Modeling learning under volatility in Borderline Personality Disorder

项目摘要/摘要 这是一份以患者为导向的职业发展建议，旨在为应聘者提供高级 培训、受保护的研究时间和有指导的研究经验。这项研究的目的是关注边缘 人格障碍(BPD)，这是一种严重的、使人衰弱的精神疾病，影响2%-6%的 并增加自杀风险。目前的治疗方法疗效有限，而且没有FDA- BPD的批准药物。候选人的长期职业目标是改善BPD的待遇和 使用计算精神病学更好地预测预后的其他具有显著社会症状的疾病 和最佳治疗相匹配。 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 导师制和三个有监督的研究目标。培训计划是为应聘者设计的 神经成像、计算模型和学习理论方面的专业知识。候选人还将在她的基础上再接再厉 对临界性人格障碍患者的临床研究和治疗有较强的基础 数据分析的统计方法。导师团队在临床精神病学方面拥有丰富的专业知识。 研究、功能磁共振研究设计和分析，以及学习的计算模型。他们将提供 应聘者拥有朝着她的研究目标前进所需的资源和监督。 这些研究充分利用了计算精神病学的最新进展。通过三个假设驱动 研究目标、受试者行为和大脑激活将在互动社交学习任务中进行测试。 这些目标测试在BPD中的波动性(当环境不可预测时)下的学习 BPD+创伤后应激障碍、创伤后应激障碍和创伤暴露的健康对照。目的1测试前扣带回皮质在脑内的作用 在这种情况下，杏仁核和前扣带回之间的信号波动和相互作用。AIM 2测试 BPD受试者与对照受试者的学习模式以识别疾病特定的表型。AIM 3测试如何 学习的神经和行为标记物与社会功能有关。下一步将是细化一个 BPD学习的计算模型，以预测预后，预测个人的最佳治疗匹配， 并测试新的生物处理靶点。 提高我们对人际关系症状的机械性理解将改进我们的临床治疗和 大大减少数百万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.