Using Computational Neuroimaging and Extended Smartphone Assessment to Understand the Pathways Linking Threat-Related Brain Circuits to Alcohol Misuse Across Adulthood

酒精滥用是人类痛苦、发病和死亡的主要原因。现有的治疗方法 有疗效的虽然酒精滥用的根源是复杂和多因素的，但焦虑起着关键作用。模型 成瘾表明，许多饮酒者滥用酒精来缓解过度焦虑（“自我治疗”）。焦虑相关 状态、特征和紊乱增加了酒精使用和问题的几率，这些关联被放大了 在习惯性使用酒精来缓解的人中。最近的研究激发了一种假设，即焦虑- 酒精滥用反映了对不确定威胁的过度反应，这是焦虑的典型触发因素。然而， 在我们的理解中仍然存在着关键的差距：（G1）没有系统的，有力的努力来测试 不确定威胁电路与酒精滥用维度变化的相关性，阻碍了 更有效或可耐受的治疗剂。先前的成像研究主要集中在皮层区域。的 在成瘾和焦虑的动物模型中涉及的皮层下区域的相关性仍然不清楚。（G2） 计算精神病学认识到两种不同的不确定性：风险和模糊性。哪一个更 与酒精滥用有关的疾病仍然没有得到探索，阻碍了精确治疗的发展。（G3） 临床前工作已经确定了一种对不确定威胁敏感的分布式大脑回路，但它仍然存在。 不知道这个回路的哪些组成部分与焦虑引发的渴望和消费最相关， 真实的世界。为了解决这些问题，我们将招募240名AUD+成年人的种族多元化社区样本， 过度抽样那些用酒精缓解焦虑的人。参数化的威胁预测范例将使我们能够 探测电路对威胁不确定性的类别和维度变化敏感。智能手机表型分析 将评估现实世界的威胁暴露，威胁不确定性，焦虑，渴望和酒精使用。这些数据将 使我们能够实现三个目标。(A1)识别与威胁不确定性最相关的大脑区域和方面， 酒精使用、症状和问题的临床变化。(A2)利用智能手机技术 可改变的因素--包括感知到的威胁和焦虑的改变--触发了渴望和消费。 (A3)融合功能磁共振成像和智能手机数据流将使我们能够打破不确定威胁电路， 在真实的世界中，找出与焦虑引发的酒精滥用最相关的成分--这是一个无法实现的目标。 单独使用任何一种工具。这种综合方法有望在分析和评估的各个层次之间架起桥梁， 从未被用于酒精滥用。摘要：AUD是出了名的异构，有> 2，000个独特的 临床特征我们的重点是在一个多样化的，临床相关的， sample有望克服这一障碍，并为潜在的神经生物学提供新的见解。建筑 在建立良好的负强化模型和心理生理学研究成果的基础上， 将提供一个潜在的变革性机会，以确定新的治疗目标;指导开发 新的翻译模式;并为新的数字干预措施的发展提供信息。

Alcohol misuse is a leading cause of human misery, morbidity, and mortality. Existing treatments are far from curative. While the roots of alcohol misuse are complex and multifactorial, Anxiety plays a key role. Models of addiction suggest that many drinkers misuse alcohol to relieve excess anxiety (‘self-medicate’). Anxiety-related states, traits, and disorders increase the odds of alcohol use and problems, and these associations are magnified among individuals who habitually use alcohol for relief. Recent research motivates the hypothesis that anxiety- fueled alcohol misuse reflects hyper-reactivity to Uncertain Threat, the prototypical trigger of anxiety. Yet several key gaps in our understanding remain: (G1) There have been no systematic, well-powered efforts to test the relevance of Uncertain Threat circuitry to dimensional variation in alcohol misuse, impeding the development of more effective or tolerable therapeutics. Prior imaging studies have largely focused on cortical regions. The relevance of subcortical regions implicated in animal models of addiction and anxiety remains unclear. (G2) Computational psychiatry recognizes 2 distinct kinds of uncertainty: Risk and Ambiguity. Which of these is more relevant to alcohol misuse remains unexplored, thwarting the development of precision treatments. (G3) Preclinical work has identified a distributed brain circuit that is sensitive to Uncertain Threat, but it remains unknown which components of this circuit are most relevant to anxiety-fueled craving and consumption in the real world. To address these questions, we will recruit a racially diverse community sample of 240 AUD+ adults, over-sampling those who use alcohol for anxiety relief. Parametric threat-anticipation paradigms will allow us to probe circuits sensitive to categorical and dimensional variation in threat uncertainty. Smartphone phenotyping will assess real-world threat exposure, threat uncertainty, anxiety, craving, and alcohol use. These data will enable us to address 3 aims. (A1) Identify the brain regions and facets of threat uncertainty most relevant to clinical variation in alcohol use, symptoms, and problems. (A2) Use smartphone technology to pinpoint modifiable factors—including alterations in perceived threat and anxiety—that trigger craving and consumption. (A3) Fusing the fMRI and smartphone data-streams will allow us to fractionate the Uncertain Threat circuit and pinpoint the components most relevant to anxiety-fueled alcohol misuse in the real world—an aim that cannot be addressed using either tool in isolation. This integrative approach promises to bridge levels of analysis and has never been applied to alcohol misuse. Summary: AUD is notoriously heterogeneous, with >2,000 unique clinical profiles. Our focus on a theoretically coherent set of dimensional measures in a diverse, clinically relevant sample promises to overcome this barrier and provide fresh insights into the underlying neurobiology. Building on well-established negative reinforcement models and a fruitful line of psychophysiological research, this study will provide a potentially transformative opportunity to identify new treatment targets; guide the development of new translational models; and inform the development of new digital interventions.