Neurobiology of Alcohol and Nicotine Co-Addiction

威廉·霍夫曼，博士，医学博士，PI：酒精和尼古丁联合成瘾的神经生物学 目的：这项建议，酒精和尼古丁联合成瘾的神经生物学解决了关键 酒精使用障碍(AUD)康复的神经生物学信息缺乏(Etoh) 和尼古丁(NIC)使用退伍军人。AUD和尼古丁使用障碍(NUD)，几乎完全吸烟， 是美国最常见的滥用(非处方药)物质。 退伍军人。尽管全国范围内的估计是澳大利亚/非美元共同上瘾的比率为80%，但 退伍军人事务部波特兰卫生保健系统的药物滥用治疗计划 (VAPORHCS)发现，90%接受酒精使用障碍(AUD)治疗的退伍军人也符合尼古丁标准 使用无序(NUD)。 计划：SA 1：通过多项对比测试酒精和尼古丁联合成瘾的双重网络模型 AUD、NUD、NOUD和CS的神经方面：a)基于任务的(PDD)和应激调制线索诱导 Craving，b)皮质密度的体积估计(基于体素的形态测量[VBM]，c)解剖学 (扩散张量成像[DTI]和d)静息状态功能连接性。 SA 2：开发一个集成了行为、任务和休息状态功能的机器学习模型 活化度、体积数据和结构连接性，a)区分四组；b) 预测3个月后的治疗结果。 方法：4组受试者(单纯饮酒[AUD]酒精加吸烟[NUD/AUD=NOUD]，受试者 吸烟者和从不上瘾的控制组[CS])将从波特兰VA医疗保健中心招募 系统(VAPORHCS)。所有人都将在基线上接受全面评估，包括神经成像， 认知测试，仔细的人口统计学和物质使用史和实验室评估。澳元和 AUD/NUD组将在三个月后每月与整个电池一起进行跟踪和重新评估 任命监督进展情况。我们假设支持向量机学习算法将是 能够使用这些度量将对象分类为AUD、NUD或两者都不是，并且算法将预测 3个月后的结果(戒酒或复发)。 与退伍军人事务部的任务相关：AUD和吸烟是最常见的成瘾障碍 退伍军人和共同上瘾是规则。这项提议将解开单纯性脑脊髓炎的神经生物学关联 并对这些物质共同上瘾，并开发一种模型，帮助预测治疗反应。 关键词：酒精、尼古丁、联合成瘾、结果

William Hoffman, PhD, MD, PI: Neurobiology of Alcohol and Nicotine Co-Addiction OBJECTIVE: This proposal, Neurobiology of Alcohol and Nicotine Co-Addiction addresses the critical absence of information about the neurobiology of recovery from Alcohol Use Disorder (AUD) in alcohol (EtOH) and nicotine (NIC) using veterans. AUD and nicotine use disorder (NUD), almost entirely cigarette smoking, are the most commonly abused (non-prescription) substances in the U.S. Co-addiction is particularly high in military veterans. Although nationwide estimates peg the rate of AUD/NUD co-addiction at 80%, the Substance Abuse Treatment Program (SATP) at the Veterans Affairs Portland Health Care System (VAPORHCS) finds that 90% of veterans treated for Alcohol Use Disorder (AUD) also meet criteria for Nicotine Use Disorder (NUD). PLAN: SA 1: Test a dual network model of alcohol and nicotine co-addiction via contrast of multiple neural aspects of AUD, NUD, NAUD and CS: a) task based (PDD) and stress modulated cue induced craving, b) volumetric estimates of cortical density (voxel-based morphometry [VBM], c) anatomical (diffusion tensor imaging [DTI] and d) resting state functional connectivity. SA 2: Develop a machine learning model that integrates behavioral, task and resting state functional activation, volumetric data and structural connectivity that a) differentiates the four groups and b) predicts treatment outcome at 3 months. METHODS: Four subject groups (Alcohol alone [AUD] alcohol plus smoking [NUD/AUD = NAUD], subjects who smoke [NUD] and never addicted controls [CS]) will be recruited from the Portland VA Health Care System (VAPORHCS). All will undergo a comprehensive evaluation at baseline including neuroimaging, cognitive testing, careful demographic and substance use history and laboratory evaluation. The AUD and AUD/NUD groups will be followed and re-evaluated with the entire battery at three months with monthly appointments to monitor progress. We hypothesize that a support vector machine learning algorithm will be able to use the measures to classify subjects as AUD, NUD both or neither and that the algorithm will predict outcome (sobriety or relapse) at 3 months. RELEVANCE TO THE VA MISSION: AUD and cigarette smoking are the most common addictive disorders in veterans and co-addiction is the rule. This proposal will disentangle the neurobiological correlates of single and co-addiction to these substances and develop a model that can help predict treatment response. Key words: Alcohol, nicotine, co-addiction, outcome