A history-dependent model of muscle spindle function

Muscle spindles;Mechanotransduction;Proprioception;Sensory encoding

Blum, Kyle ; Ting, Lena H. Biomedical Engineering (Joint GT/Emory Department) Cope, Timothy C. Nichols, T. R. Butera, Robert Campbell, Kenneth S. ; Ting, Lena H.

University:Georgia Institute of Technology

Department:Biomedical Engineering (Joint GT/Emory Department)

关键词: Muscle spindles;    Mechanotransduction;    Proprioception;    Sensory encoding;   

Others  :  https://smartech.gatech.edu/bitstream/1853/61604/1/BLUM-DISSERTATION-2018.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

Proprioception, or the sense of one’s own body, is an essential part of the neural control of movement, as evidenced by the profound deficits in movement abilities after loss of proprioceptive afferent information to the central nervous system. Muscle spindles are primary proprioceptive sense organs embedded within skeletal muscles that that are thought to be sensors of muscle length and velocity. However, the relationship of muscle spindle firing with muscle length and velocity is history-dependent and we do not have a mechanistic understanding of these phenomenon, which are implicated in a range of both healthy and impaired states. Thus, understanding the nature of mechanotransduction in muscle spindles is critical for understanding sensorimotor control. Here, we present the first-ever model capable of history-dependent muscle spindle encoding based on hypothesized muscle contractile mechanisms governing muscle spindle encoding for sensorimotor control. First, we predicted Ia afferent firing rates in response to passive stretch using only recorded force variables, in contrast to previous studies focusing on length-related variables. We showed that muscle force-related variables could parsimoniously explain muscle spindle primary, or Ia, afferent firing features even in history-dependent conditions, when length-related variables could not. Next, we found that changing single parameters in our simple descriptive model could account for not only variability in firing features across different Ia afferents, but also changes to Ia afferent firing in neural impairments. Finally, we built a mechanistic model based on history-dependent muscle cross bridge cycling that was capable of producing many classical yet unexplained features of muscle spindle sensory responses across different movement and activation contexts. Our multi-scale muscle spindle function is the first-ever muscle spindle model capable of history-dependent sensory encoding observed in muscle spindles. Our model could advance sensorimotor control research by providing a model of history-dependent muscle spindle function across healthy and impaired conditions at multiple scales.