University of Twente Student Theses


Decoding neural control strategies underlying human movement

Gomez Orozco, I.E. (2023) Decoding neural control strategies underlying human movement.

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Abstract:Understanding the neural mechanisms involved in the generation of human movement is fundamental for the development of technologies oriented to motor control and neurorehabilitation. One of the integral concepts for understanding motor control are α-motor neurons (MNs). MNs are excitable cells that control the activation of the skeletal muscle they innervate. Therefore, exploring the neural mechanisms underlying the activation and modulation of MNs is essential for a complete comprehension of human movement. However, the study of these neural mechanisms is constrained by the number of MNs that current approaches can observe. Therefore, in this work, we implement person-specific biophysical MN models, previously developed, that can reproduce neural firing dynamics from the entire pool of MNs. These models represent the neural excitability as a subject-specific constant gain, represented as ΔIF. However, it has been reported that neural excitability changes as a function of the rate of force development (RFD). Hence, this work proposes an approach to demonstrate that ΔIF changes depending on the RFD. For this purpose, first, we examine the firing characteristics at different force levels and RFDs of in vivo decomposed MNs from the tibialis anterior muscle (TA) from four healthy subjects. Second, we compare the neural firing dynamics reproduced by the in silico models, created specifically for every subject and driven by its corresponding constant ΔIF, with the neural firing dynamics observed in vivo at different RFDs. Finally, we propose a methodology to optimize ΔIF that best reproduces the neural firing dynamics at different RFDs, turning a person-specific ΔIF into a function of the RFD. Therefore, the objective of this study is to characterize the neural excitability, represented as ΔIF in the neural biophysical models, as a function of the RFD. This approach can create new opportunities in analyzing and understanding human movement control, enabling the development of new neuro-prosthetic devices.
Item Type:Essay (Master)
Faculty:ET: Engineering Technology
Programme:Biomedical Engineering MSc (66226)
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