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

基本信息

批准号：
9809076
负责人：
Xiaosi Gu
金额：
$ 25.43万
依托单位：
ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-08-01 至 2021-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9809076
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 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

项目摘要

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.

项目概要对物质使用障碍（SUD）的研究已经发现了一种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的发展和甚至明显不相容的行为表现，例如冲动和缺乏弹性。或毅力，已被发现在 SUD 中同时出现，并表明类似的成瘾脆弱性。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看似矛盾的研究结果和个体差异，从而阻碍了发展该项目的总体目标是验证一种新的个体化诊断和治疗模型。使用尼古丁使用障碍（NUD）作为测试案例，我们建议将以前的理论扩展到。提供更全面的成瘾神经计算框架，包括表型冲动性和毅力的可变性和共现性，以有效性为特征该项目的科学前提是基于数十年的经验。人类和非人类动物的工作已经证明了腹侧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行为。对这两个回路中的神经动力学的模拟使我们的模型能够描述两个方面的成瘾在第一个维度上，尼古丁等成瘾药物会导致回路增加。腹侧和背侧皮质纹状体系统的增益和状态转换稳定性，放大了初步由于反馈效应，证据（冲动）和选择变得缺乏弹性（毅力）在第二个维度上，我们的模型不一定受到药物暴露的影响。集中表明，两个皮质纹状体回路的这种与增益相关的过度稳定性因在目标 1 中，两个电路中的一个电路相对于另一个电路存在“优势”。在目标 2 中，我们预测高电路增益预示着更大的行为冲动和毅力。验证模型预测，即两个皮质纹状体回路之间的平衡可以预测药物使用电路增益和电路平衡将在 NUD 个体 (n=32) 和健康对照中进行测试。 (n=32)，负责决策任务的电路增益将根据有效连接来衡量。每个回路内的皮质和纹状体区域之间，并使用动态因果关系进行估计建模（DCM）将使用 DCM 来估计腹背有效连接，在梯度上建立主导地位这个概念验证项目可以提供一种新的计算方法。药物成瘾的框架，以及表征临床异质性的定量模型，最终告知个体化治疗。