Control of the time course of dopamine release through optimized electrical brain stimulation.

项目摘要 脑深部结构的电刺激是神经系统因果调查的重要工具 来调节学习和决策。脑深部电刺激也是一种有价值的治疗工具， 神经系统疾病，如帕金森病和震颤，最近的数据表明， 刺激可以有效地治疗癫痫和严重抑郁症。尽管它的科学和翻译 应用中，很少有人知道电刺激如何影响神经元或神经元的持续活动。 释放神经调节剂，如多巴胺。了解电刺激如何影响多巴胺 释放是特别重要的，因为多巴胺参与学习，运动控制，决策， 神经可塑性有相当多的证据表明，多巴胺的功能是由时间进程决定的， release.例如，快速的“阶段性”释放（~1-2秒）涉及神经可塑性和奖励引导的神经活动。 学习时缓慢的“紧张”释放（几十秒）涉及运动控制和动机。知之甚少 关于大脑如何选择性地调节紧张性和阶段性释放，几乎没有方法可以控制 多巴胺释放的时间过程。开发这样的方法可能会导致1）新的实验方法 用于对相位性和紧张性释放在动机和运动控制中所起作用的因果调查，以及2） 翻译工具，以纠正扰乱模式的多巴胺释放的疾病，如帕金森氏病， 精神分裂症成瘾和抑郁症为了实现这些目标，我们小组的证据表明， 电脑刺激的频率和持续时间允许选择性地控制多巴胺的时程 release.我们的总体目标是表征脑刺激的参数，例如刺激 频率、变异性和目标脑区影响多巴胺释放的时间过程， 多巴胺在奖励引导学习中的作用我们的实验目标是确定（1）频率如何 并且在脑刺激期间传递的脉冲序列的可变性影响相位和紧张性多巴胺 释放，（2）大脑刺激和紧张和相位信号如何相互作用，以影响奖励驱动的学习，以及 (3)以及紧张性和阶段性信号传导如何影响神经元之间的相互作用和塑造神经可塑性。我们 实验方法包括用于多巴胺测量的伏安法、用于多巴胺测量的神经系综记录、用于多巴胺测量的神经系统记录、以及用于多巴胺测量的神经系统记录。 神经协调的测量，光遗传学和多巴胺释放的在线优化， 麻醉和行为大鼠。我们的计算目标是使用收集的数据来开发多尺度 系统和细胞模型，描述刺激频率和变化如何影响时间进程， 多巴胺释放我们预测，多尺度模型将优于目前的突触模型，并改善 科学家和临床医生在实验和治疗环境中控制多巴胺释放的能力。 这些模型也可以解释临床效应，例如最近来自人类患者的数据表明， 对大脑深部区域的电刺激可以减轻抑郁症。

Project Summary Electrical stimulation of deep brain structures is an essential tool for the causal investigation of neural systems that regulate learning and decision making. Deep brain electrical stimulation is also a valuable tool for treating neurological disorders such as Parkinson's disease and tremor, and recent data suggests that electrical brain stimulation may effectively treat epilepsy and severe depression. Despite its scientific and translational applications, little is known about how electrical stimulation affects the ongoing activities of neurons or the release of neuromodulators such as dopamine. Understanding how electrical stimulation affects dopamine release is particularly important given dopamine's involvement in learning, motor control, decision making, and neuroplasticity. There is considerable evidence that dopamine's function is determined by the time course of release. For example, fast, “phasic” release (~1-2 seconds) is involved in neuroplasticity and reward-guided learning while slow, “tonic” release (tens of seconds) is involved in motor control and motivation. Little is known about how the brain selectively regulates tonic and phasic release, and few methods exist for controlling the time course of dopamine release. Developing such methods could result in 1) new experimental approaches for the causal investigation of the roles phasic and tonic release play in motivation and motor control, and 2) translational tools to correct disrupted patterns of dopamine release in disorders such as Parkinson's disease, schizophrenia, addiction, and depression. Towards these goals, evidence from our group suggests that the frequency and duration of electrical brain stimulation allows selective control of the time course of dopamine release. Our general objective is to characterize how parameters of brain stimulation such as stimulation frequency, variability, and the brain region targeted impacts the time-course of dopamine release and dopamine's role in reward-guided learning. Our experimental objectives are to determine (1) how the frequency and variability of the sequence of pulses delivered during brain stimulation affects phasic and tonic dopamine release, (2) how brain stimulation and tonic and phasic signaling interact to affect reward-driven learning, and (3) and how tonic and phasic signaling affect interactions between neurons and shape neuroplasticity. Our experimental approaches involve voltammetry for dopamine measurement, neural ensemble recordings for measurement of neural coordination, optogenetics, and on-line optimization of dopamine release in anesthetized and behaving rats. Our computational objective is to use data collected to develop multi-scale systems and cellular models that describe how stimulation frequency and variance affect the time course of dopamine release. We predict that multi-scale models will outperform current synaptic models and improve the capacity of scientists and clinicians to control dopamine release in experimental and therapeutic settings. These models may also explain clinical effects such as recent data from human patients suggesting that electrical stimulation of deep brain regions reduces depression.