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 人类可以根据对环境的最新需求快速调节行动。一个关键组成部分 行动调节是行动抑制，导致各种神经精神疾病的失败，此类 作为帕金森氏病（PD），强迫症和图雷特综合症。行动抑制发生在 至少3种方法：（i）行动选择 - 选择一个动作需要抑制替代电机计划，（ii） 彻底停止 - 在使响应变得不适当和（iii）切换时抑制响应 - 更改 响应环境变化的行动计划。尽管付出了广泛的努力，以了解大脑如何 选择，停止和切换动作，这些动作调节功能的基础机制以及更多 重要的是，他们之间的相互关系仍然难以捉摸。这一挑战的一部分在于研究很少探索， 表征并一起研究这些功能，使得很难开发一种统一的理论来解释 动作调节的计算方面。当前的提议旨在通过 开发一个神经计算模型，与先前的模型不同，该模型从多个来源集成了信息 （例如，目标的价值，更改动作的成本，上下文信息）并预测电动机的两种运动学 行为和三种不同类型的作用调节类型的神经机制的基础。我们将直接 评估模型预测患者志愿者正在接受深脑刺激的患者志愿者中的颅内记录预测 植入手术。这些手术提供了获得多焦点皮质和基本的独特机会 在这三个动作中具有高临时和光谱分辨率和空间特异性的神经节记录（BG）记录 法规任务。总体目标将通过三个目标实现。在AIM 1中，我们将收集行为 来自PD患者和老年匹配的神经型参与者的数据，执行涉及选择， 停止并切换到达动作。神经型组的运动行为将用于发展 一种在作用调节中模拟额 - -BG电路的神经计算模型。然后，我们将评估如何 模型结构的神经机制的具体变化预测了PD的运动行为 患者。在AIM 2中，我们将评估有关相对于动作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 丘脑下核（STN）。神经记录将在没有临时和空间精确的情况下收集 丘脑下核（STN）刺激以研究STN在作用选择中的因果作用。在AIM 3中，我们将 通过记录PD的神经活动来评估有关转换动作机制的模型预测 具有STN刺激的患者关闭。总体而言，成功完成将提供统一的行动理论 人脑中的调节，具有行为和身体验证，开辟了新的途径 通过DBS和其他神经训练疗法提高神经调节的有效性。