A mathematical model of reward-mediated learning in drug addiction
A mathematical model of reward-mediated learning in drug addiction
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DOI：
10.1063/5.0082997
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发表时间：
2022-02-01
期刊：
影响因子：
2.9
通讯作者：
D'Orsogna, Maria R.
中科院分区：
文献类型：
--
作者：
Chou, Tom;D'Orsogna, Maria R.
众所周知，滥用物质会激活和扰乱大脑奖励系统中的神经回路。我们提出了一个简单且易于解释的动态系统模型来描述药物成瘾的神经生物学，该模型融合了奖赏预测误差、药物诱导的激励突显和对手过程理论等精神病学概念。药物诱导的多巴胺释放通过愉悦、积极的“a-过程”(兴奋、兴奋)激活双相奖赏反应，随后是不愉快的、消极的“b-过程”(渴望、戒断)。由连续摄入引发的神经适应过程增强了奖励反应的负面成分，使用者通过增加药物剂量和/或摄入频率来弥补这一负面成分。生理变化和药物自我给药之间的这种正反馈会导致习惯性、耐受性，并最终导致完全上瘾。我们的模型产生了定性上不同的成瘾途径，可以代表不同的用户配置文件(遗传、年龄)和药物效力。我们发现，对药物消费有强烈b过程反应的吸毒者，或具有神经适应性的吸毒者，最容易上瘾。最后，我们包括了缓解戒断症状的可能机制，例如通过使用美沙酮或其他用于戒毒的辅助药物。
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, drug-induced incentive salience, and opponent process theory. 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.