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

通过优化脑电刺激来控制多巴胺释放的时间过程。

• 批准号: 10285860
• 负责人: Stephen Leigh Cowen
• 金额: $ 111.76万
• 依托单位: UNIVERSITY OF ARIZONA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10285860
• 关键词: Affect; Behavior; Behavioral; Brain; Brain region; Cell model; Cognitive; Data; Decision Making; Deep Brain Stimulation; Disease; Dopamine; Drug Addiction; Electric Stimulation; Electrical Stimulation of the Brain; Epilepsy; Food; Frequencies; Goals; Human; Investigation; Investigational Therapies; Knowledge; Learning; Link; Measurement; Measures; Medial; Mental Depression; Methods; Mission; Modeling; Modernization; Motivation; Neuromodulator; Neuronal Plasticity; Neurons; Nucleus Accumbens; Outcome; Parkinson Disease; Patients; Pattern; Periodicity; Phase; Physiologic pulse; Physiological; Pilot Projects; Play; Prefrontal Cortex; Process; Psychological reinforcement; Public Health; Rattus; Research; Reversal Learning; Rewards; Role; Scanning; Schizophrenia; Scientist; Shapes; Signal Transduction; Structure; Synapses; System; Technology; Testing; Therapeutic; Time; Translational Research; Tremor; United States National Institutes of Health; Ventral Tegmental Area; addiction; clinical effect; disability; expectation; flexibility; human data; improved; innovative technologies; insight; mathematical model; median forebrain bundle; motor control; multi-scale modeling; nervous system disorder; neural stimulation; novel strategies; optogenetics; relating to nervous system; response; theories; tool

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.

项目摘要 深脑结构的电气模拟是神经系统因果投资的重要工具 调节学习和决策。深脑电刺激也是治疗的宝贵工具 神经系统疾病，例如帕金森氏病和震颤，最近的数据表明电脑 刺激可以有效治疗癫痫病和严重的抑郁症。尽管科学和翻译 应用，对电刺激如何影响神经元或神经元活动的活动知之甚少 释放神经调节剂，例如多巴胺。了解电刺激如何影响多巴胺 鉴于多巴胺参与学习，运动控制，决策和 神经可塑性。有大量证据表明多巴胺的功能取决于时间过程 发布。例如，快速的“阶段”版本（〜1-2秒）参与神经可塑性和奖励引导 学习缓慢，“补品”释放（数十秒）涉及运动控制和动机。鲜为人知 关于大脑如何有选择地调节滋补和阶段性的释放，并且几乎没有任何方法来控制 多巴胺释放的时间课程。开发这种方法可能会导致1）新的实验方法 为了在动机和运动控制方面发挥作用和补品发行作用的因果投资，2） 转化工具以纠正帕金森氏病等疾病中多巴胺释放的破坏模式， 精神分裂症，成瘾和抑郁症。达到这些目标，我们小组的证据表明 电脑刺激的频率和持续时间可以选择性控制多巴胺的时间过程 发布。我们的一般目标是表征大脑刺激的参数（例如刺激） 频率，可变性和靶向大脑区域会影响多巴胺释放的时间顺序 多巴胺在奖励指导学习中的作用。我们的实验目标是确定（1）频率如何 脑刺激过程中脉冲序列的变异性会影响阶段性多巴胺 释放，（2）大脑刺激以及滋补和阶段信号如何相互作用以影响奖励驱动的学习以及 （3）以及滋补和阶段信号如何影响神经元之间的相互作用和形状的神经塑性。我们的 实验方法涉及多巴胺测量的伏安法，神经合奏记录 测量神经元协调，光遗传学和多巴胺释放的在线优化 麻醉和行为的老鼠。我们的计算目标是使用收集的数据来开发多尺度 描述模拟频率和方差如何影响时间过程的系统和蜂窝模型 多巴胺释放。我们预测，多尺度模型将胜过电流突触模型并改善 科学家和临床医生控制实验和治疗环境中多巴胺释放的能力。 这些模型也可能解释了临床效应，例如来自人类患者的最新数据表明 深脑区域的电刺激减少了抑郁症。

项目成果

Automated system for training and assessing string-pulling behaviors in rodents.

用于训练和评估啮齿动物拉绳行为的自动化系统。

• DOI: 10.1101/2023.07.02.547431
• 发表时间: 2023
• 期刊: bioRxiv : the preprint server for biology
• 作者: Jordan,GiannaA;Vishwanath,Abhilasha;Holguin,Gabriel;Bartlett,MitchellJ;Tapia,AndrewK;Winter,GabrielM;Sexauer,MorganR;Stopera,CarolynJ;Falk,Torsten;Cowen,StephenL
• 通讯作者: Cowen,StephenL

Automated system for training and assessing reaching and grasping behaviors in rodents.

用于训练和评估啮齿动物的伸手和抓握行为的自动化系统。

• DOI: 10.1016/j.jneumeth.2023.109990
• 发表时间: 2024
• 期刊: Journal of neuroscience methods
• 影响因子: 3
• 作者: Jordan,GiannaA;Vishwanath,Abhilasha;Holguin,Gabriel;Bartlett,MitchellJ;Tapia,AndrewK;Winter,GabrielM;Sexauer,MorganR;Stopera,CarolynJ;Falk,Torsten;Cowen,StephenL
• 通讯作者: Cowen,StephenL