Models of recruitment and rate coding organization in motor-unit pools.
Abstract
1. Isometric muscle force and the surface electromyogram (EMG) were simulated from a model that predicted recruitment and firing times in a pool of 120 motor units under different levels of excitatory drive. The EMG-force relationships that emerged from simulations using various schedules of recruitment and rate coding were compared with those observed experimentally to determine which of the modeled schemes were plausible representations of the actual organization in motor-unit pools. 2. The model was comprised of three elements: a motoneuron model, a motor-unit force model, and a model of the surface EMG. Input to the neuron model was an excitatory drive function representing the net synaptic input to motoneurons during voluntary muscle contractions. Recruitment thresholds were assigned such that many motoneurons had low thresholds and relatively few neurons had high thresholds. Motoneuron firing rate increased as a linear function of excitatory drive between recruitment threshold and peak firing rate levels. The sequence of discharge times for each motoneuron was simulated as a random renewal process. 3. Motor-unit twitch force was estimated as an impulse response of a critically damped, second-order system. Twitch amplitudes were assigned according to rank in the recruitment order, and twitch contraction times were inversely related to twitch amplitude. Nonlinear force-firing rate behavior was simulated by varying motor-unit force gain as a function of the instantaneous firing rate and the contraction time of the unit. The total force exerted by the muscle was computed as the sum of the motor-unit forces. 4. Motor-unit action potentials were simulated on the basis of estimates of the number and location of motor-unit muscle fibers and the propagation velocity of the fiber action potentials. The number of fibers innervated by each unit was assumed to be directly proportional to the twitch force. The area of muscle encompassing unit fibers was proportional to the number of fibers innervated, and the location of motor-unit territories were randomly assigned within the muscle cross section. Action-potential propagation velocities were estimated from an inverse function of contraction time. The train of discharge times predicted from the motoneuron model determined the occurrence of each motor-unit action potential. The surface EMG was synthesized as the sum of all motor-unit action-potential trains. 5. Two recruitment conditions were tested: narrow (limit of recruitment < 50% maximum excitation) and broad recruitment range conditions (limit of recruitment > 70% maximum excitation).(ABSTRACT TRUNCATED AT 400 WORDS)
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Firing rates were excluded from further analysis when the coefficient of variation in mean firing rate was greater than 30% or when the number of firings was lower than two.
“Firing rates were excluded from further analysis when the coefficient of variation in mean firing rate was greater than 30% or when the number of firings was lower than two, i.e., one firing (Fuglevand et al., 1993).”
From Effect of resistance training and fish protein intake on motor unit firing pattern and motor function of elderly (2018) · cites this paper as Fuglevand et al., 1993
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“For that purpose, mechanical properties of MUs obtained using the spike-triggered averaging method recorded in the tibialis anterior (Van Cutsem et al. 1997) were inserted into the model developed by Fuglevand et al. (1993).”
MethodVan Cutsem, M., Duchateau, J., & Hainaut, K. (1998) · The Journal of Physiology · doi:10.1111/j.1469-7793.1998.295by.x · Cited by 777 · see citing works
“The more segmented aspect of the EMG activity during these contractions could be explained by a tendency of motor units to pulse at a similar frequency after training (Fuglevand et al. 1993).”
BackgroundNeuronal variability: noise or part of the signal?
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“In this simulation, motor units were recruited and derecruited according to the size principle (44) during each ramp (Fig.”
Sources of Signal-Dependent Noise During Isometric Force Production
Jones, K. E., Hamilton, A. F. D. C., & Wolpert, D. M. (2002) · Journal of Neurophysiology · doi:10.1152/jn.2002.88.3.1533 · Cited by 556 · see citing works
“In the standard model, ISI variability was modeled using a Gaussian distribution and a constant CV of 0.2 (Fuglevand et al. 1993).”
BackgroundMethodResultInfluentialMechanisms that contribute to differences in motor performance between young and old adults
Enoka, R. M., Christou, E. A., Hunter, S. K., Kornatz, K. W., Semmler, J. G., Taylor, A. M., & Tracy, B. L. (2002) · Journal of Electromyography and Kinesiology · doi:10.1016/s1050-6411(02)00084-6 · Cited by 566 · see citing works
“The computer model comprised 120 motor neurons with systematic variations in recruitment threshold, range of discharge rates, innervation ratio, conduction velocity of sarcolemmal action potentials, motor unit territory, amplitude and duration of the twitch force, and the specific shape of the force-frequency relation…”
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“To quantify the outputs of the model, we postulated the existence of direct relationships between the number of recruited axons and the overall muscle activation (Fuglevand et al., 1993).”
MethodInfluentialJiang, N., Englehart, K. B., & Parker, P. A. (2008) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2008.2007967 · Cited by 453 · see citing works
“plemented using the exponential threshold method described in [35].”
MethodMoritz, C. T., Barry, B. K., Pascoe, M. A., & Enoka, R. M. (2004) · Journal of Neurophysiology · doi:10.1152/jn.01122.2004 · Cited by 430 · see citing works
“This trend was similar to that observed in other studies on the first dorsal interosseus (Burnett et al. 2000; Galganski et al. 1993; Laidlaw et al. 1999, 2000) and quadriceps femoris (Christou et al. 2002; Tracy and Enoka 2002) muscles.”
ResultHeckman, C. J., & Enoka, R. M. (2012) · Comprehensive Physiology · doi:10.1002/cphy.c100087 · Cited by 414 · see citing works
Holobar, A., Minetto, M. A., & Farina, D. (2014) · Journal of Neural Engineering · doi:10.1088/1741-2560/11/1/016008 · Cited by 400 · see citing works
“The distribution of recruitment thresholds for the motor neurons was modelled with an exponential function with many low-threshold neurons and progressively fewer high-threshold neurons, as described by Fuglevand et al (1993).”
MethodInfluentialUnilateral intramuscular injections of acidic saline produce a bilateral, long-lasting hyperalgesia
Sluka, K. A., Kalra, A., & Moore, S. A. (2001) · Muscle & Nerve · doi:10.1002/1097-4598(200101)24:1 · Cited by 502 · see citing works
Self-citationInfluence of amplitude cancellation on the simulated surface electromyogram
Keenan, K. G., Farina, D., Maluf, K. S., Merletti, R., & Enoka, R. M. (2004) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00894.2004 · Cited by 386 · see citing works
“It has been suggested that variation in recruitment range may contribute to the different EMGforce relations observed experimentally (3, 21).”
InfluentialCrosstalk in surface electromyography: Theoretical and practical estimates
Winter, D. A., Fuglevand, A. J., & Archer, S. (1994) · Journal of Electromyography and Kinesiology · doi:10.1016/1050-6411(94)90023-x · Cited by 390 · see citing works
Self-citationDecoding the neural drive to muscles from the surface electromyogram
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“This finding indicates that the amplitude of the surface enerated by motor units were simulated with the planar volume conductor model by Fuglevand et al. (1993).”
MethodMethodological aspects of SEMG recordings for force estimation – A tutorial and review
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“Because of the size principle, the increase in force with additional MU recruitment is predicted by modeling to be more than proportional (Fuglevand et al., 1993).”
BackgroundResultInfluentialTutorial: Analysis of motor unit discharge characteristics from high-density surface EMG signals
Vecchio, A. D., Holobar, A., Falla, D., Felici, F., Enoka, R. M., & Farina, D. (2020) · Journal of Electromyography and Kinesiology · doi:10.1016/j.jelekin.2020.102426 · Cited by 373 · see citing works
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“Simulation studies appear to support an association between motor neuron discharge rates and rapid force production, although there are no direct experimental observations for this association (Fuglevand et al. 1993; Harwood & Rice, 2012).”
ResultMotor unit firing rates and contractile properties in tibialis anterior of young and old men
Connelly, D. M., Rice, C. L., Roos, M. R., & Vandervoort, A. A. (1999) · Journal of Applied Physiology · doi:10.1152/jappl.1999.87.2.843 · Cited by 340 · see citing works
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Negro, F., Holobar, A., & Farina, D. (2009) · The Journal of Physiology · doi:10.1113/jphysiol.2009.178509 · Cited by 336 · see citing works
“For example, when using as a window the function proposed by Fuglevand et al. (1993) for describing the motor unit twitch force (with contraction time = 90 ms) instead of the Hann window, the association between the FCC and force signal increased to 71.8 ± 13.1% (average over all contraction…”
BackgroundMethodInfluentialInterpretation of the Surface Electromyogram in Dynamic Contractions
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“Moreover, the relation between amplitude and excitation level depends on the pattern of motor unit activation (10), electrode location in relation to innervation zones and tendon regions, and crosstalk (Fig.”
BackgroundEstimating motor unit discharge patterns from high-density surface electromyogram.
Holobar, A., Farina, D., Gazzoni, M., Merletti, R., & Zazula, D. (2009) · Clinical Neurophysiology · doi:10.1016/j.clinph.2008.10.160 · Cited by 318 · see citing works
“The discharge rates at the force peak (10% MVC) were significantly larger than at recruitment and derecruitment and were inversely correlated to recruitment threshold (De Luca et al., 1982; Fuglevand et al., 1993; Hogrel, 2003; Monster and Chan, 1977).”
BackgroundMethodResultInfluentialMotor unit recruitment strategies investigated by surface EMG variables
Farina, D., Fosci, M., & Merletti, R. (2002) · Journal of Applied Physiology · doi:10.1152/jappl.2002.92.1.235 · Cited by 320 · see citing works
The scaling of motor noise with muscle strength and motor unit number in humans
Hamilton, A. F. D. C., Jones, K. E., & Wolpert, D. M. (2004) · Experimental Brain Research · doi:10.1007/s00221-004-1856-7 · Cited by 285 · see citing works
“The relationship between the number of motor units in a muscle and muscle noise was examined using a previously tested model of force generation by a single muscle under isometric conditions (Fuglevand et al. 1993; Jones et al. 2002).”
BackgroundMethodInfluentialA fast and reliable technique for muscle activity detection from surface EMG signals
Merlo, A., Farina, D., & Merletti, R. (2003) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2003.808829 · Cited by 287 · see citing works
A novel approach for precise simulation of the EMG signal detected by surface electrodes
Farina, D., & Merletti, R. (2001) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.923782 · Cited by 283 · see citing works
“8 and 9 show examples of simulated surface EMG signals detected during electrically elicited and voluntary contractions [13], [29], [41].”
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Motoneuron excitability: The importance of neuromodulatory inputs
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“Both modeling studies (Fuglevand et al., 1993; Heckman, 1994) and reflex experiments in human subjects (see Cathers et al. (2004) for a review) indicate that this ‘‘recruitment nonlinearity” mechanism is effective only up to about 30–50% of maximum voluntary force, as most of recruitment is…”
InfluentialHerbert, R., & Gandevia, S. C. (1999) · Journal of Neurophysiology · doi:10.1152/jn.1999.82.5.2271 · Cited by 269 · see citing works
“Model structure and modeling strategy We modified the model of a motoneuron pool described in detail by Fuglevand et al. (1993) (see also Fuglevand 1989; Fuglevand and Bigland-Ritchie 1993) so that it could simulate interpolated twitches of the adductor pollicis muscle.”
BackgroundMethodInfluentialQuadriceps muscle strength, contractile properties, and motor unit firing rates in young and old men
Roos, M. R., Rice, C. L., Connelly, D. M., & Vandervoort, A. A. (1999) · Muscle & Nerve · doi:10.1002/(sici)1097-4598(199908)22:8 · Cited by 269 · see citing works
The effective neural drive to muscles is the common synaptic input to motor neurons
Farina, D., Negro, F., & Dideriksen, J. L. (2014) · The Journal of Physiology · doi:10.1113/jphysiol.2014.273581 · Cited by 264 · see citing works
“The motor neuron parameters were the same as used by Cisi & Kohn (2008; their Table 2) and selected according to an exponential distribution over the pool of motor neurons (Fuglevand et al. 1993).”
MethodA Novel Framework Based on FastICA for High Density Surface EMG Decomposition
Chen, M., & Zhou, P. (2015) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2015.2412038 · Cited by 262 · see citing works
“In [46], different motor unit discharge patterns were simulated (including those following or violating”
MethodInfluentialOlder adults are less steady during submaximal isometric contractions with the knee extensor muscles
Tracy, B. L., & Enoka, R. M. (2002) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00954.2001 · Cited by 254 · see citing works
“Because the relative contribution of single motor units to net muscle force declines as the number of active motor units increases (13), however, it is unlikely that differences in the steadiness of limb muscles are attributable to the behavior of single motor units.”
BackgroundChallenges and New Approaches to Proving the Existence of Muscle Synergies of Neural Origin
Kutch, J. J., & Valero‐Cuevas, F. J. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002434 · Cited by 252 · see citing works
“We do not believe that such a model is currently feasible, largely because existing detailed mechanistic models of EMG apply to isometric contractions only [30] and often fail to realistically replicate fundamental features of EMG [31].”
BackgroundHuman motor unit recordings: Origins and insight into the integrated motor system
Duchateau, J., & Enoka, R. M. (2011) · Brain Research · doi:10.1016/j.brainres.2011.06.011 · Cited by 238 · see citing works
Blind source identification from the multichannel surface electromyogram
Holobar, A., & Farina, D. (2014) · Physiological Measurement · doi:10.1088/0967-3334/35/7/r143 · Cited by 238 · see citing works
Hierarchical control of motor units in voluntary contractions
Luca, C. J. D., & Contessa, P. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00961.2010 · Cited by 235 · see citing works
“For the FDI muscle, the number of motor units (n) is 120 (Feinstein et al. 1955), the range of recruitment (RR) is 0 to 0.67 (De Luca and Hostage 2010), and, according to Fuglevand et al. (1993), the recruitment threshold distribution is exponential, as shown in Fig.”
BackgroundMethodInfluentialElectromyographic and Kinematic Analysis of Cutting Maneuvers
Colby, S. M., FRANCISCO, A. C., Yu, B., Kirkendall, D. T., Finch, M., & Garrett, W. E. J. (2000) · The American Journal of Sports Medicine · doi:10.1177/03635465000280021501 · Cited by 233 · see citing works
“The relationship between force and EMG signals can be nearly linear under isometric conditions.(3,17,20) Our data were normalized to a maximum voluntary contraction on a Cybex dynamometer at three different angles.”
MethodMultiple Features of Motor-Unit Activity Influence Force Fluctuations During Isometric Contractions
Taylor, A. M., Christou, E. A., & Enoka, R. M. (2003) · Journal of Neurophysiology · doi:10.1152/jn.00056.2003 · Cited by 233 · see citing works
“Because the force contributed by a single motor unit to the net muscle force declines as the number of active motor units increases (Fuglevand et al. 1993), a muscle with fewer motor units might exhibit greater force fluctuations.”
BackgroundMethodInfluentialNeurophysiological methods for studies of the motor system in freely moving human subjects
Capaday, C. (1997) · Journal of Neuroscience Methods · doi:10.1016/s0165-0270(97)02250-4 · Cited by 230 · see citing works
“How the number of active motoneurons, their discharge rate and the degree of their synchronization is related to the mean value of the rectified EMG is not known, but computer models bearing on this issue are currently being developed (Fuglevand et al., 1993)”
BackgroundInfluentialPotvin, J. R., & Brown, S. H. M. (2003) · Journal of Electromyography and Kinesiology · doi:10.1016/j.jelekin.2003.10.005 · Cited by 228 · see citing works
“[18] modeled motor-unit pool organization and concluded that these differences may be due more to firing rate behavior differences between muscles.”
BackgroundMethodRate Coding and the Control of Muscle ForcePreprint
Enoka, R. M., & Duchateau, J. (2017) · Cold Spring Harbor Perspectives in Medicine · doi:10.1101/cshperspect.a029702 · Cited by 234 · see citing works
“5A) (Fuglevand et al. 1993).”
BackgroundMethodInfluentialPhysical and cognitive consequences of fatigue: A review
Abd-Elfattah, H. M., Abdelazeim, F. H., & Elshennawy, S. (2015) · Journal of Advanced Research · doi:10.1016/j.jare.2015.01.011 · Cited by 229 · see citing works
“Initially the motoneurons fire repetitively, but with time, some motoneurons slow their firing rate and others stop [37,38].”
BackgroundOrderly recruitment of motor units under optical control in vivo
Llewellyn, M. E., Thompson, K. R., Deisseroth, K., & Delp, S. L. (2010) · Nature Medicine · doi:10.1038/nm.2228 · Cited by 217 · see citing works
Effect of Experimental Muscle Pain on Motor Unit Firing Rate and Conduction Velocity
Farina, D., Arendt‐Nielsen, L., Merletti, R., & Graven‐Nielsen, T. (2004) · Journal of Neurophysiology · doi:10.1152/jn.00620.2003 · Cited by 215 · see citing works
Principles of Motor Unit Physiology Evolve With Advances in Technology
Farina, D., Negro, F., Muceli, S., & Enoka, R. M. (2016) · Physiology · doi:10.1152/physiol.00040.2015 · Cited by 207 · see citing works
“Because the recruitment of one motor unit adds relatively little to the force being exerted by a muscle (43), even weak contractions require the concurrent activation of many motor units.”
BackgroundSurface EMG models: properties and applications
Stegeman, D. F., Blok, J. H., Hermens, H. J., et al. (2000) · Journal of Electromyography and Kinesiology · doi:10.1016/s1050-6411(00)00023-7 · Cited by 210 · see citing works
Farina, D., Fattorini, L., Felici, F., et al. (2002) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00314.2002 · Cited by 203 · see citing works
“The number of MU fibers was uniformly distributed between 50 and 450, and the MU fiber density was 20 fibers/mm2 (19).”
BackgroundMethodEffect of joint angle on EMG variables in leg and thigh muscles
Farina, D., Merletti, R., Nazzaro, M., & Caruso, I. (2001) · IEEE Engineering in Medicine and Biology Magazine · doi:10.1109/51.982277 · Cited by 202 · see citing works
“The number of fibers of each MU varied between 50 and 450, and the MU fiber density was 20 fibers/mm(2) [20, 21].”
A Dynamical Model of Muscle Activation, Fatigue, and Recovery
Liu, J. Z., Brown, R. W., & Yue, G. H. (2002) · Biophysical Journal · doi:10.1016/s0006-3495(02)75580-x · Cited by 193 · see citing works
“Because the discharge rate is typically high (e.g., 100 Hz) in the case of an MVC task (Fuglevand et al., 1993; Herbert and Gandevia, 1999) and the number of motor units is supposed to be sufficiently large, the assumption of a continuous voluntary drive (B) is adequate for our force data fitting.”
BackgroundMethodInfluentialTracy, B. L., Maluf, K. S., Stephenson, J. L., Hunter, S. K., & Enoka, R. M. (2005) · Muscle & Nerve · doi:10.1002/mus.20392 · Cited by 194 · see citing works
Day, S. J., & Hulliger, M. (2001) · Journal of Neurophysiology · doi:10.1152/jn.2001.86.5.2144 · Cited by 192 · see citing works
“…LeFever and De Luca 1982; Mambrito and De Luca 1984; McGill et al. 1985; ), or when analyzing signal properties in computer simulations or mathe- atical models of EMG (Day et al. 1996; Fuglevand et al. 1993; Libkind 1968, 1969; Milner-Brown and Stein 1975; Person and Libkind 1970; Yao et al. 2000).”
MethodDideriksen, J. L., Negro, F., Enoka, R. M., & Farina, D. (2012) · Journal of Neurophysiology · doi:10.1152/jn.00938.2011 · Cited by 185 · see citing works
“2010) that represented the motor unit recruitment and rate coding characteristics (Fuglevand et al. 1993) of the first dorsal interosseus muscle (FDI; index finger abductor) (Barry et al.”
BackgroundMadeleine, P., Bajaj, P., Søgaard, K., & Arendt-Nielsen, L. (2001) · Journal of Electromyography & Kinesiology · doi:10.1016/s1050-6411(00)00044-4 · Cited by 206 · see citing works
Determinants of physiologic tremor in a large normal population
Raethjen, J., Pawlas, F., Lindemann, M., Wenzelburger, R., & Deuschl, G. (2000) · Clinical Neurophysiology · doi:10.1016/s1388-2457(00)00384-9 · Cited by 184 · see citing works
Gazzoni, M., Farina, D., & Merletti, R. (2004) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2004.01.002 · Cited by 180 · see citing works
Force Steadiness: From Motor Units to Voluntary Actions
Enoka, R. M., & Farina, D. (2021) · Physiology · doi:10.1152/physiol.00027.2020 · Cited by 178 · see citing works
Neural control of shortening and lengthening contractions: influence of task constraints
Duchateau, J., & Enoka, R. M. (2008) · The Journal of Physiology · doi:10.1113/jphysiol.2008.160747 · Cited by 175 · see citing works
The Physiological Control of Motoneuron Activity
Binder, M. D., Heckman, C. J., & Powers, R. K. (1996) · Comprehensive physiology · doi:10.1002/cphy.cp120101 · Cited by 174 · see citing works
Fuglevand, A. J., Macefield, V. G., & Bigland-Ritchie, B. (1999) · Journal of Neurophysiology · doi:10.1152/jn.1999.81.4.1718 · Cited by 161 · see citing works
Self-citationRunning Economy from a Muscle Energetics Perspective
Fletcher, J. R., & MacIntosh, B. R. (2017) · Frontiers in Physiology · doi:10.3389/fphys.2017.00433 · Cited by 158 · see citing works
“…of muscle activation, a combination of motor unit recruitment and rate coding or increased frequency of activation of already active motor units (Fuglevand et al., 1993), dictates the energy cost since the rate of energy use depends on the number of fibers activated, the cross-bridge turnover…”
BackgroundCoactivation of the antagonist muscle does not covary with steadiness in old adults
Burnett, R. A., Laidlaw, D. H., & Enoka, R. M. (2000) · Journal of Applied Physiology · doi:10.1152/jappl.2000.89.1.61 · Cited by 157 · see citing works
“This effect of the unfused tetani on the force fluctuations is greatest at low forces, when the relative contribution of a motor unit to the net force is greatest, and is greater in muscles with a narrow recruitment range (17), such as occurs with the decline in motor unit number with age (7, 11).”
BackgroundRobust and accurate decoding of motoneuron behaviour and prediction of the resulting force output
Thompson, C. K., Negro, F., Johnson, M. D., Holmes, M. R., McPherson, L. M., Powers, R. K., Farina, D., & Heckman, C. J. (2018) · The Journal of Physiology · doi:10.1113/jp276153 · Cited by 149 · see citing works
“…CSTwiththeimpulseresponseofacriticallydampened, second-ordersystem f ( t ), which has been used extensively to model the motor unit twitch force (Fuglevand et al. 1993): Using this equation, a brute force optimization procedure was used to optimally scale both P , the peak twitch force, from…”
MethodDetecting the Unique Representation of Motor-Unit Action Potentials in the Surface Electromyogram
Farina, D., Negro, F., Gazzoni, M., & Enoka, R. M. (2008) · Journal of Neurophysiology · doi:10.1152/jn.90219.2008 · Cited by 150 · see citing works
“1992), with an exponential distribution across the population (Fuglevand et al. 1993; Kernell, 1992).”
BackgroundExcitatory drive to the alpha‐motoneuron pool during a fatiguing submaximal contraction in man.
Löscher, W. N., Cresswell, A. G., & Thorstensson, A. (1996) · The Journal of Physiology · doi:10.1113/jphysiol.1996.sp021214 · Cited by 149 · see citing works
A motor unit-based model of muscle fatigue
Potvin, J. R., & Fuglevand, A. J. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005581 · Cited by 145 · see citing works
Self-citationReal-Time Motor Unit Identification From High-Density Surface EMG
Glaser, V., Holobar, A., & Zazula, D. (2013) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2013.2247631 · Cited by 143 · see citing works
“Altogether, five different realizations of a muscle with elliptical cross-section (25 (transversal) 16 (depth) mm) were simulated using planar volume conductor model [5] and the MU recruitment model [10].”
MethodBarry, B. K., Pascoe, M. A., Jesunathadas, M., & Enoka, R. M. (2007) · Journal of Neurophysiology · doi:10.1152/jn.01280.2006 · Cited by 144 · see citing works
Enoka, R. M., & Duchateau, J. (2015) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00280.2015 · Cited by 140 · see citing works
“Nonetheless, the amplitude of a surface EMG signal can provide a useful approximation of the amplitude component of the neural drive to muscle during some controlled conditions (8, 18).”
BackgroundIdentification of common synaptic inputs to motor neurons from the rectified electromyogram
Farina, D., Negro, F., & Jiang, N. (2013) · The Journal of Physiology · doi:10.1113/jphysiol.2012.246082 · Cited by 133 · see citing works
“The motor neuron parameters were the same as used by Cisi & Kohn (2008; their Table 2) and selected according to an exponential distribution over the pool of motor neurons (Fuglevand et al. 1993).”
MethodElectromyography in the biomechanical analysis of human movement and its clinical application
Kleissen, R. F. M., Buurke, J. H., Harlaar, J., et al. (1998) · Gait & Posture · doi:10.1016/s0966-6362(98)00025-3 · Cited by 133 · see citing works
Amplitude cancellation reduces the size of motor unit potentials averaged from the surface EMG
Keenan, K. G., Farina, D., Merletti, R., et al. (2006) · Journal of Applied Physiology · doi:10.1152/japplphysiol.01282.2005 · Cited by 129 · see citing works
“The study involved computer simulations that were based on a model of a population of motor units (28) with the addition of a surface EMG model that incorporated muscle fibers with a finite length and a volume conductor with skin, subcutaneous, and muscle layers (25).”
BackgroundMethodInfluentialCan co-activation reduce kinematic variability? A simulation study
Selen, L. P. J., Beek, P. J., & van Dieën, J. H. (2005) · Biological Cybernetics · doi:10.1007/s00422-005-0015-y · Cited by 123 · see citing works
“The motor-neuron pool, controlling the motor units, was inspired by the model presented by Fuglevand et al. (1993).”
BackgroundMethodGradient Convolution Kernel Compensation Applied to Surface Electromyograms
Holobar, A., & Zazula, D. (2007) · Springer eBooks · doi:10.1007/978-3-540-74494-8_77 · Cited by 124 · see citing works
The human motor neuron pools receive a dominant slow‐varying common synaptic input
Negro, F., Yavuz, U. Ş., & Farina, D. (2016) · The Journal of Physiology · doi:10.1113/jp271748 · Cited by 121 · see citing works
“The motor neuron parameters were the same as used by Cisi & Kohn (2008) and selected according to an exponential distribution over the pool of motor neurons (Fuglevand et al. 1993).”
MethodChristou, E. A., & Carlton, L. G. (2001) · The Journals of Gerontology Series A · doi:10.1093/gerona/56.12.b524 · Cited by 124 · see citing works
“Large motor units (50) and higher discharge-rate variabilities of motor units (51) have been associated with greater variability in force.”
A sigmoid function is the best fit for the ascending limb of the Hoffmann reflex recruitment curve
Klimstra, M. D., & Zehr, E. P. (2007) · Experimental Brain Research · doi:10.1007/s00221-007-1207-6 · Cited by 124 · see citing works
“However, the rate of recruitment of alpha motor neurons may follow an exponential function (Fuglevand et al. 1993; Jones 2005).”
BackgroundCisi, R. R. L., & Kohn, A. F. (2008) · Journal of Computational Neuroscience · doi:10.1007/s10827-008-0092-8 · Cited by 123 · see citing works
Are age-related increases in force variability due to decrements in strength?
Sosnoff, J. J., & Newell, K. M. (2006) · Experimental Brain Research · doi:10.1007/s00221-006-0422-x · Cited by 121 · see citing works
“Given that there are distinct contributions of neuromuscular mechanisms at various portions of the force production curve (Fuglevand et al. 1993; Milner-Brown et al. 1973), the age-force level variability interaction has lead to the proposition that various neurophysiological mechanisms are…”
BackgroundInfluentialNeural drive to human genioglossus in obstructive sleep apnoea
Saboisky, J. P., Butler, J. E., McKenzie, D. K., Gorman, R. B., Trinder, J. A., White, D. P., & Gandevia, S. C. (2007) · The Journal of Physiology · doi:10.1113/jphysiol.2007.139584 · Cited by 122 · see citing works
“Irrespective of the precise cause for this change, it would significantly increase the amplitude of the multiunit EMG signal (e.g. Fuglevand et al. 1993; Keenan et al. 2006), and may be sufficiently large to account fully for the previously reported difference in multiunit EMG between the control…”
BackgroundAutomatic segmentation of surface EMG images: Improving the estimation of neuromuscular activity
Vieira, T., Merletti, R., & Mesin, L. (2010) · Journal of Biomechanics · doi:10.1016/j.jbiomech.2010.03.049 · Cited by 119 · see citing works
“Table 1 Description of the parameters used to simulate interference EMGs (SD means standard deviation; Fuglevand et al., 1993).”
Duchateau, J., & Baudry, S. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00234 · Cited by 117 · see citing works
Computational Models for Neuromuscular Function
Valero‐Cuevas, F. J., Hoffmann, H., Kurse, M. U., Kutch, J. J., & Theodorou, E. A. (2009) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2009.2034981 · Cited by 118 · see citing works
“glevand and colleagues [95], which has been used extensively”
MethodWatanabe, K., Holobar, A., Kouzaki, M., Ogawa, M., Akima, H., & Moritani, T. (2016) · AGE · doi:10.1007/s11357-016-9915-0 · Cited by 115 · see citing works
“Mean firing rates with >30 % coefficient of variation were excluded for further analysis (Fuglevand et al. 1993).”
BackgroundSidhu, S. K., Weavil, J. C., Venturelli, M., Garten, R. S., Rossman, M. J., Richardson, R. S., Gmelch, B. S., MORGAN, D. E., & Amann, M. (2014) · The Journal of Physiology · doi:10.1113/jphysiol.2014.275438 · Cited by 113 · see citing works
“Regardless of the underlying mechanism, based on the correlation between EMG and MEP, it could be argued that the increase in EMG, per se, ‘facilitated’ the MEP size in the FENT condition (Fuglevand et al. 1993; McNeil et al. 2011) and therefore masked the fall seen in the CTRL condition (Fig.”
BackgroundTask-dependent spatial distribution of neural activation pattern in human rectus femoris muscle
Watanabe, K., Kouzaki, M., & Moritani, T. (2011) · Journal of Electromyography and Kinesiology · doi:10.1016/j.jelekin.2011.11.004 · Cited by 113 · see citing works
“This non-linear relationship could reflect physiological, i.e. motor unit recruitment and rate coding strategy (Fuglevand et al., 1993), and non-physiological factors, spatial inhomogeneity in the location of different types of motor unit within a muscle in the dimension of depth (Knight and Kamen,…”
BackgroundFactors Governing the Form of the Relation Between Muscle Force and the EMG: A Simulation Study
Zhou, P., & Rymer, W. Z. (2004) · Journal of Neurophysiology · doi:10.1152/jn.00367.2004 · Cited by 113 · see citing works
“It is further worth noting that, in the earlier model (Fuglevand et al. 1993), the diameter of muscle fibers of all motor units was assigned the same value, while bigger motor units contained more muscle fibers.”
BackgroundMethodResultInfluentialA note on the probability distribution function of the surface electromyogram signal
Nazarpour, K., Al-Timemy, A. H., Bugmann, G., et al. (2012) · Brain Research Bulletin · doi:10.1016/j.brainresbull.2012.09.012 · Cited by 111 · see citing works
Micromechanical modelling of skeletal muscles based on the finite element method
Böl, M., & Reese, S. (2008) · Computer Methods in Biomechanics & Biomedical Engineering · doi:10.1080/10255840701771750 · Cited by 109 · see citing works
“The aforementioned results nicely agree with models describing the activation behaviour at fibre or motor unit level, see e.g. Fuglevand et al. (1993); Ding et al. (1998) and Herbert and Gandevia (1999).”
ResultOlder Adults Use a Unique Strategy to Lift Inertial Loads With the Elbow Flexor Muscles
Graves, A. E., Kornatz, K. W., & Enoka, R. M. (2000) · Journal of Neurophysiology · doi:10.1152/jn.2000.83.4.2030 · Cited by 111 · see citing works
“At 10% MVC force therefore, the last-recruited motor unit in the hand muscle would have a larger relative peak-to-peak force than would the proximal arm muscle (Fuglevand et al. 1993).”
BackgroundFlexible neural control of motor units
Marshall, N. J., Glaser, J. I., Trautmann, E. M., Amematsro, E. A., Perkins, S. M., Shadlen, M. N., Abbott, L. F., Cunningham, J. P., & Churchland, M. M. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01165-8 · Cited by 107 · see citing works
- Authors
- Fuglevand, A. J., Winter, D. A., & Patla, A. E.
- Affiliations
- University of Waterloo (CA)
- Venue
- Journal of Neurophysiology
- Publisher
- American Physiological Society
- Published
- 1993-12-01
- Volume
- 70
- Issue
- 6
- Pages
- 2470-2488
- ISSN
- 0022-3077 (ISSN-L)
- 1522-1598
- OpenAlex
- W1809394698
- Cited by
- 988
- References
- 0
- Citations by year
- 2026: 41 · 2025: 47 · 2024: 64 · 2023: 49 · 2022: 38 · 2021: 55 · 2020: 43 · 2019: 44 · 2018: 45 · 2017: 37 · 2016: 37 · 2015: 41 · 2014: 34 · 2013: 38 · 2012: 39
- FWCI
- 4.88
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Neurophysiology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Muscle Physiology and Disorders
- Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
- Keywords
- motor unit recruitment, rate coding, surface electromyogram, EMG-force relationship, motor unit action potentials, action potential propagation velocity, recruitment threshold, motor unit territory, isometric force, voluntary muscle contraction
- MeSH terms
- Animals, Computer Simulation, Electromyography, Humans, Isometric Contraction, Models, Neurological, Motor Neurons, Muscles, Synaptic Transmission, Muscle Spindles, Recruitment, Neurophysiological
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