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.

摘要 人类可以根据环境的更新需求迅速调整行动。的关键组成部分 动作调节是动作抑制，其失败导致各种神经精神疾病，如 如帕金森氏症（PD）、强迫症和抽动秽语综合征。动作抑制发生在 至少3种方式：（i）动作选择-选择一个动作需要抑制替代运动计划，（ii） 完全停止-当反应不适当时，抑制反应;（iii）动作转换-改变 应对环境变化的行动计划。尽管人们花了大量的精力去了解大脑是如何 选择、停止和切换动作，以及这些动作调节功能的基础机制，等等 重要是，它们之间的相互关系仍然难以捉摸。这一挑战的部分原因在于，研究很少探索， 描述，并一起研究这些功能，使得很难发展一个统一的理论来解释 行为调节的计算方面。目前的建议旨在通过以下方式促进我们的理解： 开发一个神经计算模型，与以前的模型不同，它整合了来自多个来源的信息 (e.g.,目标值、改变动作的成本、上下文信息），并预测运动的运动学 行为和三种不同类型的动作调节的基础神经机制。我们会直接 在接受脑深部电刺激的患者志愿者中评价颅内记录的模型预测 植入手术这些手术提供了一个独特的机会，以获得多灶性皮质和基底 神经节（BG）记录具有高的时间和光谱分辨率以及跨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患者和年龄匹配的神经典型参与者的数据， 停止和切换到达动作。神经典型组的运动行为将用于开发 一个神经计算模型，模拟动作调节中的额-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PD的运动行为 患者在目标2中，我们将评估模型预测的作用选择机制， 停止记录接受DBS植入手术的PD患者的神经活动 丘脑底核（subthalamic nucleus，简称TH）。神经记录将在没有和没有时间和空间精确的情况下收集， 刺激丘脑底核（subthalamic nucleus，ENA）来研究ENA在动作选择中的因果作用。在目标3中，我们 通过记录PD的神经活动，评估模型对转换行为机制的预测 总的来说，成功完成将提供一个统一的行动理论 人类大脑中的调节，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的神经调节效果。