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a , Distribution of targets. First set of experiments: subjects produced isometric forces in 20 different directions covering the sagittal ( Up – Back ) plane. Second set: subjects produced forces in 54 different directions covering the sagittal ( Up – Back ), horizontal ( Back – Out ), and frontal ( Up – Out ) planes. b , Subject B, sagittal plane: 120 force traces (6 per direction) from four different experiments. Force traces stay close to the sagittal plane, especially those in the up and forward directions. c , Typical force trace ( I ), unit record ( II ), and unit raster ( III ). Threshold force is marked in force trace with a gray line . C lose-up below unit trace shows consistent shape and amplitude of the potential of this unit.
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The directional activity of whole muscles has been shown to be broadly and often multimodally tuned, raising the question of how this tuning is subserved at the level of single motor units (SMUs). Previously defined rules of SMU activation would predict that units of the same muscle (or at least of the same neuromuscular compartment) are activated...
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... were amplified and bandpass filtered (10 –5000 Hz). Both force and surface EMG data were digitized at 10 kHz. EMG levels at steady state were computed by averaging across a 200 msec segment of rectified EMG (Pellegrini and Flanders, 1996). Single unit EMG was recorded with bipolar, Teflon-coated, fine-wire electrodes ( ϳ 25 m bare diameter) inserted into the muscle with a 27 gauge hypodermic needle. The needle and wires were sterilized before the experiment, and the skin was rubbed clean with alcohol before insertion of the electrodes. Unit recordings were amplified, bandpass filtered (100 –5000 Hz or 100 –10,000 Hz), viewed on an oscilloscope, digitized (10 or 20 kHz), stored on magnetic disk, and backed up on magneto-optical disk. Over the course of an experimental series, record- ings were made from 15– 48 units per subject and muscle, such that the different recording locations covered the whole width of each muscle. The place of needle insertion was defined relative to anatomical land- marks and its location was marked on a “map” overlaid on the muscle using a transparency. Unit identification. The activity of each unit was identified off-line and marked in the unit recording using a custom-written template-matching program. This program computed the Pearson correlation coefficient ( r ) between a template of the unit (selected from a trial with little noise) and the unit recording at every point of the trace. The value of r approached 1.0 for the parts of the trace that closely corresponded to the template in shape and amplitude, whereas in our experience the maximum value of r between template and background noise was between 0.3 and 0.6. Each time the value exceeded a cut-off criterion (which depending on the background noise level was between 0.7 and 0.9), the program “recog- nized” this part of the record as the unit potential and marked it in a raster of the trace (Fig. 1 c ). Trials in which a unit started firing during the force ramp but quit completely at a later point were excluded from the analysis. Relation between firing rate and recruitment threshold: the model. In the first series of experiments, whenever possible, threshold force for recruit- ment and firing rate at steady state were determined for each unit in each direction (it was not possible to measure firing frequency when the recruitment of additional units obscured the unit waveform). Firing frequency was graphed against direction in polar coordinates (Fig. 2 a ). In such a polar plot, the direction of force at the wrist is given by the direction of the line connecting the origin to a data point (or to a point on the circle in Fig. 2 a ). The distance of the data point from the origin represents the firing frequency of the unit in this direction. Analogous to the analysis used by Flanders and Soechting (1990) for surface EMG activity, cosine f unctions with a threshold nonlinearity were fit to the frequency data according to the formula: Firing frequency cos (1) where c is a constant offset, a is a constant scaling the cosine f unction, F is force magnitude, 0 is the best direction of the unit defined as the center of the cosine peak, and is the current force direction. Figure 2 a depicts hypothetical unit activity that is perfectly cosine tuned with its best direction 0 being straight up. For forces in this direction, the unit fires at maximum frequency; as the force direction deviates from this best direction, firing frequency (the length of the dashed lines) decreases until the unit is silent at angles 90° away from 0 . Compared to the steady-state firing frequency, the level of threshold force should show the opposite relation to force direction: for its best direction 0 , the unit should be recruited most readily, i.e., the force level at recruitment should be a minimum (Fig. 2, compare a , b ). As force direction deviates from 0, the magnitude of threshold force (Fig. 2 b , the length of the dashed lines ) should increase until for force directions Ն 90° from 0, the unit is silent and threshold force is infinite. This relation of threshold force to force direction is f ulfilled if threshold force data fall on a straight line when plotted in x , y force coordinates. That this is, in fact, the case has been demonstrated by Theeuwen et al. (1994) using SMU threshold data from seven human shoulder and elbow muscles. In our model, threshold force (during the ramp) and firing frequency (at steady state) are thus inversely related. In the right triangles in Figure 2, a and b , firing frequency ( f ) is equal to the cosine of the angle between the best and the current force direction of the unit, whereas threshold force ( t ) is equal to 1.0 divided by the cosine of the same angle . Based on the findings of Monster and Chan (1977), which show that SMUs are recruited at a characteristic frequency and increase their ...
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After exposure of the human body to resistive exercise, the force‐generation capacity of the trained muscles increases significantly. Despite decades of research, the neural and muscular stimuli that initiate these changes in muscle force are not yet fully understood. The study of these adaptations is further complicated by the fact that the change...
Citations
... We show here that most of the motor units from the triceps surae muscles might receive two sources of common inputs. The combination of multiple independent inputs might therefore explain why previous works reported changes in recruitment strategies between motor units from the same muscle while changing the mechanical constraints of the task (Desnedt & Gidaux, 1981;Herrmann & Flanders, 1998;Marshall et al., 2022;ter Haar Romeny et al., 1984). ...
Recent studies have suggested that the nervous system generates movements by controlling groups of motor neurons (synergies) that do not always align with muscle anatomy. In this study, we determined whether these synergies are robust across tasks with different mechanical constraints. We identified motor neuron synergies using principal component analysis (PCA) and cross‐correlations between smoothed discharge rates of motor neurons. In part 1, we used simulations to validate these methods. The results suggested that PCA can accurately identify the number of common inputs and their distribution across active motor neurons. Moreover, the results confirmed that cross‐correlation can separate pairs of motor neurons that receive common inputs from those that do not receive common inputs. In part 2, 16 individuals performed plantarflexion at three ankle angles while we recorded EMG signals from the gastrocnemius lateralis (GL) and medialis (GM) and the soleus (SOL) with grids of surface electrodes. The PCA revealed two motor neuron synergies. These motor neuron synergies were relatively stable, with no significant differences in the distribution of motor neuron weights across ankle angles (P = 0.62). When the cross‐correlation was calculated for pairs of motor units tracked across ankle angles, we observed that only 13.0% of pairs of motor units from GL and GM exhibited significant correlations of their smoothed discharge rates across angles, confirming the low level of common inputs between these muscles. Overall, these results highlight the modularity of movement control at the motor neuron level, suggesting a sensible reduction of computational resources for movement control. image
Key points
The CNS might generate movements by activating groups of motor neurons (synergies) with common inputs.
We show here that two main sources of common inputs drive the motor neurons innervating the triceps surae muscles during isometric ankle plantarflexions.
We report that the distribution of these common inputs is globally invariant despite changing the mechanical constraints of the tasks, i.e. the ankle angle.
These results suggest the functional relevance of the modular organization of the CNS to control movements.
... Optimality suggests flexibly using MUs suited to each situation [5][6][7] . However, nearly a century of research supports an alternative rigid strategy that approximates optimality 1,8,9 . ...
... Second, cell-intrinsic mechanisms cause some MUs to rotate on and off, over many seconds, to combat fatigue 27 . Finally, for 'multifunctional' muscles with multiple mechanical actions, the size principle holds only within each action 6,28,29 . Each mechanical action is hypothesized to have its own common drive (or 'synergy'). ...
... We can fit this model with one latent (x t can be a single value, as for all analyses in Fig. 7) or multiple latents (Fig. 8b). The relationship between the MU activity and the underlying latent(s) is given by (6) where f i denotes a learned link function for the i th MU, and τ i denotes a learned lag between its response and the shared latent variables. We constrained τ i ∈ [− 25,25] ms. ...
Voluntary movement requires communication from cortex to the spinal cord, where a dedicated pool of motor units (MUs) activates each muscle. The canonical description of MU function rests upon two foundational tenets. First, cortex cannot control MUs independently but supplies each pool with a common drive. Second, MUs are recruited in a rigid fashion that largely accords with Henneman’s size principle. Although this paradigm has considerable empirical support, a direct test requires simultaneous observations of many MUs across diverse force profiles. In this study, we developed an isometric task that allowed stable MU recordings, in a rhesus macaque, even during rapidly changing forces. Patterns of MU activity were surprisingly behavior-dependent and could be accurately described only by assuming multiple drives. Consistent with flexible descending control, microstimulation of neighboring cortical sites recruited different MUs. Furthermore, the cortical population response displayed sufficient degrees of freedom to potentially exert fine-grained control. Thus, MU activity is flexibly controlled to meet task demands, and cortex may contribute to this ability.
... In particular, the biceps brachii, used in this study, is known to have multiple anatomical neuromuscular compartments, with separate subdivisions even within the gross anatomical divide of the short and long head 45 . Biceps brachii motor unit recruitment has been shown to vary depending on the contraction levels in the flexion and/or supination directions, with motor units distributed across the biceps with no clear spatial distribution relevant to function [30][31][32]46 . Other muscles have been shown to have similar taskdependent recruitment order differences, such as in the first dorsal interosseus muscle when performing flexion versus abduction of the index finger and in a variety of non-multifunctional arm muscles 33,47 . ...
Brain-machine interfaces (BMIs) have the potential to augment human functions and restore independence in people with disabilities, yet a compromise between non-invasiveness and performance limits their relevance. Here, we hypothesized that a non-invasive neuromuscular-machine interface (NMI) providing real-time neurofeedback of individual motor units within a muscle could enable independent motor unit control to an extent suitable for high-performance BMI applications. Over 6 days of training, 8 participants progressively learned to skillfully and independently control three biceps brachii motor units to complete a two-dimensional center-out task. We show that neurofeedback enabled motor unit activity that largely violated recruitment constraints observed during ramp-and-hold isometric contractions thought to limit individual motor unit controllability. Finally, participants demonstrated the suitability of individual motor units for powering general applications through a spelling task. These results illustrate the flexibility of the sensorimotor system and highlight individual motor units as a promising source of control for BMI applications.
... These task groups may have intrinsic properties optimized for the performance of a specific functional task (Hoffer et al. 1987), however, recent studies suggest that the physical location of the units may also be important (Hodson-Tole and Wakeling 2009). Given that individual muscles are nonuniform in their mechanical actions (Chanaud et al. 1991), directional tuning of single motor units has been observed (Herrmann and Flanders 1998). The directional tuning of single motor units seems to depend more upon the location of motor units within the muscle volume rather than on the degree of muscle activation (Herrmann and Flanders 1998). ...
... Given that individual muscles are nonuniform in their mechanical actions (Chanaud et al. 1991), directional tuning of single motor units has been observed (Herrmann and Flanders 1998). The directional tuning of single motor units seems to depend more upon the location of motor units within the muscle volume rather than on the degree of muscle activation (Herrmann and Flanders 1998). ...
The purpose of this study is to investigate whether regional modulation of the ankle plantarflexors during standing was related to the recruitment of motor units associated with force direction. Fourteen participants performed a multi-directional leaning task in standing. Participants stood on a force platform and maintained their center of pressure in five different target directions. Motor unit firings were extracted by decomposition of high-density surface electromyograms recorded from the ankle plantarflexor muscles. The motor unit barycentre, defined as the weighted mean of the maximal average rectified values across columns and rows, was used to evaluate the medio-lateral and proximo-distal changes in the surface representation of single motor units across different leaning target directions. Using a motor unit tracking analysis, groups of motor units were identified as being common or unique across the target directions. The leaning directions had an effect on the spatial representations of motor units in the medial gastrocnemius and soleus (p < 0.05), but not in the lateral gastrocnemius (p > 0.05). Motor unit action potentials were represented in the medial and proximal aspects of the muscles during forward vs. lateral leans. Further analysis determined that the common motor units were found in similar spatial locations across the target directions, whereas newly recruited unique motor units were found in different spatial locations according to target direction (p < 0.05). The central nervous system may possess the ability to activate different groups of motor units according to task demands to meet the force-direction requirements of the leaning task.
... CC-BY-NC-ND 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made 15,25,36,37 . Other muscles have been shown to have similar task-dependent recruitment order differences, such as in the first dorsal interosseus muscle when performing flexion versus abduction of the index finger and in a variety of non-multifunctional arm muscles 20,38 . ...
Brain-machine interfaces (BMIs) have the potential to restore independence in people with disabilities, yet a compromise between non-invasiveness and performance limits their translational relevance. Here, we demonstrate a high-performance BMI controlled by individual motor units non-invasively recorded from the biceps brachii. Through real-time auditory and visual neurofeedback of motor unit activity, 8 participants learned to skillfully and independently control three motor units in order to complete a two-dimensional center-out task, with marked improvements in control over 6 days of training. Concomitantly, dimensionality of the motor unit population increased significantly relative to naturalistic behaviors, largely violating recruitment orders displayed during stereotyped, isometric muscle contractions. Finally, participants' performance on a spelling task demonstrated translational potential of a motor unit BMI, exceeding performance across existing non-invasive BMIs. These results demonstrate a yet-unexplored level of flexibility of the peripheral sensorimotor system and show that this can be exploited to create novel non-invasive, high-performance BMIs.
... During cycling, effective muscle coordination is required to provide an effective force profile, given that activation patterns can affect the direction (Herrmann and Flanders, 1998), magnitude and duration (Blake and Wakeling, 2015) of the external force developed. ...
Optimisation of movement strategies during cycling is an area which has gathered a lot of attention over the past decade. Resolutions to augment performance have involved manipulations of bicycle mechanics, including chainring geometries. Elliptical chainrings are proposed to provide a greater effective diameter during the downstroke, manipulating mechanical leverage and resulting in greater power production during this period.
A review of the literature indicates that there is a pervasive gap in our understanding of how the theoretical underpinnings of elliptical chainrings might be translated to practical use. Despite reasonable theory of how these chainrings might enforce a variation in crank angular velocity and consequently alter force production, performance-based analyses have struggled to present evidence of this.
The purpose of this thesis was to provide a novel approach to this problem by combining experimental data with musculoskeletal modelling and evaluating how elliptical chainrings might influence crank reactive forces, joint kinematics, muscle-tendon unit behaviour and muscle activation. One main study was proposed to execute this analysis, and an anatomically constrained model was subsequently used to determine the joint kinematics and muscle-tendon unit behaviour. Bespoke elliptical chainrings were designed for this study and as such, different levels of chainring eccentricity (i.e. ratio of major to minor axis) and positioning against the crank were presented whilst controlling the influence of other variables known to affect the neuromuscular system such as cadence and load.
Findings presented in this thesis makes a new and major contribution in our understanding of the neuromusculoskeletal adaptations which occur when using elliptical chainrings, showing alterations in crank reaction force, muscle-tendon unit velocities, joint kinematics and muscle excitation over a range of cadences and loads, and provides direction for where the future of this research might be best applied.
Keywords: Elliptical chainrings; Cycling; Musculoskeletal modelling; Principal Component Analysis; Electromyography
... Even though there seems to be a size rule governing the recuritment of MUs within a pool (Henneman et al. 1965), different studies on human subjects observed Communicated by Toshio Moritani. that MUs in the same upper limb muscle may be activated selectively (Herrmann and Flanders 1998;Riek and Bawa 1992;ter Romeny et al. 1984). Moreover, the selective activation of different muscle sub-portions (Brown et al. 2007;Holtermann et al. 2009;Pappas et al. 2002) further suggests that MUs may be recruited according to functional demands imposed by the motor task and, thus, according to their location within the muscle. ...
... Different motor units were recruited during different tasks. This observation is consistent with previous studies (Herrmann and Flanders 1998;Riek and Bawa 1992;ter Romeny et al. 1984) that identified task-specific recruitment of MUs of upper limb muscles. However, the different units, recruited during different tasks, were not identified to be spatially localized in different portions of the BB during different tasks. ...
... However, the different units, recruited during different tasks, were not identified to be spatially localized in different portions of the BB during different tasks. This observation is in line with the study performed by Herrmann and Flanders (1998), who demonstrated that MUs with closely located territories may have different directional tuning. Our results, however, differ from those reported by ter Haar Romeny and collaborators (1984), who observed a preferential recruitment of units in the most lateral and medial regions of BB long head during elbow flexion and forearm supination, respectively. ...
PurposeDifferent motor units (MUs) in the biceps brachii (BB) muscle have been shown to be preferentially recruited during either elbow flexion or supination. Whether these different units reside within different regions is an open issue. In this study, we tested wheter MUs recruited during submaximal isometric tasks of elbow flexion and supination for two contraction levels and with the wrist fixed at two different angles are spatially localized in different BB portions.Methods
The MUs’ firing instants were extracted by decomposing high-density surface electromyograms (EMG), detected from the BB muscle of 12 subjects with a grid of electrodes (4 rows along the BB longitudinal axis, 16 columns medio-laterally). The firing instants were then used to trigger and average single-differential EMGs. The average rectified value was computed separately for each signal and the maximal value along each column in the grid was retained. The center of mass, defined as the weighted mean of the maximal, average rectified value across columns, was then consdiered to assess the medio-lateral changes in the MU surface representation between conditions.ResultsContraction level, but neither wrist position nor force direction (flexion vs. supination), affected the spatial distribution of BB MUs. In particular, higher forces were associated with the recruitment of BB MUs whose action potentials were represented more medially.Conclusion
Although the action potentials of BB MUs were represented locally across the muscle medio-lateral region, dicrimination between elbow flexion or supination seems unlikely from the surface representation of MUs action potentials.
... The solutions available within a muscle may, therefore, be constrained by several factors. For example: organization into motor unit task groups (20); common neural drive acting across motor unit populations (21) and; spatial organization of motor units influencing which can appropriately contribute to the direction of the force vector required at the joint (22)(23)(24). As such, it may be that although the number of solutions available to the limb as a whole is limited by pedal cycle duration, reductions in the solutions available within individual muscles may not occur. ...
Purpose:
A key determinant of muscle coordination and maximum power output during cycling is pedalling cadence. During cycling the neuromuscular system may select from numerous solutions that solve the task demands while producing the same result. For more challenging tasks fewer solutions will be available. Changes in the variability of individual muscle excitations (EMG) and multi-muscle coordination, quantified by entropic half-life (EnHL), can reflect the number of solutions available at each system level. We therefore ask whether reduced variability in muscle coordination patterns occur at critical cadences and if they coincide with reduced variability in excitations of individual muscles.
Methods:
Eleven trained cyclists completed an array of cadence-power output conditions. EnHL of EMG intensity recorded from 10 leg muscles and EnHL of principal components describing muscle coordination were calculated. Multivariate adaptive regressive splines were used to determine the relationships between each EnHL and cycling condition or excitation characteristics (duration, duty cycle).
Results:
Muscle coordination became more persistent at cadences up to 120 r.p.m., indicated by increasing EnHL values. Changes in EnHL at the level of the individual muscles differed from the changes in muscle coordination EnHL, with longer EnHLs occurring at the slowest (< 80 r.p.m.) and fastest (>120 r.p.m.) cadences. EnHL of the main power producing muscles however reached a minimum by 80 r.p.m. and did not change across the faster cadences studied.
Conclusions:
Muscle coordination patterns, rather than the contribution of individual muscles, are key to power production at faster cadences in trained cyclists. Reductions in maximum power output at cadences above 120 r.p.m. could be a function of the time available to coordinate orientation and transfer of forces to the pedals.
... Such selective activation of muscles subvolumes implies that motor neurons serving different subvolumes receive distinct net inputs. Following this reasoning, it is possible that different pools of MUs, each elicited for a specific purpose (e.g., to regulate force direction or to endure a fatiguing contraction 13,31,32 ), receive different inputs. The present results suggest this concept may be extended, at least in VM, to motor neurons serving different proximodistal muscle regions. ...
Introduction:
Previous evidence suggests the fibres of different motor units reside within distinct vastus medialis (VM) regions. Whether the activity of these motor units may be modulated differently remains unknown. Here we assess the discharge rate of motor units detected proximo-distally from VM to address this issue.
Methods:
Surface electromyograms (EMGs) were recorded proximally and distally from VM while ten healthy subjects performed isometric contractions. Single motor units were decomposed from surface EMGs. The smoothed discharge rates of motor units identified from the same and from different VM regions were then cross-correlated.
Results:
During low-level contractions, the discharge rate varied more similarly for distal (cross-correlation peak; interquartile interval: 0.27-0.40) and proximal (0.28-0.52) than for proximo-distal pairs of VM motor units (0.20-0.33; P=0.006).
Discussion:
The discharge rates of motor units from different proximo-distal VM regions show less similarity in their variations than those of pairs of units either distally or proximally. This article is protected by copyright. All rights reserved.
... For different skeletal muscles, regional changes in the EMG amplitude have been observed for a number of circumstances, such as for different joint positions (Watanabe et al., 2012), contraction durations (Farina et al., 2008;Tucker et al., 2009) and force levels (Holtermann et al., 2005;Rojas-Martínez et al., 2012), as well as during standing (Vieira et al., 2010) or dynamic contractions (Falla and Farina, 2007). The mechanical efficiency of specific regions within the muscle (Herrmann and Flanders, 1998;Holtermann et al., 2005) and neural strategies for delaying and/or minimizing muscle fatigue (Falla and Farina, 2007;van Dieën et al., 1993) are examples of potential mechanisms of uneven distribution of activity within muscles. ...
Proper muscle activity quantification is highly relevant to monitor and treat spastic cocontraction. As activity may distribute unevenly within muscle volumes, particularly for pennate calf muscles, surface electromyograms (EMGs) detected by traditional bipolar montage may provide biased estimations of muscle activity. We compared cocontraction estimates obtained using bipolar vs grids of electrodes (high-density EMG, HD-EMG). EMGs were collected from medial gastrocnemius, soleus and tibialis anterior during isometric plantar and dorsi-flexion efforts at three levels (30%, 70% and 100% MVC), knee flexed and extended. Cocontraction index (CCI) was estimated separately for each electrode pair in the grid. While soleus and tibialis anterior CCI estimates did not depend on the detection system considered, for gastrocnemius bipolar electrodes provided larger cocontraction estimates than HD-EMG at highest effort levels, at both knee angles (ANOVA; P < .001). Interestingly, HD-EMG detected greater gastrocnemius EMGs distally during plantar flexions, and greater CCI values proximally during dorsiflexions. These results suggest that bipolar electrodes: (i) provide reliable estimates of soleus and tibialis anterior cocontraction; (ii) may under-or overestimate gastrocnemius cocontraction, depending on their distal or proximal position.
