Varieties of Impulsivity in Opiate and Stimulant Users

项目摘要 冲动已经成为发展的主要病因风险因素之一， 维持成瘾性障碍。然而，冲动和成瘾都是高度异质性的， 阻碍了对两者之间联系的理解。为了解决这种异质性，我们 在保加利亚开发了一个成瘾研究项目，保加利亚是欧洲主要的合成药物生产中心。 由于其战略地理位置， 在巴尔干半岛的贩毒路线上的位置通过与保加利亚同事长达17年的合作，我们 接触了主要依赖单一物质（“纯”）海洛因和安非他明的少数人群， 许多人长期禁欲。在母研究DA 021421中，我们测试了>800名参与者， 冲动性和相关的临床、人格和神经认知任务的综合评估组合 外化和内化表型。我们用Smokescreen阵列对参与者进行基因分型， 因鸦片和兴奋剂成瘾而不和谐的兄弟姐妹。我们结合了理论驱动（例如认知建模，联合 建模）与数据驱动（例如机器学习（ML））的计算方法，这证明特别 信息丰富，揭示了不同的多变量风险特征，其特征是阿片类药物和兴奋剂成瘾， 准确度。来自母研究的发现为我们的综合多学科研究提供了重要信息。 框架（Vassileva & Conrod，2019），它强调了冲动的不同维度的潜力， 为不同类型的成瘾提供信息的临床评估和干预发展。冲动也是 突出地在基于神经科学的启发式框架中用于成瘾的神经临床评估（ANA; Kwako等人，2016），其中提出，成功的成瘾治疗必须适应异质性 和不同的病因机制牵连成瘾，通过执行多维评估， 专注于冲动和强迫的三个神经功能领域：执行功能（EF），激励 显著性（IS）和消极情绪性（NE）。然而，由于ANA框架主要基于 在酒精使用障碍中的发现，尚不清楚这些领域如何推广到其他SUD， 例如鸦片剂和兴奋剂使用障碍。目前的竞争性续期申请旨在解决这一问题 关键的差距，以下具体目标：目标1：确定关键的个性，神经行为，多基因，和 阿片类药物和兴奋剂成瘾的计算标记物遵循ANA框架，使用综合 在母公司DA 021421中开发的评估组合和计算方法，有250名参与者（100 阿片类药物使用障碍，100例兴奋剂使用障碍，50例健康对照）;目的2：识别大脑 阿片类和兴奋剂成瘾中ANA 3个结构域（EF、IS、NE）的特征;探索性目标3：联合收割机 来自目的1和2的数据，以基于ANA结构域中涉及的神经回路来鉴定成瘾生物型。

Project Summary Impulsivity has gained prominence as one of the cardinal etiological risk factors for the development and maintenance of addictive disorders. However, both impulsivity and addiction are highly heterogeneous, which has hampered progress in understanding the link between the two. To address this heterogeneity, we have developed a program of addiction research in Bulgaria, a major European center for production of synthetic amphetamine-type stimulants and a key transit country for heroin trafficking, due to its strategic geographical position on the Balkan Drug Route. Through our 17-yearlong collaboration with Bulgarian colleagues, we have accessed rare populations of predominantly monosubstance-dependent (‘pure’) heroin and amphetamine users, many in protracted abstinence. In the parent DA021421 study, we have tested >800 participants with a comprehensive assessment battery of clinical, personality, and neurocognitive tasks of impulsivity and related externalizing and internalizing phenotypes. We genotyped participants with the Smokescreen array and enrolled siblings discordant for opiate and stimulant addictions. We combined theory-driven (e.g. cognitive modeling, joint modeling) with data-driven (e.g. machine learning (ML)) computational approaches, which proved particularly informative and revealed distinct multivariate risk profiles characterizing opiate and stimulant addictions with high degree of accuracy. Findings from the parent study significantly informed our integrative multidisciplinary framework (Vassileva & Conrod, 2019), which highlights the potential for distinct dimensions of impulsivity to inform clinical assessment and intervention development for different types of addictions. Impulsivity also figures prominently in the neuroscience-based heuristic framework for the neuroclinical assessment of addictions (ANA; Kwako et al., 2016), which proposes that successful addiction treatment must accommodate the heterogeneity and different etiological mechanisms implicated in addictions, by performing multidimensional assessments focusing on three neurofunctional domains of impulsivity and compulsivity: executive function (EF), incentive salience (IS), and negative emotionality (NE). However, because the ANA framework is based primarily on findings in alcohol use disorder, it is not well understood how these domains might generalize to other SUD, such as opiate and stimulant use disorders. The current competing renewal application aims to address this critical gap with the following specific aims: Aim 1: Identify key personality, neurobehavioral, polygenic, and computational markers of opiate and stimulant addiction following the ANA framework, using the comprehensive assessment battery and computational methods developed in the parent DA021421 with 250 participants (100 with opiate use disorder, 100 with stimulant use disorder, and 50 healthy controls); Aim 2: Identify the brain signatures of the 3 ANA domains (EF, IS, NE) in opiate and stimulant addictions; Exploratory Aim 3: Combine data from Aims 1 and 2 to identify addiction biotypes based on neurocircuitry implicated in the ANA domains.