以及杏仁核和前扣带回之间的相互作用。Aim 2测试 BPD与对照受试者的学习模式，以确定疾病特异性表型。目标3测试如何 学习的神经和行为标记与社会功能有关。下一步将是完善一个 BPD学习的计算模型，以预测预后，预测个体的最佳治疗匹配， 并测试新的生物治疗目标。 提高我们对人际关系症状的机械理解将改善我们的临床治疗， 大大减轻数百万BPD患者及其家人的痛苦。

Project Summary / Abstract This is a patient-oriented career development proposal designed to provide the candidate with advanced training, protected research time, and mentored research experience. The research aims focus on Borderline Personality Disorder (BPD), which is a serious and debilitating mental illness that affects 2-6% of the population and increases risk of suicide. Current treatments have limited efficacy and there are no FDA- approved medications for BPD. The candidate's long-term career goal is to improve the treatment of BPD and other disorders with significant social symptoms by using computational psychiatry to better predict prognosis and best treatment match. To continue her progress toward this goal, the candidate proposes a detailed plan for training, including expert mentorship and three supervised research aims. The training plan is designed for the candidate to gain expertise in neuroimaging, computational modelling, and learning theory. The candidate will also build on her strong foundation in clinical research and treatment of people with Borderline Personality Disorder and in statistical approaches to data analysis. The mentorship team has extensive expertise in clinical psychiatry research, fMRI study design and analysis, and computational modelling of learning. They will provide the candidate with the resources and supervision needed to advance toward her research goals. These studies leverage recent advances in computational psychiatry. Through three hypothesis-driven research aims, subject behavior and brain activation will be tested during an interactive social learning task. These aims test learning under volatility (when the environment is unpredictable) in BPD compared to BPD+PTSD, PTSD, and trauma-exposed healthy controls. Aim 1 tests the role of anterior cingulate cortex for signaling volatility, and interaction between amygdala and anterior cingulate in this setting. Aim 2 tests learning patterns in BPD versus comparator subjects to identify illness-specific phenotypes. Aim 3 tests how neural and behavioral markers of learning relate to social functioning. Next steps will be to refine a computational model of learning in BPD to predict prognosis, predict best treatment match for an individual, and test novel biological treatment targets. Improving our mechanistic understanding of interpersonal symptoms will improve our clinical treatments and significantly reduce suffering for millions of people with BPD and their families.