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

基本信息

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
项目类别：
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

项目摘要

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，然后辅助地启动SSRI，以表现为没有或仅部分的患者对初始CBT的响应。迈出个性化医学的重大进步和一步是确定可靠的治疗预测因素并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，指向这样的神经标志物预测SAD对CBT的反应。下一个翻译步骤和我们的主要目的是应用最新状态计算精神病学方法进一步建立这些神经标志物的证据将精神病学转向精确医学。通过收集多模式数据，将有效实现此目标与先进的神经影像在井中的对照相比动力样本以及与神经回路的差异治疗相关的变化（目标参与）。这最终目标是有效治疗所有患者，不仅少数患者，也不知道原因，并照亮了所有患者与有效治疗相关的脑电路，以告知精神病理学，典型学和治疗常见的精神障碍。由于这些原因，我们建议招募大量SAD患者（n = 190）和健康对照（n = 50），以检查相关神经记录的差异，这也将被用作治疗反应的神经标志物。 SAD患者将首先接受CBT组治疗。那些展示的人然后，不会或唯一的部分响应将接收个人和量身定制的CBT加上SSRI。除了MRI 措施，我们将检查脑电图和行为措施，以确定是否可能有更便宜的可以在临床实践中轻松实施的神经数字的相关性。我们组建了一个团队马萨诸塞州理工学院（MIT; John D.）拥有互补专业知识的熟练研究人员 E. Gabrieli博士，波士顿大学（BU； Stefan G. Hofmann，博士）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）。