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

项目总结/摘要 社交焦虑障碍（SAD）是最常见的精神障碍之一。由于未知的原因，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作为一线治疗，对于没有或仅部分显示 回应CBT个性化医疗的一个重大进步是确定可靠的治疗方法 预测因子，然后阐明治疗变化的神经机制。一种有希望的方法， 改善患者预后的方法是检查SAD的关键神经回路， 预测治疗反应。我们已经收集了令人信服的飞行员数据，确定了预测 对SAD成年人的CBT反应。下一个转换步骤，也是我们的主要目标，是应用 艺术计算精神病学方法，以加强这些神经标志物的证据基础，符合 推动精神病学走向精准医疗这一目标将通过收集最先进的， 多模式神经成像数据，以更好地阐明SAD的关键神经回路（与对照组相比） 动力样本，同时还识别神经回路（目标）中的差异治疗相关变化 订婚）。最终目标是有效地治疗所有患者，而不仅仅是少数患者和不知道为什么， 阐明与有效治疗相关的大脑回路，以告知精神病理学，疾病分类学， 常见精神疾病的治疗。基于这些原因，我们建议招募大量的患者， SAD（n = 190）和健康对照（n = 100），以检查相关神经回路的差异， 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 没有或只有部分反应的患者将接受单独和定制的CBT加SSRI。除了 MRI，我们将检查脑电图和行为措施，以确定是否有更多的成本效益的相关性， 神经预测器，可以很容易地在临床实践中实施。我们已经组建了一个 马萨诸塞州理工学院（MIT; John D. E. Gabrieli博士），波士顿大学（BU; Stefan G. Hofmann博士），和姆克林医院（丹尼尔狄龙， Ph.D.），以及神经影像分析方面的杰出顾问（东北大学：Susan Whitfield- Gabrieli博士）以及机器学习在精神病学中的应用（姆克林Hospital：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, 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.).