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Caillet AH, Phillips ATM, Modenese L, Farina D. NeuroMechanics: Electrophysiological and computational methods to accurately estimate the neural drive to muscles in humans in vivo. J Electromyogr Kinesiol 2024; 76:102873. [PMID: 38518426 DOI: 10.1016/j.jelekin.2024.102873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/24/2024] Open
Abstract
The ultimate neural signal for muscle control is the neural drive sent from the spinal cord to muscles. This neural signal comprises the ensemble of action potentials discharged by the active spinal motoneurons, which is transmitted to the innervated muscle fibres to generate forces. Accurately estimating the neural drive to muscles in humans in vivo is challenging since it requires the identification of the activity of a sample of motor units (MUs) that is representative of the active MU population. Current electrophysiological recordings usually fail in this task by identifying small MU samples with over-representation of higher-threshold with respect to lower-threshold MUs. Here, we describe recent advances in electrophysiological methods that allow the identification of more representative samples of greater numbers of MUs than previously possible. This is obtained with large and very dense arrays of electromyographic electrodes. Moreover, recently developed computational methods of data augmentation further extend experimental MU samples to infer the activity of the full MU pool. In conclusion, the combination of new electrode technologies and computational modelling allows for an accurate estimate of the neural drive to muscles and opens new perspectives in the study of the neural control of movement and in neural interfacing.
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Affiliation(s)
| | - Andrew T M Phillips
- Department of Civil and Environmental Engineering, Imperial College London, UK
| | - Luca Modenese
- Graduate School of Biomedical Engineering, University of New South Wales, Sydney, Australia.
| | - Dario Farina
- Department of Bioengineering, Imperial College London, UK.
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Caillet AH, Phillips ATM, Farina D, Modenese L. Motoneuron-driven computational muscle modelling with motor unit resolution and subject-specific musculoskeletal anatomy. PLoS Comput Biol 2023; 19:e1011606. [PMID: 38060619 PMCID: PMC10729998 DOI: 10.1371/journal.pcbi.1011606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 12/19/2023] [Accepted: 10/16/2023] [Indexed: 12/20/2023] Open
Abstract
The computational simulation of human voluntary muscle contraction is possible with EMG-driven Hill-type models of whole muscles. Despite impactful applications in numerous fields, the neuromechanical information and the physiological accuracy such models provide remain limited because of multiscale simplifications that limit comprehensive description of muscle internal dynamics during contraction. We addressed this limitation by developing a novel motoneuron-driven neuromuscular model, that describes the force-generating dynamics of a population of individual motor units, each of which was described with a Hill-type actuator and controlled by a dedicated experimentally derived motoneuronal control. In forward simulation of human voluntary muscle contraction, the model transforms a vector of motoneuron spike trains decoded from high-density EMG signals into a vector of motor unit forces that sum into the predicted whole muscle force. The motoneuronal control provides comprehensive and separate descriptions of the dynamics of motor unit recruitment and discharge and decodes the subject's intention. The neuromuscular model is subject-specific, muscle-specific, includes an advanced and physiological description of motor unit activation dynamics, and is validated against an experimental muscle force. Accurate force predictions were obtained when the vector of experimental neural controls was representative of the discharge activity of the complete motor unit pool. This was achieved with large and dense grids of EMG electrodes during medium-force contractions or with computational methods that physiologically estimate the discharge activity of the motor units that were not identified experimentally. This neuromuscular model advances the state-of-the-art of neuromuscular modelling, bringing together the fields of motor control and musculoskeletal modelling, and finding applications in neuromuscular control and human-machine interfacing research.
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Affiliation(s)
- Arnault H. Caillet
- Department of Civil and Environmental Engineering, Imperial College London, London, United Kingdom
- Department of Bioengineering, Imperial College London, London, United Kingdom
| | - Andrew T. M. Phillips
- Department of Civil and Environmental Engineering, Imperial College London, London, United Kingdom
| | - Dario Farina
- Department of Bioengineering, Imperial College London, London, United Kingdom
| | - Luca Modenese
- Department of Civil and Environmental Engineering, Imperial College London, London, United Kingdom
- Graduate School of Biomedical Engineering, University of New South Wales, Sydney, Australia
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Gogeascoechea A, Ornelas-Kobayashi R, Yavuz US, Sartori M. Characterization of Motor Unit Firing and Twitch Properties for Decoding Musculoskeletal Force in the Human Ankle Joint In Vivo. IEEE Trans Neural Syst Rehabil Eng 2023; 31:4040-4050. [PMID: 37756177 DOI: 10.1109/tnsre.2023.3319959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/29/2023]
Abstract
Understanding how motor units (MUs) contribute to skeletal mechanical force is crucial for unraveling the underlying mechanism of human movement. Alterations in MU firing, contractile and force-generating properties emerge in response to physical training, aging or injury. However, how changes in MU firing and twitch properties dictate skeletal muscle force generation in healthy and impaired individuals remains an open question. In this work, we present a MU-specific approach to identify firing and twitch properties of MU samples and employ them to decode musculoskeletal function in vivo. First, MU firing events were decomposed offline from high-density electromyography (HD-EMG) of six lower leg muscles involved in ankle plantar-dorsi flexion. We characterized their twitch responses based on the statistical distributions of their firing properties and employed them to compute MU-specific activation dynamics. Subsequently, we decoded ankle joint moments by linking our framework to a subject-specific musculoskeletal model. We validated our approach at different ankle positions and levels of activation and compared it with traditional EMG-driven models. Our proposed MU-specific formulation achieves higher generalization across conditions than the EMG-driven models, with significantly lower coefficients of variation in torque predictions. Furthermore, our approach shows distinct neural strategies across a large repertoire of contractile conditions in different muscles. Our proposed approach may open new avenues for characterizing the relationship between MU firing and twitch properties and their influence on force capacity. This can facilitate the development of targeted rehabilitation strategies tailored to individuals with specific neuromuscular conditions.
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Bersani A, Davico G, Viceconti M. Modeling Human Suboptimal Control: A Review. J Appl Biomech 2023; 39:294-303. [PMID: 37586711 DOI: 10.1123/jab.2023-0015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 07/03/2023] [Accepted: 07/03/2023] [Indexed: 08/18/2023]
Abstract
This review paper provides an overview of the approaches to model neuromuscular control, focusing on methods to identify nonoptimal control strategies typical of populations with neuromuscular disorders or children. Where possible, the authors tightened the description of the methods to the mechanisms behind the underlying biomechanical and physiological rationale. They start by describing the first and most simplified approach, the reductionist approach, which splits the role of the nervous and musculoskeletal systems. Static optimization and dynamic optimization methods and electromyography-based approaches are summarized to highlight their limitations and understand (the need for) their developments over time. Then, the authors look at the more recent stochastic approach, introduced to explore the space of plausible neural solutions, thus implementing the uncontrolled manifold theory, according to which the central nervous system only controls specific motions and tasks to limit energy consumption while allowing for some degree of adaptability to perturbations. Finally, they explore the literature covering the explicit modeling of the coupling between the nervous system (acting as controller) and the musculoskeletal system (the actuator), which may be employed to overcome the split characterizing the reductionist approach.
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Affiliation(s)
- Alex Bersani
- Medical Technology Lab, IRCCS Istituto Ortopedico Rizzoli, Bologna,Italy
- Department of Industrial Engineering, Alma Mater Studiorum, University of Bologna, Bologna,Italy
| | - Giorgio Davico
- Medical Technology Lab, IRCCS Istituto Ortopedico Rizzoli, Bologna,Italy
- Department of Industrial Engineering, Alma Mater Studiorum, University of Bologna, Bologna,Italy
| | - Marco Viceconti
- Medical Technology Lab, IRCCS Istituto Ortopedico Rizzoli, Bologna,Italy
- Department of Industrial Engineering, Alma Mater Studiorum, University of Bologna, Bologna,Italy
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Lloyd DG, Jonkers I, Delp SL, Modenese L. The History and Future of Neuromusculoskeletal Biomechanics. J Appl Biomech 2023; 39:273-283. [PMID: 37751904 DOI: 10.1123/jab.2023-0165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 07/04/2023] [Accepted: 07/05/2023] [Indexed: 09/28/2023]
Abstract
The Executive Council of the International Society of Biomechanics has initiated and overseen the commemorations of the Society's 50th Anniversary in 2023. This included multiple series of lectures at the ninth World Congress of Biomechanics in 2022 and XXIXth Congress of the International Society of Biomechanics in 2023, all linked to special issues of International Society of Biomechanics' affiliated journals. This special issue of the Journal of Applied Biomechanics is dedicated to the biomechanics of the neuromusculoskeletal system. The reader is encouraged to explore this special issue which comprises 6 papers exploring the current state-of the-art, and future directions and roles for neuromusculoskeletal biomechanics. This editorial presents a very brief history of the science of the neuromusculoskeletal system's 4 main components: the central nervous system, musculotendon units, the musculoskeletal system, and joints, and how they biomechanically integrate to enable an understanding of the generation and control of human movement. This also entails a quick exploration of contemporary neuromusculoskeletal biomechanics and its future with new fields of application.
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Affiliation(s)
- David G Lloyd
- Griffith Centre of Biomedical and Rehabilitation Engineering, Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, School of Health Science and Social Work, Griffith University, Gold Coast, QLD, Australia
| | - Ilse Jonkers
- Institute of Physics-Based Modeling for in Silico Health, Human Movement Science Department, KU Leuven, Leuven, Belgium
| | - Scott L Delp
- Bioengineering, Mechanical Engineering and Orthopedic Surgery, and Wu Tsai Human Performance Alliance at Stanford, Stanford University, Stanford, CA, USA
| | - Luca Modenese
- Graduate School of Biomedical Engineering, University of New South Wales, Sydney, NSW, Australia
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Lubel E, Sgambato BG, Rohlen R, Ibanez J, Barsakcioglu DY, Tang MX, Farina D. Non-Linearity in Motor Unit Velocity Twitch Dynamics: Implications for Ultrafast Ultrasound Source Separation. IEEE Trans Neural Syst Rehabil Eng 2023; 31:3699-3710. [PMID: 37703141 DOI: 10.1109/tnsre.2023.3315146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/15/2023]
Abstract
Ultrasound (US) muscle image series can be used for peripheral human-machine interfacing based on global features, or even on the decomposition of US images into the contributions of individual motor units (MUs). With respect to state-of-the-art surface electromyography (sEMG), US provides higher spatial resolution and deeper penetration depth. However, the accuracy of current methods for direct US decomposition, even at low forces, is relatively poor. These methods are based on linear mathematical models of the contributions of MUs to US images. Here, we test the hypothesis of linearity by comparing the average velocity twitch profiles of MUs when varying the number of other concomitantly active units. We observe that the velocity twitch profile has a decreasing peak-to-peak amplitude when tracking the same target motor unit at progressively increasing contraction force levels, thus with an increasing number of concomitantly active units. This observation indicates non-linear factors in the generation model. Furthermore, we directly studied the impact of one MU on a neighboring MU, finding that the effect of one source on the other is not symmetrical and may be related to unit size. We conclude that a linear approximation is partly limiting the decomposition methods to decompose full velocity twitch trains from velocity images, highlighting the need for more advanced models and methods for US decomposition than those currently employed.
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Caillet AH, Avrillon S, Kundu A, Yu T, Phillips ATM, Modenese L, Farina D. Larger and Denser: An Optimal Design for Surface Grids of EMG Electrodes to Identify Greater and More Representative Samples of Motor Units. eNeuro 2023; 10:ENEURO.0064-23.2023. [PMID: 37657923 PMCID: PMC10500983 DOI: 10.1523/eneuro.0064-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 08/02/2023] [Accepted: 08/03/2023] [Indexed: 09/03/2023] Open
Abstract
The spinal motor neurons are the only neural cells whose individual activity can be noninvasively identified. This is usually done using grids of surface electromyographic (EMG) electrodes and source separation algorithms; an approach called EMG decomposition. In this study, we combined computational and experimental analyses to assess how the design parameters of grids of electrodes influence the number and the properties of the identified motor units. We first computed the percentage of motor units that could be theoretically discriminated within a pool of 200 simulated motor units when decomposing EMG signals recorded with grids of various sizes and interelectrode distances (IEDs). Increasing the density, the number of electrodes, and the size of the grids, increased the number of motor units that our decomposition algorithm could theoretically discriminate, i.e., up to 83.5% of the simulated pool (range across conditions: 30.5-83.5%). We then identified motor units from experimental EMG signals recorded in six participants with grids of various sizes (range: 2-36 cm2) and IED (range: 4-16 mm). The configuration with the largest number of electrodes and the shortest IED maximized the number of identified motor units (56 ± 14; range: 39-79) and the percentage of early recruited motor units within these samples (29 ± 14%). Finally, the number of identified motor units further increased with a prototyped grid of 256 electrodes and an IED of 2 mm. Taken together, our results showed that larger and denser surface grids of electrodes allow to identify a more representative pool of motor units than currently reported in experimental studies.
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Affiliation(s)
- Arnault H Caillet
- Department of Bioengineering, Imperial College London, London SW7 2AZ, United Kingdom
- Department of Civil and Environmental Engineering, Imperial College London, London SW7 2AZ, United Kingdom
| | - Simon Avrillon
- Department of Bioengineering, Imperial College London, London SW7 2AZ, United Kingdom
| | - Aritra Kundu
- Department of Bioengineering, Imperial College London, London SW7 2AZ, United Kingdom
| | - Tianyi Yu
- Department of Bioengineering, Imperial College London, London SW7 2AZ, United Kingdom
| | - Andrew T M Phillips
- Department of Civil and Environmental Engineering, Imperial College London, London SW7 2AZ, United Kingdom
| | - Luca Modenese
- Graduate School of Biomedical Engineering, University of New South Wales, Sydney, New South Wales 1466, Australia
| | - Dario Farina
- Department of Bioengineering, Imperial College London, London SW7 2AZ, United Kingdom
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