The Unlearning of Stimulus-Outcome Associations through Intracortical Microstimulation

通过皮质内微刺激忘记刺激-结果关联

• 批准号: 9262185
• 负责人: Joni D Wallis
• 金额: $ 19.63万
• 依托单位: UNIVERSITY OF CALIFORNIA BERKELEY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-04-15 至 2019-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9262185
• 关键词: Addictive Behavior; Adopted; Adverse effects; Affect; Animals; Automobile Driving; Award; Behavior; Behavior Therapy; Behavioral; Brain; Cognitive Therapy; Compulsive Behavior; Coupled; Cues; Decision Making; Development; Devices; Disease; Electric Stimulation; Food Interactions; Foundations; Functional disorder; Grant; Health; Intake; Intervention; Learning; Location; Methods; Modification; Monkeys; Neurons; Neurotransmitters; Organism; Outcome; Performance; Pharmaceutical Preparations; Pharmacology; Population; Process; Prosthesis; Psychological reinforcement; Relapse; Rewards; Role; Sensory; Signal Transduction; Site-Directed Mutagenesis; Social Interaction; Specificity; Stimulus; System; Technology; Testing; Time; Training; addiction; awake; base; brain machine interface; classical conditioning; drug craving; electrical microstimulation; experimental study; flexibility; learning outcome; microstimulation; mind control; neuropsychiatry; novel; psychologic; public health relevance; relating to nervous system; reward circuitry; reward processing; social; treatment strategy

 DESCRIPTION (provided by applicant): Addiction is a disease of aberrant learning. Addictive substances, and the environmental cues that predict those substances, hijack normal reward circuitry in the brain and become overvalued relative to all else, leading to a catastrophic cycle of drug craving, seeking, and intake behaviors at the expense of healthy behaviors. These aberrant stimulus-outcome (SO) associations are manifested in frontolimbic (FL) networks that normally drive a wide range of behaviors vital for healthy function. In the addicted brain, these circuits become maladaptive, leading to compulsive behaviors, incapable of the flexibility the system originally possessed. Currently, two major avenues of treatment for addiction exist: behavioral and neuropsychiatric treatments. Behavioral treatments aim to manage maladaptive SO associations by breaking habitual drug taking behavior, though relapse rates are high. Neuropsychiatric treatments broadly target neurotransmitter systems as a whole without regard to specific SO associations, thereby impacting normal reward processing and learning. In the current grant, we aim to test an alternate approach to the treatment of addiction by the precise targeting and modification of SO associations at the systems level through a brain-machine interface (BMI) framework. We propose that by identifying the underlying neural representations of SO associations in FL networks and modifying them through electrical microstimulation, we can provide a means to unlearn addictive behaviors without unwanted side effects. Modifying SO associations for the treatment of addiction requires understanding how SO associations are formed in FL networks and how these formations can be manipulated through targeted electrical stimulation. To do so, large populations of neurons must be recorded and stimulated during SO learning. Recent advances in recording technology enable such experiments. For example, recent studies have manipulated sensory networks through electrical stimulation to affect learning, thus optimizing the control of BMI prostheses. We plan to adopt the same methods to manipulate value signals to alter SO associative learning in FL networks. Our experiments will focus on two major building blocks. First, we will determine optimal stimulation parameters and FL targets to modify SO associations. We will test this by determining the effects of a wide range of stimulation parameters and locations across the FL circuit on value-based learning and decision making (open loop). Second, we will use a BMI framework to decode value information about a given stimulus from large populations of FL neurons in real time as a control signal to preferentially apply targeted stimulation. This will enable closed-loop bidirectional control over SO associations. We will study how stimulation affects SO associations represented in the FL circuit across days in both open and closed-loop conditions.

 描述（适用提供）：成瘾是一种异常学习的疾病。成瘾是一种异常学习的疾病。成瘾以及预测这些物质的环境提示，劫持大脑中的正常奖励电路，并相对于其他所有物质而变得高估，从而导致灾难性的渴望，寻求和摄入行为的灾难性循环，而以健康行为为代价。这些异常的刺激结果（SO）关联体现在额叶（FL）网络中，这些网络通常会推动对健康功能至关重要的广泛行为。在附加的大脑中，这些电路变得不良适应性，导致了强迫性行为，无法实现系统最初假设的灵活性。目前，存在两个主要的成瘾治疗途径：行为和神经精神治疗。行为治疗旨在通过破坏习惯性药物行为来管理适应不良的行为，尽管中继率很高。神经精神疗法总体上广泛针对神经递质系统，而无需考虑特定的社会，从而影响了正常的奖励处理和学习。在当前的赠款中，我们旨在通过通过脑机界面（BMI）框架在系统级别对SO关联进行的精确靶向和修改来测试成瘾治疗的替代方法。我们建议，通过识别FL网络中SO关联的基本神经表示并通过电微刺激修改它们，我们可以提供一种手段来取消额外的行为而没有不必要的副作用。修改成瘾治疗的关联需要了解如何在FL网络中形成SO关联，以及如何通过靶向电气模拟来操纵这些地层。为此，必须在学习过程中记录和刺激大量神经元。记录技术的最新进展可以实现此类实验。例如，最近的研究通过电刺激操纵了感觉网络以影响学习，从而优化了对BMI假体的控制。我们计划采用相同的方法来操纵价值信号以更改FL网络中的关联学习。我们的实验将集中在两个主要的构件上。首先，我们将确定最佳刺激参数和FL目标以修改SO的关联。我们将通过确定跨FL电路的广泛刺激参数和位置对基于价值的学习和决策做出（开放循环）的影响来测试这一点。其次，我们将使用BMI框架来实时地解码来自大型FL神经元的给定刺激的价值信息，作为控制信号，以最好地应用目标刺激。这将实现对SO关联的闭环双向控制。我们将研究刺激如何影响，因此在开放环境和闭环条件下，在FL电路中代表的关联。