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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：
8982884
负责人：
Kyle Blum
金额：
$ 4.31万
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/8982884
关键词：
Affect 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 Qualifying Rattus Recording of previous events Research Risk Role Sensory Signal Transduction Source Stretching Sum Tendon structure Testing Time Training Training Programs Triceps Brachii Muscle Uncertainty Universities Washington Weight Work analytical method base body sense chemotherapy clinical application computational neuroscience coping fall risk improved kinematics model building mortality motor control 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).