Neurocomputational Mechanisms for Addiction Heterogeneity, Impulsivity and Perseverance

成瘾异质性、冲动性和毅力的神经计算机制

• 批准号: 9980853
• 负责人: Xiaosi Gu
• 金额: $ 21.19万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9980853
• 关键词: Affect; Age; American; Animals; Area; Behavior; Behavior Control; Behavioral; Brain; Cause of Death; Consumption; Corpus striatum structure; Data; Decision Making; Development; Diagnosis; Dimensions; Disease; Dopamine; Dorsal; Drug Addiction; Drug Exposure; Drug usage; Education; Equilibrium; Exhibits; Feedback; Functional Magnetic Resonance Imaging; Gender; Goals; Habits; Heterogeneity; Human; Impulsive Behavior; Impulsivity; Individual; Individual Differences; Knowledge; Learning; Literature; Measures; Modeling; Motor; Neurocognitive; Neurotransmitters; Nicotine; Nicotine Dependence; Nicotine Use Disorder; Outcome; Pharmaceutical Preparations; Phase; Phenotype; Physiological; Play; Prefrontal Cortex; Process; Psychiatry; Research; Response to stimulus physiology; Role; Severities; Signal Transduction; Substance Use Disorder; Symptoms; System; Techniques; Testing; Tobacco smoking behavior; Ventral Striatum; Work; addiction; behavioral phenotyping; causal model; cigarette smoke; clinical heterogeneity; cohort; computer framework; goal oriented behavior; indexing; individualized medicine; neuromechanism; nicotine use; non-smoking; novel; predictive modeling; relating to nervous system; response; simulation; symptomatology; theories

Project Summary Studies in substance use disorders (SUDs) have identified a profound inter-subject variability, where a wide variety of multifaceted, dissociable behavioral phenotypes are correlated with addiction development and symptomatology. Even apparently incompatible behavioral expressions, such as impulsivity and inelasticity or perseverance, have been found to co-occur in SUDs and to signal similar addiction vulnerabilities. However, few neural or computational mechanisms have been described so far to account for such seemingly contradicting findings and individual differences, thus hindering the development of individualized diagnosis and treatment. The overarching goal of this project is to validate a new model of addiction using Nicotine Use Disorder (NUD) as test case. We propose to expand on previous theories to provide a more comprehensive neuro-computational framework of addiction that includes phenotypic variability and co-occurrence of impulsivity and perseverance, characterized in terms of effective connectivity in cortico-striatal circuits. The scientific premise for this project is grounded in decades of human and non-human animal work which have demonstrated the roles played in addiction by the ventral and dorsal corticostriatal systems, respectively responsible for goal oriented and habitual behavior. The simulation of the neural dynamics in these two circuits has allowed our model to describe addiction on two independent dimensions. On a first dimension, addictive drugs such as nicotine result in increased circuit gain and state transition stability in both ventral and dorsal cortico-striatal systems, amplifying preliminary evidence (impulsivity) and making choice selections become inelastic due to a feedback effect (perseverance). On a second dimension, which is not necessarily affected by drug exposure, our models converge in suggesting that this gain-related over-stability of both cortico-striatal circuits is aggravated by the presence of a “dominance” of either of the two circuit over the other. In aim 1, we will validate the model prediction that high circuit gain predicts greater behavioral impulsivity and perseverance. In aim 2 we will validate the model prediction that the balance between the two cortico-striatal circuits predicts drug use severity. Circuit gain and circuit balance will be tested in NUD individuals (n=32) and healthy controls (n=32), tasked with decision-making tasks. Circuit gain will be measured in terms of effective connectivity between cortical and striatal areas, within each circuit, and estimated with the use of Dynamic Causal Modelling (DCM). Circuit balance will be estimated using DCM for the ventro-dorsal effective connectivity, to establish dominance on a gradient. This proof-of-concept project can provide a new computational framework for drug addiction, and a quantitative model to characterize clinical heterogeneity, eventually informing individualized treatments.

项目摘要 对物质使用障碍（SUD）的研究已经确定了深远的受试者间变异性，其中很广 多种多样，可分离的行为表型与成瘾的发展和 症状学。即使显然是不兼容的行为表达，例如冲动性和非弹性 或毅力，已经发现在SUD中同时发生并发出类似的成瘾漏洞。 但是，到目前为止，很少有神经或计算机制可以解决此类 看似与发现和个体差异相矛盾，从而阻碍了 个性化的诊断和治疗。该项目的总体目标是验证一种新的模型 使用尼古丁使用障碍（NUD）作为测试案例的成瘾。我们建议将以前的理论扩展到 提供更全面的神经计算成瘾框架，包括表型 冲动性和毅力的可变性以及以有效为特征的 皮质纹状体电路中的连通性。该项目的科学前提是基于数十年的 人类和非人类动物作品已经证明了腹侧的成瘾作用 和背皮层系统，分别负责以目标为导向和习惯行为。 这两个电路中神经动力学的模拟使我们的模型描述了两个上瘾 独立的维度。在第一个维度上，其他药物（例如尼古丁）导致电路增加 腹侧和背皮纹状体系统中的增益和状态过渡稳定性，放大初步 由于反馈效果，证据（冲动）并使选择选择变得无弹性 （毅力）。在不一定受药物暴露影响的第二维度上，我们的模型 提示这两个皮质纹状体电路的这种与增益相关的过度稳定性汇总。 两个电路在另一个电路上存在“优势”。在AIM 1中，我们将验证模型 预测高电路获得预测更大的行为冲动和毅力。在目标2中，我们将 验证模型预测，即两个皮质 - 纹状体回路之间的平衡预测药物使用 严重程度。电路增益和电路平衡将在裸体个体（n = 32）和健康对照中进行测试 （n = 32），负责决策任务。电路增益将以有效的连通性来衡量 在每个电路内的皮质和纹状体区域之间，并使用动态因果估计 建模（DCM）。将使用DCM估算电路平衡的腹侧有效连通性， 在梯度上建立优势。该概念证明项目可以提供新的计算 药物成瘾的框架和定量模型以表征临床异质性，最终 告知个性化治疗。