FMRI & DTI Study on Neural Predictor of Treatment Outcome of Cocaine Dependence

描述（由申请人提供）：候选人的长期目标是成为一名独立的研究者，通过整合神经影像学和临床试验方法，对药物使用障碍（sud）的神经相关性进行跨学科研究，特别是基于大脑的药物使用障碍治疗结果预测。基于大脑的预测有可能指导最佳的患者-治疗匹配，以提高治疗sud的疗效。他的短期目标是通过K01奖支持的培训获得实现其长期目标所需的技能。该候选人曾在中国接受医学和诊断放射学培训，在美国俄亥俄州医学院获得神经科学博士学位，并在加州大学洛杉矶分校（UCLA）接受功能磁共振成像（fMRI）在SUDs上的T32博士后培训。自2007年以来，该候选人一直是耶鲁大学医学院精神科药物滥用部门的教员级调查员。他的研究主要集中在使用功能磁共振成像来识别可卡因依赖治疗结果的神经相关性。候选人现在寻求支持，以扩大他对其他SUD治疗结果的神经预测的研究范围，并获得额外的培训，重点是掌握弥散张量成像（DTI）分析，学习SUD治疗原则和临床试验方法。他的初步研究结果表明，更好的额叶和胼胝体白质完整性与更好的认知行为治疗可卡因依赖的治疗效果呈正相关。候选人寻求在使用不同疗法和其他sud患者的研究中复制和扩展这些初步发现。他的研究策略将是对fMRI、DTI和治疗结果数据进行一系列分析，以确定依赖可卡因或其他物质的患者的神经预测因素和治疗结果的相关性，这些患者参与了5项针对sud的不同治疗方法的临床试验。他的培训计划将涵盖以下主题：1)加强和扩大他在神经影像学方面的专业知识，2)学习sud的临床试验方法，特别是与大脑测量有关的方法，3)加强对成瘾治疗中大脑相关性的跨学科思考，4)增强纵向研究的统计知识，5)增强与人类研究和负责任的研究行为有关的伦理知识，以及6)在准备手稿和拨款申请方面的培训。他的导师包括神经影像学方面的国际知名专家（Marc Potenza， M.D.，博士，Godfrey Pearlson， M.D.）和SUD治疗研究方面的专家（Bruce Rounsaville， M.D., Kathleen Carroll，博士）。

DESCRIPTION (provided by applicant): The long-term goal of the candidate is to be an independent investigator conducting interdisciplinary studies on neural correlates of substance use disorders (SUDs), especially brain-based predictors of treatment outcomes of SUDs by integrating neuroimaging and clinical trial methods. Brain-based predictors have the potential to guide the optimal patient-therapy matching to improve the efficacy of treatment for SUDs. His short-term goal is to gain required skills for achieving his long-term goal through the training supported by this K01 Award. The candidate received training in Medicine and Diagnostic Radiology in China, a Ph.D. in Neuroscience from the Medical College of Ohio, and T32 Postdoctoral training in functional magnetic resonance imaging (fMRI) on SUDs from the University of California Los Angeles (UCLA). Since 2007, the candidate has been a faculty level investigator in the Division of Substance Abuse at the Yale School of Medicine Department of Psychiatry. His research has focused on using fMRI to identify neural correlates of treatment outcomes of cocaine dependence. The candidate now seeks support to expand the scope of his research on neural predictors of treatment outcomes of other SUDs and to obtain additional training focused on mastering diffusion tensor imaging (DTI) analysis and on learning principles of SUD treatment and clinical trial methods. His preliminary findings indicate that better integrity of white matter in the frontal lobes and anterior corpus callosum positively correlates with better treatment outcomes of cognitive behavioral therapy for cocaine dependence. The candidate seeks to replicate and expand these initial findings in studies that use different therapies and with patients of other SUDs. His research strategy will be to conduct a series of analyses of fMRI, DTI, and treatment outcome data to identify neural predictors and correlates of treatment outcomes of patients dependent on cocaine or other substances participating in 5 clinical trials of several different therapies for SUDs. His training program will cover the following topics: 1) enhancing and expanding his expertise in neuroimaging, 2) learning clinical trial methodologies for SUDs, particularly as they relate to brain measures, 3) sharpening interdisciplinary thinking about brain correlates of addiction treatments, 4) enhancing statistical knowledge for longitudinal research, 5) enhancing knowledge on ethics related to human studies and responsible conduct of research, and 6) training in preparation for manuscripts and grant proposals. His mentors include internationally prominent experts in neuroimaging (Marc Potenza, M.D., Ph.D., Godfrey Pearlson, M.D.) and in SUD treatment research (Bruce Rounsaville, M.D., Kathleen Carroll, Ph.D.). PUBLIC HEALTH RELEVANCE: Cocaine addiction is a major problem of public health, which is exacerbated by the fact that different patients respond to each therapy differently. The aim of this project is to identify brain-based predictors of treatment outcomes of different therapies for cocaine addiction using state of the art brain mapping methods. Brain-based predictors can help optimal matching of different patients with different therapies to improve the treatment efficacy of current therapies for cocaine addiction.