Relative contribution of different altered motor unit control to muscle weakness in stroke: a simulation study

Shin, Henry; Suresh, Nina L.; Rymer, William Z.; Hu, Xiaogang

doi:10.1088/1741-2552/aa925d

Cited by 7 publications

(8 citation statements)

References 28 publications

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“…For the EMG-force relation, the force value and surface EMG amplitude were normalized to their maximum levels. Because the variation of the simulation outcomes from different repetitions was very small, as observed in previous studies [19,20] and confirmed in the current study, only the mean value of the 10 repetitions was reported for each simulation condition.…”

Section: Methodssupporting

confidence: 63%

“…To overcome this difficulty, a classic motor neuron pool model, developed by Fuglevand et al [ 15 ], has been widely used to better understand the experimental observations of the force and EMG signals and explore the mechanisms of motor impairment [ 16 – 18 ]. For example, Shin et al [ 19 ] and Zhou et al [ 20 ] have used the model to explore the effect of motor unit control property (recruitment and firing rate) changes on muscle weakness and the EMG-force relation.…”

Section: Introductionmentioning

confidence: 99%

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Model-Based Analysis of Muscle Strength and EMG-Force Relation with respect to Different Patterns of Motor Unit Loss

et al. 2021

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This study presents a model-based sensitivity analysis of the strength of voluntary muscle contraction with respect to different patterns of motor unit loss. A motor unit pool model was implemented including simulation of a motor neuron pool, muscle force, and surface electromyogram (EMG) signals. Three different patterns of motor unit loss were simulated, including (1) motor unit loss restricted to the largest ones, (2) motor unit loss restricted to the smallest ones, and (3) motor unit loss without size restriction. The model outputs including muscle force amplitude, variability, and the resultant EMG-force relation were quantified under two different motor neuron firing strategies. It was found that motor unit loss restricted to the largest ones had the most dominant impact on muscle strength and significantly changed the EMG-force relation, while loss restricted to the smallest motor units had a pronounced effect on force variability. These findings provide valuable insight toward our understanding of the neurophysiological mechanisms underlying experimental observations of muscle strength, force control, and EMG-force relation in both normal and pathological conditions.

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Section: Methodssupporting

confidence: 63%

Section: Introductionmentioning

confidence: 99%

Model-Based Analysis of Muscle Strength and EMG-Force Relation with respect to Different Patterns of Motor Unit Loss

et al. 2021

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“…A computer model approach was used to simulate different situations by varying relevant model parameters which are otherwise difficult to manipulate experimentally. Computational models have been applied as a useful means for understanding electrical and mechanical outputs of a muscle in both healthy and pathological conditions [42][43][44][45][46]. In the present study, different situations regarding the number of IZs and the level of MU synchronization were simulated.…”

Section: Discussionmentioning

confidence: 99%

Neurophysiological Factors Affecting Muscle Innervation Zone Estimation Using Surface EMG: A Simulation Study

Huang

Chen

et al. 2021

Biosensors

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Surface electromyography (EMG) recorded by a linear or 2-dimensional electrode array can be used to estimate the location of muscle innervation zones (IZ). There are various neurophysiological factors that may influence surface EMG and thus potentially compromise muscle IZ estimation. The objective of this study was to evaluate how surface-EMG-based IZ estimation might be affected by different factors, including varying degrees of motor unit (MU) synchronization in the case of single or double IZs. The study was performed by implementing a model simulating surface EMG activity. Three different MU synchronization conditions were simulated, namely no synchronization, medium level synchronization, and complete synchronization analog to M wave. Surface EMG signals recorded by a 2-dimensional electrode array were simulated from a muscle with single and double IZs, respectively. For each situation, the IZ was estimated from surface EMG and compared with the one used in the model for performance evaluation. For the muscle with only one IZ, the estimated IZ location from surface EMG was consistent with the one used in the model for all the three MU synchronization conditions. For the muscle with double IZs, at least one IZ was appropriately estimated from interference surface EMG when there was no MU synchronization. However, the estimated IZ was different from either of the two IZ locations used in the model for the other two MU synchronization conditions. For muscles with a single IZ, MU synchronization has little effect on IZ estimation from electrode array surface EMG. However, caution is required for multiple IZ muscles since MU synchronization might lead to false IZ estimation.

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“…(Febrer-Nafría et al, 2020 , 2021 ) to achieve subject-specific model-based optimization of the device and its control. Then, such simulations might reveal insights into biomechanical effects such as altered recruitment, reduced force production due to atrophy, fatigue effects, or abnormal synergies (Shin et al, 2018 ). However, creating a personalized neuromusculoskeletal model of a patient is still challenging, since the exact underlying neurological problems of a patient are difficult to extract, and simulations cannot include the variability in muscle responses over time.…”

Section: Interfacing With the Peripherymentioning

confidence: 99%

Adaptation Strategies for Personalized Gait Neuroprosthetics

et al. 2021

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Personalization of gait neuroprosthetics is paramount to ensure their efficacy for users, who experience severe limitations in mobility without an assistive device. Our goal is to develop assistive devices that collaborate with and are tailored to their users, while allowing them to use as much of their existing capabilities as possible. Currently, personalization of devices is challenging, and technological advances are required to achieve this goal. Therefore, this paper presents an overview of challenges and research directions regarding an interface with the peripheral nervous system, an interface with the central nervous system, and the requirements of interface computing architectures. The interface should be modular and adaptable, such that it can provide assistance where it is needed. Novel data processing technology should be developed to allow for real-time processing while accounting for signal variations in the human. Personalized biomechanical models and simulation techniques should be developed to predict assisted walking motions and interactions between the user and the device. Furthermore, the advantages of interfacing with both the brain and the spinal cord or the periphery should be further explored. Technological advances of interface computing architecture should focus on learning on the chip to achieve further personalization. Furthermore, energy consumption should be low to allow for longer use of the neuroprosthesis. In-memory processing combined with resistive random access memory is a promising technology for both. This paper discusses the aforementioned aspects to highlight new directions for future research in gait neuroprosthetics.

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Relative contribution of different altered motor unit control to muscle weakness in stroke: a simulation study

Cited by 7 publications

References 28 publications

Model-Based Analysis of Muscle Strength and EMG-Force Relation with respect to Different Patterns of Motor Unit Loss

Model-Based Analysis of Muscle Strength and EMG-Force Relation with respect to Different Patterns of Motor Unit Loss

Neurophysiological Factors Affecting Muscle Innervation Zone Estimation Using Surface EMG: A Simulation Study

Adaptation Strategies for Personalized Gait Neuroprosthetics

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