Modeling and Mapping Human Action Regulation Networks

人类行为调节网络的建模和映射

• 批准号: 10712708
• 负责人: Vasileios N Christopoulos
• 金额: $ 103.86万
• 依托单位: UT SOUTHWESTERN MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10712708
• 关键词: Affect; Architecture; Basal Ganglia; Behavior; Behavioral; Brain; Clinical; Collaborations; Computer Models; Data; Deep Brain Stimulation; Disease; Disparate; Effectiveness; Environment; Face; Failure; Funding; Gilles de la Tourette syndrome; Goals; Human; Hybrids; Individual; Joystick; Manufactured basketball; Maps; Mediating; Mental disorders; Modeling; Motor; Movement; Neurobiology; Neurosciences; Neurosurgical Procedures; Obsessive-Compulsive Disorder; Operative Surgical Procedures; Outcome; Parkinson Disease; Participant; Pathway interactions; Patients; Persons; Physiological; Physiology; Regulation; Research Personnel; Resolution; Rewards; Role; STN stimulation; Signal Transduction; Source; Specificity; Structure of subthalamic nucleus; Testing; Update; Validation; Work; aged; brain circuitry; brain dysfunction; cost; design; environmental change; experimental study; flexibility; high reward; implantation; improved; kinematics; motor behavior; nervous system disorder; neural; neural circuit; neuromechanism; neurophysiology; neuropsychiatric disorder; neuroregulation; neurorestoration; predictive modeling; response; sensorimotor system; theories; volunteer

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.

抽象的 人类可以根据环境的更新需求快速调节行为。 动作调节是动作抑制，其失败会导致各种神经精神疾病，例如 如帕金森病（PD）、强迫症和抽动秽语综合症等。 至少有 3 种方式：(i) 动作选择 – 选择一项动作需要抑制替代运动计划，(ii) 彻底停止——当反应不恰当时抑制反应；(iii) 行动转换——改变 尽管人们付出了大量努力来了解大脑如何应对环境变化。 选择、停止和切换动作，这些动作调节功能背后的机制等等 重要的是，它们之间的相互关系仍然难以捉摸，部分原因在于研究很少探索， 一起表征和研究这些功能，使得很难开发一个统一的理论来解释 当前的提案旨在通过以下方式增进我们的理解。 开发一种神经计算模型，与之前的模型不同，它集成了多个来源的信息 （例如，目标的价值、改变动作的成本、上下文信息）并预测电机的运动学 我们将直接研究三种不同类型的行为调节的行为和基础神经机制。 通过接受深部脑刺激的患者志愿者的颅内记录评估模型预测 这些手术提供了获得多焦点皮质和基底的独特机会。 神经节（BG）记录在三个动作中具有高时间和光谱分辨率以及空间特异性 总体目标将通过三个目标来实现：在目标 1 中，我们将收集行为。 来自 PD 患者和年龄匹配的神经典型参与者的数据，这些参与者执行的任务包括选择、 神经典型组的运动行为将被用来发展。 模拟动作调节中的额叶-BG 回路的神经计算模型然后，我们将评估如何进行。 模型架构神经机制的特定变化可预测 PD 的运动行为 在目标 2 中，我们将评估有关行动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）的神经记录将在没有或有时间和空间精确的情况下被收集。 在目标 3 中，我们将通过刺激底丘脑核 (STN) 来研究 STN 在动作选择中的因果作用。 通过记录 PD 的神经活动来评估关于切换动作机制的模型预测 总体而言，成功完成 STN 刺激将提供统一的行动理论。 人脑的调节，通过行为和生理验证，开辟了新的途径 提高 DBS 和其他神经恢复疗法神经调节的有效性。