Goyal Abhinav, Karanovic Una, Blaha Charles D, Lee Kendall H, Shin Hojin, Oh Yoonbae
Mayo Clinic Medical Scientist Training Program, Mayo Clinic, Rochester, Minnesota 55905, United States.
Department of Neurologic Surgery, Mayo Clinic, Rochester, Minnesota 55905, United States.
ACS Omega. 2024 Jul 24;9(31):33563-33573. doi: 10.1021/acsomega.4c01322. eCollection 2024 Aug 6.
Dopamine (DA) is a neurotransmitter present within the animal brain that is responsible for a wide range of physiologic functions, including motivation, reward, and movement control. Changes or dysfunction in the dynamics of DA release are thought to play a pivotal role in regulating various physiological and behavioral processes, as well as leading to neuropsychiatric diseases. Therefore, it is of fundamental interest to neuroscientists to understand and accurately model the kinetics that govern dopaminergic neurotransmission. In the past several decades, many mathematical models have been proposed to attempt to capture the biologic parameters that govern dopaminergic kinetics, with each model seeking to improve upon a previous model. In this review, each of these models are derived, and the ability of each model to properly fit two fast-scan cyclic voltammetry (FSCV) data sets will be demonstrated and discussed. The dopamine oxidation current in both FSCV data sets exhibits hang-up and overshoot behaviors, which have traditionally been difficult for mathematical models to capture. We show that more recent models are better able to model DA release that exhibits these behaviors but that no single model is clearly the best. Rather, models should be selected based on their mathematical properties to best fit the FSCV data one is trying to model. Developing such differential equation models to describe the kinetics of DA release from the synapse confers significant applications both for advancing scientific understanding of DA neurotransmission and for advancing clinical ability to treat neuropsychiatric diseases.
多巴胺（DA）是动物大脑中存在的一种神经递质，负责多种生理功能，包括动机、奖赏和运动控制。多巴胺释放动态的变化或功能障碍被认为在调节各种生理和行为过程中起关键作用，同时也会导致神经精神疾病。因此，理解并准确模拟控制多巴胺能神经传递的动力学过程，是神经科学家们的根本兴趣所在。在过去几十年里，人们提出了许多数学模型，试图捕捉控制多巴胺能动力学的生物学参数，每个模型都力求在前一个模型的基础上有所改进。在这篇综述中，我们推导了这些模型中的每一个，并展示和讨论了每个模型对两个快速扫描循环伏安法（FSCV）数据集的拟合能力。两个FSCV数据集中的多巴胺氧化电流均表现出拖尾和过冲行为，传统上数学模型很难捕捉到这些行为。我们表明，较新的模型能够更好地模拟表现出这些行为的多巴胺释放，但没有一个模型明显是最佳的。相反，应根据模型的数学特性来选择模型，以最佳拟合想要模拟的FSCV数据。开发这样的微分方程模型来描述突触中多巴胺释放的动力学，对于推进对多巴胺能神经传递的科学理解以及提高治疗神经精神疾病的临床能力都具有重要应用价值。
