A Neural Controller Model Considering the Vestibulospinal Tract in Human Postural Control - Omura, Yuichiro; Kaminishi, Kohei; Chiba, Ryosuke; Takakusaki, Kaoru; Ota, Jun (Frontiers in Computational Neuroscience, 2022)
Humans are able to control their posture in their daily lives. It is important to understand how this is achieved in order to understand the mechanisms that lead to impaired postural control in various diseases. The descending tracts play an important role in controlling posture, particularly the reticulospinal and the vestibulospinal tracts (VST), and there is evidence that the latter is impaired in various diseases. However, the contribution of the VST to human postural control remains unclear, despite extensive research using neuroscientific methods. One reason for this is that the neuroscientific approach limits our understanding of the relationship between an array of sensory information and the muscle outputs. This limitation can be addressed by carrying out studies using computational models, where it is possible to make and validate hypotheses about postural control. However, previous computational models have not considered the VST. In this study, we present a neural controller model that mimics the VST, which was constructed on the basis of physiological data. The computational model is composed of a musculoskeletal model and a neural controller model. The musculoskeletal model has 18 degrees of freedom and 94 muscles, including those of the neck, which are needed for modeling the function of the VST. We used an optimization method to adjust the control parameters for different conditions of muscle tone and with/without the VST. We examined the postural sway for each condition. The validity of the neural controller model was evaluated by comparing the modeled postural control with (1) experimental results in human subjects, and (2) the results of a previous study that used a computational model. It was found that the pattern of results was similar for both. This therefore validated the neural controller model, and we could present the neural controller model that mimics the VST.