Developing a mechanistic neurobiological model of exposure therapy response based on fear extinction theory

基于恐惧消退理论开发暴露疗法反应的机械神经生物学模型

• 批准号: 9904774
• 负责人: Tali Manber Ball
• 金额: $ 18.64万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-04-01 至 2023-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9904774
• 关键词: Address; Adult; Affect; American; Amygdaloid structure; Animals; Anterior; Anxiety; Behavioral Sciences; Biological Markers; Brain; Clinical; Clinical Trials; Clinical Trials Design; Cognitive; Data; Development; Distress; Dorsal; Exposure to; Extinction (Psychology); Fright; Functional Magnetic Resonance Imaging; Functional disorder; Funding; Future; Galvanic Skin Response; Goals; Human; Image; K-Series Research Career Programs; Knowledge; Laboratories; Life; Link; Loudness; Machine Learning; Measures; Mediating; Mentored Patient-Oriented Research Career Development Award; Mentors; Mentorship; Modeling; Neurobiology; Neurosciences; Noise; Organ; Outcome; Outcome Measure; Participant; Patient Self-Report; Patient-Focused Outcomes; Patients; Phase; Physiological; Physiology; Postdoctoral Fellow; Prediction of Response to Therapy; Prefrontal Cortex; Protocols documentation; Psychiatry; Psychologist; Psychology; Randomized; Research; Research Training; Risk; Role; Sampling; Scientific Advances and Accomplishments; Severities; Social Anxiety Disorder; Symptoms; Techniques; Training; Translating; Translational Research; Treatment outcome; Universities; Waiting Lists; Work; anxiety symptoms; anxious; anxious individuals; base; career; career development; cingulate cortex; clinical decision-making; clinically relevant; disability; improved; improved outcome; individualized medicine; innovation; learning extinction; neuroimaging; novel; outcome prediction; patient oriented; patient response; pre-clinical; predictive marker; predictive modeling; programs; recruit; response; skills; social; social anxiety; statistics; theories; treatment of anxiety disorders

Project Summary I am applying for a Mentored Patient-Oriented Career Development Award (K23) to support my development into an independently funded translational clinical psychologist. My long-term career goal is to conduct a program of research that translates neurobiological models of anxiety and its treatment into guidance for real- life clinical decision-making. Currently, although exposure therapy is the best available treatment for anxiety disorders, 25% of patients do not respond to an adequate course of exposure therapy and we do not know why. A critical long-term goal is to improve exposure therapy response by tailoring therapy based on the neurobiological profile of each patient. Because the mechanism of exposure therapy is thought to be extinction learning, the proposed research aims to address this goal by directly linking exposure therapy response to extinction learning in a sample of adults with social anxiety disorder. Specifically, I aim to identify neurobiological features of extinction learning that predict successful recall, are associated with clinical symptoms, and predict therapy response. The project will use a paradigm that I developed and piloted to assess physiological and neuroimaging measures of extinction learning. The expected outcomes are an innovative mechanistic neurobiological model that can identify patients at risk of non-response, as well as specific, testable hypotheses for how to improve outcomes for these patients. In order for this project, and my broader career goals, to succeed, it is imperative that I have expertise related to exposure therapy, predictive modeling, analysis of self-report, physiological, and fMRI data, and traditional and biomarker-guided clinical trial designs. I already have expertise in the theory and implementation of exposure therapy, as well as the analysis of self-report and fMRI data. This K23 award would allow me to receive advanced training in (1) machine learning techniques for building and evaluating clinically relevant predictive models, (2) specific skills for analysis and interpretation of physiological data, and (3) traditional and biomarker-guided clinical trials. I have assembled an inter-disciplinary team of mentors at Stanford University that are ideally suited to guide my research and training. Dr. Leanne Williams (primary mentor, Department of Psychiatry) will provide expertise on using neurobiology to make treatment outcome predictions, and provide overall career development mentorship throughout the project. Dr. James Gross (co-mentor, Department of Psychology) will provide expertise on physiological data and its integration with self-report and fMRI data. Dr. Tze Lai (co-mentor, Department of Statistics) will provide expertise on predictive models and biomarker-guided clinical trial designs. The training and associated research will take place in the Psychiatry and Behavioral Sciences Department at Stanford University, a world renowned hub of neuroscience and psychiatry research, and at Stanford's Center for Cognitive and Neurobiological Imaging, a state-of-the-art neuroimaging facility.

项目摘要 我正在申请一个指导病人为导向的职业发展奖（K23），以支持我的发展 转变成一个独立资助的临床心理学家我的长期职业目标是 一项研究计划，将焦虑的神经生物学模型及其治疗转化为真实的指导， 生命临床决策。目前，尽管暴露疗法是治疗焦虑症的最佳方法， 25%的患者对适当的暴露治疗过程没有反应，我们不知道 为什么.一个关键的长期目标是通过基于以下因素定制治疗来改善暴露治疗反应： 每个患者的神经生物学特征。因为暴露疗法的机制被认为是灭绝 学习，拟议的研究旨在通过直接将暴露治疗反应与 社交焦虑障碍成年人样本中的消退学习。具体来说，我的目标是确定 预测成功回忆的消退学习的神经生物学特征，与临床 症状，并预测治疗反应。该项目将使用我开发并试用的范例， 评估消退学习的生理和神经影像学指标。预期结果是 创新的机械神经生物学模型，可以识别无反应风险的患者，以及 具体的，可检验的假设，如何改善这些患者的结果。为了这个项目，我的 更广泛的职业目标，要成功，我必须拥有与暴露疗法相关的专业知识，预测 建模，自我报告，生理和fMRI数据的分析，以及传统和生物标志物指导的临床 试验设计。我已经有了暴露疗法的理论和实施方面的专业知识， 自我报告和fMRI数据的分析。这个K23奖将使我能够接受高级培训（1） 用于构建和评估临床相关预测模型的机器学习技术，（2）特定技能 用于分析和解释生理数据，和（3）传统的和生物标志物引导的临床试验。我 我在斯坦福大学组建了一个跨学科的导师团队，他们非常适合指导我 研究和培训。Leanne威廉姆斯博士（精神病学系主要导师）将提供专业知识 使用神经生物学来预测治疗结果，并提供整体职业发展 整个项目的指导。詹姆斯格罗斯（共同导师，心理学系）将提供 生理数据及其与自我报告和功能磁共振成像数据整合的专业知识。黎子杰博士（共同导师， 统计系）将提供预测模型和生物标志物指导的临床试验方面的专业知识 的设计.培训和相关研究将在精神病学和行为科学领域进行 斯坦福大学是世界著名的神经科学和精神病学研究中心， 斯坦福大学的认知和神经生物学成像中心，一个最先进的神经成像设施。