论文标题
奖励介导的吸毒学习的数学模型
A mathematical model of reward-mediated learning in drug addiction
论文作者
论文摘要
已知滥用的物质会激活和破坏大脑奖励系统中的神经元电路。我们提出了一个简单且易于解释的动态系统模型，以描述吸毒的神经生物学，其中结合了奖励预测错误的精神科概念（RPE），药物引起的激励显着性（IST）和对手过程理论（OPT）。药物诱导的多巴胺释放，以愉悦的，积极的“ a-processes”（Euphoria，Rush）激活双相奖励反应，然后是令人不愉快的，令人不愉快的，负的“ B-过程”（渴望，戒断）。连续摄入触发的神经适应过程增强了奖励反应的负分量，用户通过增加药物剂量和/或摄入频率来补偿这些奖励反应的负分量。生理变化与药物自我管理之间的积极反馈导致习惯，宽容和最终使人完全成瘾。我们的模型在定性上产生了不同的成瘾途径，这些途径可以代表各种用户概况（遗传学，年龄）和药物效力。我们发现，患有或神经自适应的用户对吸毒的强烈反应是成瘾的风险。最后，我们包括减轻戒断症状的可能机制，例如通过使用用于排毒的美沙酮或其他辅助药物。
Substances of abuse are known to activate and disrupt neuronal circuits in the brain reward system. We propose a simple and easily interpretable dynamical systems model to describe the neurobiology of drug addiction that incorporates the psychiatric concepts of reward prediction error (RPE), drug-induced incentive salience (IST), and opponent process theory (OPT). Drug-induced dopamine releases activate a biphasic reward response with pleasurable, positive "a-processes" (euphoria, rush) followed by unpleasant, negative "b-processes" (cravings, withdrawal). Neuroadaptive processes triggered by successive intakes enhance the negative component of the reward response, which the user compensates for by increasing drug dose and/or intake frequency. This positive feedback between physiological changes and drug self-administration leads to habituation, tolerance and eventually to full addiction. Our model gives rise to qualitatively different pathways to addiction that can represent a diverse set of user profiles (genetics, age) and drug potencies. We find that users who have, or neuroadaptively develop, a strong b-process response to drug consumption are most at risk for addiction. Finally, we include possible mechanisms to mitigate withdrawal symptoms, such as through the use of methadone or other auxiliary drugs used in detoxification.
