Neural Markers of Treatment Mechanisms and Prediction of Treatment Outcomes in Social Anxiety

社交焦虑治疗机制的神经标志物和治疗结果预测

• 批准号: 10816883
• 负责人: DANIEL G DILLON
• 金额: $ 9.11万
• 依托单位: BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10816883
• 关键词: Adult; Boston; Brain; Chronic; Data; Doctor of Philosophy; Electroencephalography; Goals; Group Therapy; Hospitals; Individual; Machine Learning; Magnetic Resonance Imaging; Massachusetts; Mental disorders; Multimodal Imaging; Neurobiology; Neurosciences; Patient-Focused Outcomes; Patients; Prediction of Response to Therapy; Psychiatry; Psychopathology; Recommendation; Research; Research Personnel; Sampling; Selective Serotonin Reuptake Inhibitor; Social Anxiety Disorder; Technology; Treatment outcome; Universities; behavior measurement; brain circuitry; clinical practice; comparison control; cost effective; disease classification; effective therapy; evidence base; imaging approach; improved; multimodal neuroimaging; neural; neural circuit; neuroimaging; neuromechanism; optimal treatments; partial response; personalized medicine; precision medicine; predicting response; recruit; skills; social anxiety; systematic review; treatment guidelines; treatment response

PROJECT SUMMARY/ABSTRACT Social anxiety disorder (SAD) is one of the most common mental disorders. For unknown reasons, many patients do not respond to existing treatments. Treatment guidelines and systematic reviews often recommend CBT as the first line treatment, followed by an SSRI adjunctively for patients who show no or only partial response to CBT. A major advance toward personalized medicine would be to identify reliable treatment predictors, and then to clarify the neuromechanism of treatment change. One promising approach toward improving patient outcomes is to examine the key neurocircuitry of SAD that may also serve as neuromarkers predicting treatment response. We have gathered convincing pilot data identifying neuromarkers that predict response to CBT in adults with SAD. The next translational step, and our primary aim, is to apply state of the art computational psychiatry approaches to strengthen the evidence base for these neuromarkers, in line with moving psychiatry toward precision medicine. This aim will be efficiently achieved by collecting state-of-the-art, multimodal neuroimaging data to better elucidate the key neurocircuitry of SAD (compared to controls) in a well powered sample, while also identifying differential treatment-related changes in neural circuitry (target engagement). The ultimate goal is to effectively treat all patients, not only a few and without knowing why, and to illuminate the brain circuitry associated with effective treatments to inform psychopathology, nosology, and therapy of common mental disorders. For these reasons, we propose recruiting a large number of patients with SAD (n = 190) and healthy controls (n = 100) to examine differences in relevant neurocircuitries that will also be used as neuromarkers of treatment response. Patients with SAD will first receive CBT group therapy. Those who show no or only partial response will then receive individual and tailored CBT plus SSRI. In addition to MRI, we will examine EEG and behavioral measures to determine if there are more cost effective correlates of neuropredictors that could be easily implemented in clinical practice. We have assembled a team of skilled researchers with complementary expertise at the Massachusetts Institute of Technology (MIT; John D. E. Gabrieli, Ph.D.), Boston University (BU; Stefan G. Hofmann, Ph.D.), and McLean Hospital (Daniel Dillon, Ph.D.), as well as outstanding consultants in neuroimaging analysis (Northeastern University: Susan Whitfield- Gabrieli, Ph.D.) and machine learning applications in psychiatry (McLean Hospital: Christian Webb, Ph.D.).

项目摘要/摘要 社交焦虑症（SAD）是最常见的精神障碍之一。由于未知原因，许多 患者对现有治疗没有反应。治疗指南和系统评价通常建议 CBT作为第一行治疗，然后是SSRI，辅助出现在没有或仅部分的患者 对CBT的响应。个性化医学的主要进步将是确定可靠的治疗 预测因素，然后阐明治疗变化的神经力学。一种有前途的方法 改善患者的结果是检查SAD的关键神经记录，也可能充当神经标志物 预测治疗反应。我们收集了令人信服的飞行员数据，以识别预测的神经标志物 SAD成年人对CBT的反应。下一个翻译步骤以及我们的主要目的是应用 艺术计算精神病学方法来加强这些神经标志物的证据基础 将精神病学转向精确医学。通过收集最先进的目标，将有效实现此目标 多模式的神经影像学数据，以更好地阐明井中SAD的关键神经记录（与对照组相比） 动力样品，同时还鉴定了神经回路的差异治疗相关的变化（目标 订婚）。最终目标是有效治疗所有患者，不仅少数患者，也不知道为什么，并且 阐明与有效治疗相关的脑电路，以告知心理病理学，牙齿学和 常见精神障碍的治疗。由于这些原因，我们建议招募大量患者 SAD（n = 190）和健康对照（n = 100），以检查相关神经记录的差异 用作治疗反应的神经标志物。 SAD患者将首先接受CBT组治疗。那些 然后，谁表现出任何或仅部分响应将获得个人和量身定制的CBT加上SSRI。此外 MRI，我们将检查脑电图和行为措施，以确定是否存在更具成本效益的相关性 可以在临床实践中容易实施的神经读物。我们组建了一支熟练的团队 马萨诸塞州理工学院的补充专业知识的研究人员（麻省理工学院； John D. E. 波士顿大学（BU； Stefan G. Hofmann，Ph.D.）和麦克莱恩医院（Daniel Dillon 博士学位），以及神经影像分析的杰出顾问（东北大学：Susan Whitfield- Gabrieli博士）和精神病学的机器学习应用（麦克莱恩医院：克里斯蒂安·韦伯，博士）。