The Unlearning of Stimulus-Outcome Associations through Intracortical Microstimulation

 描述（由申请人提供）：成瘾是一种异常学习的疾病。成瘾物质和预测这些物质的环境线索，劫持了大脑中正常的奖励回路，并相对于其他所有东西被高估，导致以健康行为为代价的药物渴望，寻求和摄入行为的灾难性循环。这些异常的刺激-结果（SO）关联表现在额叶边缘（FL）网络中，这些网络通常驱动对健康功能至关重要的广泛行为。在上瘾的大脑中，这些回路变得适应不良，导致强迫行为，无法实现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：行为和神经精神治疗。行为治疗旨在通过打破习惯性吸毒行为来管理适应不良的SO协会，尽管复发率很高。神经精神治疗广泛地针对神经递质系统作为一个整体，而不考虑特定的SO协会，从而影响正常的奖励处理和学习。在目前的资助中，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协会来测试成瘾治疗的另一种方法。我们提出，通过识别FL网络中SO关联的潜在神经表征，并通过电微刺激对其进行修改，我们可以提供一种方法来忘却成瘾行为，而不会产生不必要的副作用。修改SO协会成瘾的治疗需要了解SO协会是如何形成的FL网络，以及如何通过有针对性的电刺激来操纵这些形成。要做到这一点，在SO学习过程中必须记录和刺激大量的神经元。记录技术的最新进展使这种实验成为可能。例如，最近的研究已经通过电刺激操纵感觉网络来影响学习，从而优化BMI假体的控制。我们计划采用相同的方法来操纵值信号，以改变FL网络中的SO联想学习。我们的实验将集中在两个主要的组成部分。首先，我们将确定最佳的刺激参数和FL目标来修改SO关联。我们将通过确定广泛的刺激参数和FL电路上的位置对基于价值的学习和决策（开环）的影响来测试这一点。其次，我们将使用BMI框架来解码关于来自大量FL神经元的给定刺激的值信息，作为控制信号，以真实的时间优先施加靶向刺激。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代表在FL电路在开放和闭环条件下跨天SO协会。

 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.