Interpretation Bias as a Mechanism of Treatment Response in OCD

项目摘要 尽管暴露和反应预防(ERP)被公认为强迫症的一线治疗方法- 强迫症(OCD)，接受这种干预的患者中有40%-50%无法康复。小说 需要加强以更有效地针对维持强迫症症状的机制 人口。一种可能的机制是解释偏差，将模糊刺激误解为 具有威胁性，助长了强迫症的发展和维持。在没有干预的情况下，更多 高效有效地针对解释偏差，很可能仍然很难提高治疗率 应对和减少复发。这项建议的目的是测试可行性、可接受性、遵从性、 以解释偏差为目标的干预的目标参与和临床结果，最终目标 有助于开发新的、可扩展的、由技术驱动的企业资源规划增强系统。改进 在口译方面，偏见与OC症状的减轻有关。因此，一种可获得的干预 直接靶向解释偏差可能是在事件相关电位中改善临床结果的理想补充。 认知偏差对口译偏差的修正(CBM-I)是一种计算机化的干预方法，已被证明是可靠的 参与口译的效果。强迫症的CBM-I研究大多是在模拟实验中进行的 样本，以及对临床样本的研究一直有限。这项提议将解决以下迫切需要 开发利用CBM-I增强ERP的干预措施，主要目的是：1)测试CBM-I是否诱导 强迫症患者解释偏差的变化，并确定这些变化是否与临床结果相关 跨多模式评估，以及2)利用机器学习的进步来开发个性化 预测哪些强迫症患者最适合进行CBM-I。我们假设一个多元模型 结合治疗前的临床、行为和人口学特征将预测患者的具体情况 对煤层气-I做出反应的概率。这些目标与候选人的培训目标相对应，并提供关键的新培训 并在以下领域提供指导：1)采用实验疗法的有效性试验方法 方法(共同小学导师考特尼·比尔德博士；共同导师萨宾·威廉博士)；2)生态瞬间 评估(共同小学导师克里斯蒂安·韦伯博士和比尔德博士；合作者贾斯汀·贝克博士)；3)机器 学习技巧(韦伯博士和合作者任博宇博士)；以及4)职业发展(共同导师克里博士 雷斯勒)。该K23奖将支持以患者为中心的创新研究计划，并提供 具备成为一名独立调查员所需技能的候选人，专注于推动 了解、预测和治疗无反应和复发，以优化暴露的结果 难治性OC相关疾病患者的治疗。

Project Summary Although exposure and response prevention (ERP) is well-established as the first line of treatment for obsessive- compulsive disorder (OCD), 40-50% of patients who receive this intervention do not recover. Novel augmentations are needed to more efficiently target the mechanisms that maintain OCD symptoms in this population. One candidate mechanism is interpretation bias, the misinterpretation of ambiguous stimuli as threatening, contributing to the development and maintenance of OCD. In the absence of interventions that more efficiently and effectively target interpretation bias, it will likely remain difficult to increase rates of treatment response and decrease relapse. The objective of this proposal is to test the feasibility, acceptability, adherence, target engagement, and clinical outcomes of an intervention targeting interpretation bias, with the ultimate goal of contributing to the development of novel, scalable, technology-driven augmentations to ERP. Improvements in interpretation bias have been associated with OC symptom reduction. Therefore, an accessible intervention which directly targets interpretation bias may be an ideal augmentation to improve clinical outcomes during ERP. Cognitive Bias Modification for Interpretation Bias (CBM-I), a computerized intervention, has shown reliable effects for engagement of interpretation. Studies of CBM-I in OCD have been largely conducted in analogue samples, and studies with clinical samples have been limited. This proposal will address the critical need of developing interventions to augment ERP by utilizing CBM-I, with primary aims to: 1) test whether CBM-I induces changes in interpretation bias in OCD and to determine if these changes are associated with clinical outcomes across multi-modal assessments, and 2) leverage advances in machine learning to develop personalized predictions of which individuals with OCD are best-suited for CBM-I. We hypothesize that a multivariate model incorporating pre-treatment clinical, behavioral and demographic characteristics will predict patient-specific probability of responding to CBM-I. These aims map onto the candidate’s training goals, with critical new training and mentorship provided in the areas of: 1) effectiveness trials methodology with an experimental therapeutics approach (Co-Primary Mentor Dr. Courtney Beard; Co-Mentor Dr. Sabine Wilhelm); 2) ecological momentary assessment (Co-Primary Mentors Dr. Christian Webb and Dr. Beard; Collaborator Dr. Justin Baker); 3) machine learning techniques (Dr. Webb and Collaborator Dr. Boyu Ren); and 4) career development (Co-Mentor Dr. Kerry Ressler). This K23 Award will support an innovative program of patient-oriented research and provide the candidate with the skills necessary to become an independent investigator focused on advancing the understanding, prediction, and treatment of non-response and relapse, to optimize outcomes of exposure therapy for refractory patients with OC-related disorders.