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

基本信息

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
项目类别：
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

项目摘要

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（由申请人提供）：成瘾是一种异常学习的疾病，而预测这些物质的环境线索会劫持大脑中的正常奖励回路，并相对于其他所有回路变得被高估，从而导致药物渴望的灾难性循环。这些异常的刺激-结果（SO）关联表现在额叶（FL）网络中，这些网络通常会驱动对健康至关重要的各种行为。在成瘾的大脑中，这些回路变得适应不良，导致强迫行为，无法发挥系统最初拥有的灵活性。目前，存在两种主要的成瘾治疗途径：行为治疗和神经精神治疗，旨在管理适应不良的 SO 关联。通过打破习惯性吸毒行为，尽管神经精神治疗广泛针对整个神经递质系统，而不考虑特定的 SO 关联，从而影响正常的奖励处理和学习。目前的资助，我们的目标是通过脑机接口（BMI）框架在系统水平上精确定位和修改 SO 关联来测试治疗成瘾的替代方法。我们建议通过识别 SO 的潜在神经表征来实现。 FL 网络中的关联并通过电微刺激对其进行修改，我们可以提供一种方法来消除成瘾行为，而不会产生不必要的副作用。修改 SO 关联以治疗成瘾需要了解 SO 关联在 FL 网络中是如何形成的以及如何操纵这些形成。通过有针对性的电刺激。因此，在 SO 学习过程中必须记录和刺激大量神经元，例如，最近的研究已经通过电刺激来操纵感觉网络来影响学习，从而优化了 BMI 假体的控制。采用相同的方法来评估信号来改变 FL 网络中的 SO 关联学习。我们的实验将集中在两个主要构建块上。首先，我们将操纵最佳刺激参数和 FL 目标来修改 SO 关联。大范围刺激的影响其次，我们将使用 BMI 框架实时解码来自大量 FL 神经元的给定刺激的值信息，作为控制信号。我们将研究刺激如何在开环和闭环条件下影响 FL 回路中代表的 SO 关联。