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Proprioceptive models for control of movement

用于控制运动的本体感觉模型

基本信息

批准号：
9319334
负责人：
Kyle Blum
金额：
$ 4.4万
依托单位：
GEORGIA INSTITUTE OF TECHNOLOGY
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-08-17 至 2018-08-16
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9319334
关键词：
Aging Animal Model Animals Appearance Behavior Biomechanics Biomedical Engineering Biomimetics Centers for Disease Control and Prevention (U.S.)Chemotherapy-induced peripheral neuropathy Code Complex Data Data Collection Data Set Databases Diabetes Mellitus Disease Elderly Environment Equilibrium Esthesia Face Feedback Felis catus Goals Human Individual Kinetics Laboratories Length Limb structure Measures Mechanics Modeling Morbidity - disease rate Movement Muscle Muscle Fibers Muscle Spindles Nature Nervous system structure Neuraxis Neuromechanics Neurons Neuropathy Peripheral Nervous System Diseases Population Prevalence Proprioceptor Rattus Recording of previous events Reflex action Research Risk Role Sensory Signal Transduction Source Stretching Sum Tendon structure Testing Time Training Training Programs Triceps Brachii Muscle Uncertainty Universities Volition Washington Work analytical method base body sense chemotherapy clinical application computational neuroscience fall risk improved model building mortality motor control negative affect nervous system disorder neural prosthesis neuroregulation oxaliplatin predictive modeling public health relevance relating to nervous system response spinal nerve posterior root young cancer survivor

项目摘要

DESCRIPTION (provided by applicant): Crucial to controlling movements is our bodies' sense of limb state arising from mechanically gated neurons located within the musculotendon, called proprioceptors. Understanding the mechanical encoding of proprioceptors is critical to understanding their roles in sensorimotor behaviors such as balance control. Proprioceptive loss due to aging, diabetes, as well as chemotherapy-induced peripheral neuropathy (CIPN) and other disorders can increase the risk for falls, which is the leading cause of morbidity and mortality in older adults. Neuromechanical models are essential for predicting the impact of sensory loss on movement because proprioceptors are difficult to access and measure from during actual movements. Here, the goal is to develop a parsimonious model of proprioceptive function appropriate for predictive modeling of sensorimotor control in healthy and neuropathic conditions. Specifically, this work aims to directly test what information is encoded in the instantaneous firing rates and aggregate firing activity of proprioceptors in healthy cat (Specific Aim 1) and in healthy and CIPN rat (Specific Aim 2). Preliminary data suggest that group Ia proprioceptors encode information related to musculotendon kinetics (i.e. passive tension and its first time derivative), but not kinematics (i.e. length and velocity) as previously thought. Usng a combination of previously collected and new datasets with simple musculotendon and spiking neuron model, the proposed work will explicitly test hypotheses about the relationship between firing rates and aggregate activity of group Ia, Ib, and II proprioceptors and musculotendon kinetics in anesthetized cat (healthy) and rat (healthy and neuropathic).

描述（由申请人提供）：控制运动的关键是我们身体对肢体状态的感觉，这种感觉来自于位于肌肉肌腱内的机械门控神经元，称为本体感受器。理解本体感受器的机械编码对于理解它们在感觉运动行为（如平衡控制）中的作用至关重要。由于衰老、糖尿病以及化疗诱导的周围神经病变（CIPN）和其他疾病导致的本体感受丧失可增加福尔斯的风险，这是老年人发病率和死亡率的主要原因。神经力学模型对于预测感觉丧失对运动的影响是必不可少的，因为本体感受器在实际运动中难以接近和测量。在这里，我们的目标是开发一个简约的本体感觉功能模型，适合在健康和神经病变的条件下的感觉运动控制的预测建模。具体而言，这项工作旨在直接测试健康猫本体感受器的瞬时放电率和聚集放电活动中编码的信息（具体目的1）和健康和CIPN大鼠（具体目的2）。初步数据表明，Ia组本体感受器编码与肌肉肌腱动力学相关的信息（即被动张力及其一阶导数），但不是以前认为的运动学（即长度和速度）。使用先前收集的和新的数据集与简单的肌肉肌腱和尖峰神经元模型的组合，拟议的工作将明确测试关于麻醉猫（健康）和大鼠（健康和神经病）的Ia，Ib和II组本体感受器的放电率和聚集活性与肌肉肌腱动力学之间的关系的假设。