Determining the role of muscle afferent signals in cortical proprioceptive representation

确定肌肉传入信号在皮质本体感觉表征中的作用

• 批准号: 10213612
• 负责人: Kyle Blum
• 金额: $ 5.47万
• 依托单位: NORTHWESTERN UNIVERSITY AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10213612
• 关键词: Address; Affect; Afferent Pathways; Area; BRAIN initiative; Biophysics; Brain; Characteristics; Computational Technique; Data; Data Analyses; Data Set; Distal; Environment; Feedback; Foundations; Goals; Golgi Tendon Organs; Impairment; Implanted Electrodes; Individual; Institution; Joints; Knowledge; Learning; Limb structure; Linear Models; Link; Maps; Modality; Modeling; Monkeys; Motor; Motor output; Movement; Muscle; Muscle Spindles; Nervous system structure; Neural Network Simulation; Neuraxis; Neurologic; Neurons; Output; Peripheral; Posture; Process; Property; Proprioception; Receptor Signaling; Research; Rest; Role; Scientist; Sensory; Signal Transduction; Somatosensory Cortex; Stimulus; Structure; System; Testing; Touch sensation; Training; Upper limb movement; Work; arm; base; biophysical model; body sense; career; experience; grasp; kinematics; motor control; motor impairment; multi-electrode arrays; neural circuit; neuroregulation; receptor; relating to nervous system; response; therapy development; training opportunity

Project Summary The overall premise of this proposal is to understand how the transformation of the signals from proprioceptive afferents within muscles leads to the representation of movements in somatosensory cortex. Proprioception is a fundamental part of the neural control of movement, evidenced by extreme movement impairments in individuals with proprioceptive loss. However, proprioception has been the topic of far less research compared to motor outputs and other sensory modalities, so how muscle afferent signals arising from muscle spindles and Golgi tendon organs, the peripheral afferents primarily responsible for proprioception, are transformed in the nervous system to represent movements in S1 remains a critical, unsolved mystery. In the proposed work, active and passive movements of the upper limb will be utilized in order to elicit different response characteristics from muscle spindles and Golgi tendon organs, which drive cortical proprioceptive signals in area 3a (S1). Simultaneously, neural activity from area 3a will be recorded using a multi- electrode array, as well as arm segment kinematics, and EMGs of proximal and distal arm muscles. Monkeys will be trained to perform a center-out reaching task while grasping the handle of a two-link planar manipulandum. In the horizontal plane, monkey’s arms will move via unperturbed reaches (ACT condition), passive perturbations applied at-rest (PAS condition), or passive perturbations applied during reaches (COMB condition). These movement conditions will elicit different muscle afferent feedback by altering combinations of movement kinematics, forces, and gamma motor drive. Muscle spindle afferent signals will be modeled using a biophysical model (previously developed by the PI) that accounts for both classical and paradoxical firing characteristics of muscle spindle afferents in active and passive conditions. Golgi tendon organ afferent signals will be modeled using the existing Mileusnic model. How these muscle afferent signals are transformed into movement representations in area 3a will be analyzed in two Specific Aims. In Aim 1, a combination of the modeled afferent signals and data collected from area 3a will be used determine how these signals are represented by area 3a neurons in all three movement conditions. Additionally, network activity recorded from across the array in 3a will be decoded to determine what class of movement variables are encoded in this brain area. In Aim 2, these modeled afferent inputs will be used to drive neural network models of proprioceptive transformations to determine to compare which model produces the most similar neural responses to area 3a. In total, these two aims provide two complimentary approaches for determining the role of proprioceptive afferent signals in cortical proprioceptive representation. This work will provide important foundational knowledge of how cortical proprioception arises from muscle afferents and is important for further understanding the role of proprioception in the neural control of movement.

项目摘要 这一建议的总体前提是理解从本体感觉信号如何转换 肌肉内的传入导致躯体感觉皮质运动的再现。本体感觉是一种 神经控制运动的基本部分，表现为个体的极端运动障碍 本体感受性丧失。然而，与运动相比，本体感觉的研究要少得多。 输出和其他感觉方式，那么肌肉传入信号是如何从肌梭和高尔基体产生的 肌腱器官，主要负责本体感觉的外周传入，在神经中转化。 代表S1运动的系统仍然是一个关键的未解之谜。 在拟议的工作中，将利用上肢的主动和被动运动来诱导 驱动皮质的肌梭和高尔基肌腱器官的不同反应特征 3a区本体感觉信号(S1)。同时，来自3a区的神经活动将被记录下来 电极阵列，以及手臂节段运动学，以及手臂近端和远端肌肉的肌电图。猴子 将接受训练，在抓住两连杆平面机械手的手柄的情况下执行中心外伸任务。 在水平面上，猴子的手臂将通过不受干扰的伸展(ACT条件)、被动扰动来移动 静止时应用(PAS条件)，或在到达期间应用被动扰动(梳状条件)。这些 运动条件会通过改变运动的组合来引起不同的肌肉传入反馈 运动学、力和伽马马达驱动。肌梭传入信号将使用生物物理建模 模型(之前由PI开发)，解释了经典和矛盾的激发特性 肌梭传入在主动和被动状态下。高尔基肌腱器官传入信号将被建模 使用现有的Mileusnic模型。 这些肌肉传入信号如何在3a区转换为运动表征将是 具体分析了两个目的。在目标1中，建模的传入信号和从以下位置收集的数据的组合 将使用3a区来确定这些信号是如何由3a区神经元在所有三个运动中表示的 条件。此外，将对3a中跨阵列记录的网络活动进行解码，以确定 运动变量的类别在这个大脑区域进行编码。在目标2中，将使用这些模型化的传入输入 驱动本体感觉转换的神经网络模型，以确定比较哪个模型产生 与3a区最相似的神经反应。总而言之，这两个目标提供了两种互补的方法 以确定本体感觉传入信号在大脑皮层本体感觉表征中的作用。这项工作将 提供重要的基础知识，了解皮质本体感觉是如何从肌肉传入和 对于进一步理解本体感觉在神经运动控制中的作用很重要。