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

项目摘要/摘要 社交焦虑症(SAD)是最常见的精神障碍之一。由于未知的原因，许多人 患者对现有的治疗方法没有反应。治疗指南和系统评价通常建议 CBT作为一线治疗，然后对没有或仅显示部分症状的患者开始辅助SSRI 对初始CBT的响应。向个性化医疗迈出的一大进步和一步将是识别可靠的 治疗预测因素，并阐明治疗变化的神经机制。一个很有希望的方法是 改善患者预后是检查SAD的关键神经回路，该回路也可能作为神经标记物 以预测治疗反应。我们收集了令人信服的飞行员数据，指向这些神经标记物 预测CBT对SAD的反应。翻译的下一步，我们的主要目标是应用最先进的技术 计算精神病学方法，以进一步建立这些神经标记物的证据，符合 将精神病学推向精准医学。这一目标将通过收集多模式数据来有效实现 为了更好地阐明SAD的关键神经回路与在井中进行最新神经成像的对照 有动力的样本，以及与区别治疗相关的神经电路变化(靶点接触)。这个 最终目标是有效地治疗所有患者，而不仅仅是少数人，而且不知道为什么，并阐明 与有效治疗相关的大脑回路，以告知精神病理学、病因学和治疗 常见的精神障碍。基于这些原因，我们建议招募大量SAD(N)患者 =190)和健康对照组(n=50)，以检查相关神经回路的差异，这些回路也将被用作 治疗反应的神经标志物。SAD患者将首先接受CBT小组治疗。那些出现的人 没有或只有部分答复将收到单独的和定制的CBT加上SSRI。除了核磁共振 措施，我们将检查脑电和行为指标，以确定是否可以更便宜 易于在临床实践中实施的神经预测指标的相关性。我们已经组建了一支团队 麻省理工学院具有互补专业知识的熟练研究人员(MIT；John D. E.Gabrieli博士)、波士顿大学(波士顿大学；Stefan G.Hofmann博士)和麦克莱恩医院(Daniel Dillon， 博士)，以及神经成像分析方面的杰出顾问(东北大学：苏珊·惠特菲尔德- Gabrieli博士)和机器学习在精神病学中的应用(McLean医院：Christian Webb，Ph.D.)。

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, and then to start an SSRI adjunctively for patients who show no or only partial response to initial CBT. A major advance and step toward personalized medicine would be to identify reliable treatment predictors and 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 to predict treatment response. We have gathered convincing pilot data pointing to such neuromarkers to predict response to CBT for SAD. The next translational step and our primary aim is to apply state of the art computational psychiatry approaches to further establish the evidence of these neuromarkers, in line with moving psychiatry toward precision medicine. This aim will be efficiently achieved by collecting multimodal data to better elucidate key neurocircuitry in SAD compared to controls with state-of-the art neuroimaging in a well powered sample, as well as 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 in order to inform psychopathology, nosology, and therapy of common mental disorders. For these reasons, we propose to recruit a large number of patients with SAD (n = 190) and healthy controls (n = 50) 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 measures, we will examine EEG and behavioral measures to determine whether there may be less expensive correlates of neuropredictors that can 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.).