Neurocomputational Mechanisms for Addiction Heterogeneity, Impulsivity and Perseverance

项目摘要 对物质使用障碍（SUD）的研究已经确定了一个深刻的受试者间差异，其中广泛的 各种多方面的、可分离的行为表型与成瘾发展相关， 医学。即使是明显不相容的行为表现，如冲动和缺乏弹性 或毅力，已被发现共同出现在SUD和信号类似的成瘾脆弱性。 然而，到目前为止，很少有神经或计算机制被描述来解释这种情况。 似乎矛盾的发现和个体差异，从而阻碍了发展， 个体化诊断和治疗。该项目的总体目标是验证一个新的模型， 尼古丁使用障碍（NUD）作为测试案例。我们建议扩展以前的理论， 提供了一个更全面的成瘾神经计算框架，包括表型 冲动和毅力的可变性和共同出现，其特征在于有效 皮质-纹状体回路的连接性。这个项目的科学前提是基于几十年来 人类和非人类动物的工作，已经证明了腹侧神经在成瘾中所起的作用， 和背侧皮质纹状体系统，分别负责目标导向和习惯性行为。的 对这两个回路中神经动力学的模拟使我们的模型能够在两个回路上描述成瘾。 独立的维度在第一个维度上，成瘾性药物如尼古丁导致回路增加， 腹侧和背侧皮质-纹状体系统的增益和状态转换稳定性， 由于反馈效应，证据（冲动）和选择选择变得缺乏弹性 （毅力）。在第二个维度上，不一定受到药物暴露的影响，我们的模型 集中在表明，这种增益相关的过度稳定的皮质纹状体电路是加剧了 两个电路中的任何一个电路相对于另一个电路的“优势”的存在。在目标1中，我们将验证模型 高电路增益预示着更大的行为冲动和毅力。在目标2中， 验证两个皮质-纹状体回路之间的平衡预测药物使用的模型预测 严重性。将在NUD个体（n=32）和健康对照组中测试回路增益和回路平衡 （n=32），负责决策任务。电路增益将根据有效连接性进行测量 皮质和纹状体区域之间，在每个回路内，并使用动态因果关系估计 建模（DCM）。将使用DCM估计腹背有效连通性的回路平衡， 在梯度上建立优势。这个概念验证项目可以提供一个新的计算 药物成瘾的框架，以及表征临床异质性的定量模型，最终 提供个性化治疗。

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.