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: 10685936
• 负责人: DANIEL G DILLON
• 金额: $ 81.25万
• 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/10685936
• 关键词: Adult; Behavior; Behavioral; Boston; Brain; Chronic; Clinical; Clinical Data; Cognitive Therapy; Combined Modality Therapy; DSM-V; Data; Diffusion Magnetic Resonance Imaging; Distress; Doctor of Philosophy; Electroencephalography; Emotional; Face; Feedback; Functional Magnetic Resonance Imaging; Functional disorder; Goals; Group Therapy; Health; Hospitals; Human; Image; Individual; Individual Differences; Investigation; Left; Machine Learning; Magnetic Resonance Imaging; Massachusetts; Measurement; Measures; Mental disorders; Modeling; Multimodal Imaging; Negative Valence; Neurobiology; Neurosciences; Participant; Patient-Focused Outcomes; Patients; Pharmacological Treatment; Positive Valence; Prediction of Response to Therapy; Process; Psychiatry; Psychopathology; Publishing; Recommendation; Reporting; Research; Research Domain Criteria; Research Personnel; Rest; Sampling; Selective Serotonin Reuptake Inhibitor; Sertraline; Severities; Social Anxiety Disorder; Source; Structure; System; Technology; Testing; Treatment Efficacy; Treatment outcome; Universities; Variant; Work; behavior measurement; biomarker identification; brain circuitry; clinical decision-making; clinical practice; cognitive control; comparison control; connectome; cost; disease classification; effective therapy; evidence base; imaging approach; improved; improved outcome; individual patient; multimodal data; multimodality; neural; neural circuit; neuroimaging; neuromechanism; optimal treatments; outcome prediction; partial response; personalized medicine; precision medicine; predicting response; predictive marker; predictive modeling; recruit; response; reward processing; skills; social anxiety; success; systematic review; temporal measurement; treatment guidelines; treatment response; usability; white matter

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.).

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，然后对没有或仅部分显示的患者开始SSRI 对初始CBT的反应。个性化医疗的一个重大进步和步骤是识别可靠的 治疗预测因子，并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， 来预测治疗反应。我们已经收集了令人信服的飞行员数据，指出这些神经标记物， 预测SAD对CBT的反应。下一个转化步骤和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 推动精神病学走向精准医疗这一目标将通过收集多模态数据有效地实现 为了更好地阐明SAD中的关键神经回路，与对照组相比， 动力样本，以及差异治疗相关的神经回路的变化（目标接合）。的 最终目标是有效地治疗所有患者，而不仅仅是少数患者，而且不知道为什么，并阐明 与有效治疗相关的脑回路，以告知精神病理学，疾病分类学和治疗 常见的精神疾病基于这些原因，我们建议招募大量SAD患者（n = 190）和健康对照组（n = 50），以检查相关神经回路的差异， 治疗反应的神经标志物。SAD患者将首先接受CBT组治疗。那些表现出 没有或只有部分反应，然后接受个人和定制的CBT加SSRI。除了MRI 措施，我们将检查脑电图和行为措施，以确定是否有可能更便宜 神经预测因子的相关性，可以很容易地在临床实践中实施。我们召集了一队 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（姆克林医院：克里斯蒂安·韦伯博士）。