Modeling and Mapping Human Action Regulation Networks

摘要 人类可以根据环境的最新需求快速调整行为。的一个关键组件 动作调节是动作抑制，动作抑制的失败会导致各种神经精神疾病，如 如帕金森氏病(PD)、强迫症和多发性抽动症。动作抑制发生在 至少有3种方式：(I)行动选择--选择一个行动需要抑制其他行动计划，(Ii) 彻底停止-当反应变得不适当时抑制反应和(Iii)动作切换-改变 应对环境变化的行动计划。尽管人们花了大量的努力来理解大脑是如何 选择、停止和切换动作、这些动作调节功能的基础机制等 重要的是，它们如何相互联系仍然难以捉摸。这一挑战的部分原因在于，研究很少探索， 共同描述和研究这些功能，这使得很难开发出一个统一的理论来解释 行动监管的计算方面。目前的建议旨在通过以下方式促进我们的理解 开发一种神经计算模型，与以前的模型不同，它集成了来自多个来源的信息 (例如，目标值、改变动作的成本、上下文信息)并预测电机的运动学 三种不同类型的行为调控的行为和基础神经机制。我们会直接 在接受脑深部刺激的患者志愿者中用颅内记录评估模型预测 植入手术。这些手术提供了一个独特的机会，以获得多焦点皮质和基底 神经节(BG)记录具有较高的时间和光谱分辨率以及三个动作的空间特异性 监管任务。总体目标将通过三个目标实现。在目标1中，我们将收集行为 来自PD患者和执行任务的老年匹配神经典型参与者的数据， 停止和切换到达动作。神经典型人群的运动行为将被用来发展 一个神经计算模型，模拟作用调节中的额叶-BG回路。然后，我们将评估如何 模型结构的神经机制的特定变化预测帕金森病的运动行为 病人。在目标2中，我们将评估关于行为选择机制的模型预测。 通过记录接受DBS植入手术的PD患者的神经活动来停止 丘脑底核(STN)。神经记录将在没有时间和空间精度的情况下收集 刺激丘脑底核(STN)以探讨STN在动作选择中的因果作用。在《目标3》中，我们将 通过记录帕金森病患者的神经活动来评估关于动作转换机制的模型预测 有STN刺激的患者断断续续。总体而言，成功完成将提供一个统一的行动理论 人脑的调节，通过行为和生理验证，开辟了新的途径 使用DBS和其他神经恢复性疗法提高神经调节的有效性。

Abstract Humans can rapidly regulate actions according to updated demands of the environment. A key component of action regulation is action inhibition, the failure of which contributes to various neuropsychiatric diseases, such as Parkinson’s disease (PD), obsessive compulsive disorder and Tourette syndrome. Action inhibition occurs in at least 3 ways: (i) action selection – selecting one action requires suppressing alternative motor plans, (ii) outright stopping – inhibiting a response when it is rendered inappropriate and (iii) action switching – change action plans in response to environmental changes. Despite the extensive effort to understand how the brain selects, stops and switches actions, the mechanism underlying these action regulation functions, and more importantly, how they inter-relate remain elusive. Part of this challenge lies in the fact that studies rarely explore, characterize, and investigate these functions together, making it difficult to develop a unified theory that explains the computational aspects of action regulation. The current proposal aims to advance our understanding by developing a neurocomputational model that, unlike prior models, integrates information from multiple sources (e.g., value of targets, cost for changing an action, contextual information) and predicts both kinematics of motor behavior and the underpinning neural mechanisms across 3 distinct types of action regulation. We will directly evaluate model predictions with intracranial recordings in patient volunteers undergoing deep brain stimulation implantation surgeries. These surgeries provide a unique opportunity to obtain multi-focal cortical and basal ganglia (BG) recordings with high temporal and spectral resolution and spatial specificity across the three action regulation tasks. The overarching goal will be achieved through three aims. In Aim 1, we will collect behavioral data from PD patients and aged-match neurotypical participants performing tasks that involves selecting, stopping and switching reaching actions. The motor behavior of the neurotypical group will be used to develop a neurocomputational model that simulate the fronto-BG circuits in action regulation. Then, we will assess how specific changes on the neural mechanisms of the model architecture predict the motor behavior of the PD patients. In Aim 2, we will evaluate the model predictions about the mechanisms of action selection relative to stopping by recording neural activity from PD patients who undergo surgery for DBS implantation of the subthalamic nucleus (STN). Neural recordings will be collected without and with temporally and spatially precise subthalamic nucleus (STN) stimulation to investigate the causal role of STN in action selection. In Aim 3, we will evaluate the model predictions about the mechanisms for switching actions by recording neural activity from PD patients with the STN stimulation off and on. Overall, successful completion will provide a unified theory of action regulation in the human brain, with both behavioral and physiological validation, opening new avenues on improving the effectiveness of neuromodulation with DBS and other neurorestorative therapies.