Key points
We propose and validate a method for accurately identifying the activity of populations of motor neurons during contractions at maximal rate of force development in humans.
The behaviour ...of the motor neuron pool during rapid voluntary contractions in humans is presented.
We show with this approach that the motor neuron recruitment speed and maximal motor unit discharge rate largely explains the individual ability in generating rapid force contractions.
The results also indicate that the synaptic inputs received by the motor neurons before force is generated dictate human potential to generate force rapidly.
This is the first characterization of the discharge behaviour of a representative sample of human motor neurons during rapid contractions.
During rapid contractions, motor neurons are recruited in a short burst and begin to discharge at high frequencies (up to >200 Hz). In the present study, we investigated the behaviour of relatively large populations of motor neurons during rapid (explosive) contractions in humans, applying a new approach to accurately identify motor neuron activity simultaneous to measuring the rate of force development. The activity of spinal motor neurons was assessed by high‐density electromyographic decomposition from the tibialis anterior muscle of 20 men during isometric explosive contractions. The speed of motor neuron recruitment and the instantaneous motor unit discharge rate were analysed as a function of the impulse (the time–force integral) and the maximal rate of force development. The peak of motor unit discharge rate occurred before force generation and discharge rates decreased thereafter. The maximal motor unit discharge rate was associated with the explosive force variables, at the whole population level (r2 = 0.71 ± 0.12; P < 0.001). Moreover, the peak motor unit discharge and maximal rate of force variables were correlated with an estimate of the supraspinal drive, which was measured as the speed of motor unit recruitment before the generation of afferent feedback (P < 0.05). We show for the first time the full association between the effective neural drive to the muscle and human maximal rate of force development. The results obtained in the present study indicate that the variability in the maximal contractile explosive force of the human tibialis anterior muscle is determined by the neural activation preceding force generation.
Key points
We propose and validate a method for accurately identifying the activity of populations of motor neurons during contractions at maximal rate of force development in humans.
The behaviour of the motor neuron pool during rapid voluntary contractions in humans is presented.
We show with this approach that the motor neuron recruitment speed and maximal motor unit discharge rate largely explains the individual ability in generating rapid force contractions.
The results also indicate that the synaptic inputs received by the motor neurons before force is generated dictate human potential to generate force rapidly.
This is the first characterization of the discharge behaviour of a representative sample of human motor neurons during rapid contractions.
Key points
Previous studies have indicated that several weeks of strength training is sufficient to elicit significant adaptations in the neural drive sent to the muscles.
There are few data, ...however, on the changes elicited by strength training in the recruitment and rate coding of motor units during voluntary contractions. We show for the first time that the discharge characteristics of motor units in the tibialis anterior muscle tracked across the intervention are changed by 4 weeks of strength training with isometric voluntary contractions.
The specific adaptations included significant increases in motor unit discharge rate, decreases in the recruitment‐threshold force of motor units and a similar input–output gain of the motor neurons.
The findings suggest that the adaptations in motor unit function may be attributable to changes in synaptic input to the motor neuron pool or to adaptations in intrinsic motor neuron properties.
The strength of a muscle typically begins to increase after only a few sessions of strength training. This increase is usually attributed to changes in the neural drive to muscle as a result of adaptations at the cortical or spinal level. We investigated the change in the discharge characteristics of large populations of longitudinally tracked motor units in tibialis anterior before and after 4 weeks of strength training the ankle‐dorsiflexor muscles with isometric contractions. The adaptations exhibited by 14 individuals were compared with 14 control subjects. High‐density electromyogram grids with 128 electrodes recorded the myoelectric activity during isometric ramp contractions to the target forces of 35%, 50% and 70% of maximal voluntary force. The motor unit recruitment and derecruitment thresholds, discharge rate, interspike intervals and estimates of synaptic inputs to motor neurons were assessed. The normalized recruitment‐threshold forces of the motor units were decreased after strength training (P < 0.05). Moreover, discharge rate increased by 3.3 ± 2.5 pps (average across subjects and motor units) during the plateau phase of the submaximal isometric contractions (P < 0.001). Discharge rates at recruitment and derecruitment were not modified by training (P < 0.05). The association between force and motor unit discharge rate during the ramp‐phase of the contractions was also not altered by training (P < 0.05). These results demonstrate for the first time that the increase in muscle force after 4 weeks of strength training is the result of an increase in motor neuron output from the spinal cord to the muscle.
Key points
Previous studies have indicated that several weeks of strength training is sufficient to elicit significant adaptations in the neural drive sent to the muscles.
There are few data, however, on the changes elicited by strength training in the recruitment and rate coding of motor units during voluntary contractions. We show for the first time that the discharge characteristics of motor units in the tibialis anterior muscle tracked across the intervention are changed by 4 weeks of strength training with isometric voluntary contractions.
The specific adaptations included significant increases in motor unit discharge rate, decreases in the recruitment‐threshold force of motor units and a similar input–output gain of the motor neurons.
The findings suggest that the adaptations in motor unit function may be attributable to changes in synaptic input to the motor neuron pool or to adaptations in intrinsic motor neuron properties.
The initial increases in force production with resistance training are thought to be primarily underpinned by neural adaptations. This notion is firmly supported by evidence displaying motor unit ...adaptations following resistance training; however, the precise locus of neural adaptation remains elusive. The purpose of this review is to clarify and critically discuss the literature concerning the site(s) of putative neural adaptations to short-term resistance training. The proliferation of studies employing non-invasive stimulation techniques to investigate evoked responses have yielded variable results, but generally support the notion that resistance training alters intracortical inhibition. Nevertheless, methodological inconsistencies and the limitations of techniques, e.g. limited relation to behavioural outcomes and the inability to measure volitional muscle activity, preclude firm conclusions. Much of the literature has focused on the corticospinal tract; however, preliminary research in non-human primates suggests reticulospinal tract is a potential substrate for neural adaptations to resistance training, though human data is lacking due to methodological constraints. Recent advances in technology have provided substantial evidence of adaptations within a large motor unit population following resistance training. However, their activity represents the transformation of afferent and efferent inputs, making it challenging to establish the source of adaptation. Whilst much has been learned about the nature of neural adaptations to resistance training, the puzzle remains to be solved. Additional analyses of motoneuron firing during different training regimes or coupling with other methodologies (e.g., electroencephalography) may facilitate the estimation of the site(s) of neural adaptations to resistance training in the future.
PURPOSEMotor unit conduction velocity (MUCV) represents the propagation velocity of action potentials along the muscle fibers innervated by individual motor neurons and indirectly reflects the ...electrophysiological properties of the sarcolemma. In this study, we investigated the effect of a 4-wk strength training intervention on the peripheral properties (MUCV and motor unit action potential amplitude, RMSMU) of populations of longitudinally tracked motor units (MU).
METHODSThe adjustments exhibited by 12 individuals who participated in the training (INT) were compared with 12 controls (CON). Strength training involved ballistic (4 × 10) and sustained (3 × 10) isometric ankle dorsiflexions. Measurement sessions involved the recordings of maximal voluntary isometric force and submaximal isometric ramp contractions, whereas high-density surface EMG was recorded from the tibialis anterior. High-density surface EMG signals were decomposed into individual MU discharge timings, and MU was tracked across the intervention.
RESULTSMaximal voluntary isometric force (+14.1%, P = 0.003) and average MUCV (+3.0%, P = 0.028) increased in the INT group, whereas normalized MU recruitment threshold (RT) decreased (−14.9%, P = 0.001). The slope (rate of change) of the regression between MUCV and MU RT increased only in the INT group (+32.6%, P = 0.028), indicating a progressive greater increase in MUCV for higher-threshold MU. The intercept (initial value) of MUCV did not change after the intervention (P = 0.568). The association between RMSMU and MU RT was not altered by the training.
CONCLUSIONThe increase in the rate of change in MUCV as a function of MU RT, but not the initial value of MUCV, suggests that short-term strength training elicits specific adaptations in the electrophysiological properties of the muscle fiber membrane in high-threshold MU.
There is a growing interest in decomposing high-density surface electromyography (HDsEMG) into motor unit spike trains to improve knowledge on the neural control of muscle contraction. However, the ...reliability of decomposition approaches is sometimes questioned, especially because they require manual editing of the outputs. We aimed to assess the inter-operator reliability of the identification of motor unit spike trains. Eight operators with varying experience in HDsEMG decomposition were provided with the same data extracted using the convolutive kernel compensation method. They were asked to manually edit them following established procedures. Data included signals from three lower leg muscles and different submaximal intensities. After manual analysis, 126 ± 5 motor units were retained (range across operators: 119–134). A total of 3380 rate of agreement values were calculated (28 pairwise comparisons × 11 contractions/muscles × 4–28 motor units). The median rate of agreement value was 99.6%. Inter-operator reliability was excellent for both mean discharge rate and time at recruitment (intraclass correlation coefficient > 0.99). These results show that when provided with the same decomposed data and the same basic instructions, operators converge toward almost identical results. Our data have been made available so that they can be used for training new operators.
Bioelectrical impedance analysis (BIA) is a rapid and user-friendly technique for assessing body composition in sports. Currently, no sport-specific predictive equations are available, and the ...utilization of generalized formulas can introduce systematic bias. The objectives of this study were as follows: (i) to develop and validate new predictive models for estimating fat-free mass (FFM) components in male elite soccer players; (ii) to evaluate the accuracy of existing predictive equations.
A total of 102 male elite soccer players (mean age 24.7 ± 5.7 years), participating in the Italian first league, underwent assessments during the first half of the in-season period and were randomly divided into development and validation groups. Bioelectrical resistance (R) and reactance (Xc), representing the bioimpedance components, were measured using a foot-to-hand BIA device at a single frequency of 50 kHz. Dual-energy X-ray absorptiometry was employed to acquire reference data for FFM, lean soft tissue (LST), and appendicular lean soft tissue (ALST). The validation of the newly developed predictive equations was conducted through regression analysis, Bland-Altman tests, and the area under the curves (AUC) of regression receiver operating characteristic (RROC) curves.
Developed models were: FFM = - 7.729 + (body mass × 0.686) + (stature
/R × 0.227) + (Xc × 0.086) + (age × 0.058), R
= 0.97, Standard error of estimation (SEE) = 1.0 kg; LST = - 8.929 + (body mass × 0.635) + (stature
/R × 0.244) + (Xc × 0.093) + (age × 0.048), R
= 0.96, SEE = 0.9 kg; ALST = - 24.068 + (body mass × 0.347) + (stature
/R × 0.308) + (Xc × 0.152), R
= 0.88, SEE = 1.4 kg. Train-test validation, performed on the validation group, revealed that generalized formulas for athletes underestimated all the predicted FFM components (p < 0.01), while the new predictive models showed no mean bias (p > 0.05), with R
values ranging from 0.83 to 0.91, and no trend (p > 0.05). The AUC scores of the RROC curves indicated an accuracy of 0.92, 0.92, and 0.74 for FFM, LST, and ALST, respectively.
The utilization of generalized predictive equations leads to an underestimation of FFM and ALST in elite soccer players. The newly developed soccer-specific formulas enable valid estimations of body composition while preserving the portability of a field-based method.
Bioelectrical impedance analysis (BIA) and anthropometry are considered alternatives to well-established reference techniques for assessing body composition. In team sports, the percentage of fat ...mass (FM%) is one of the most informative parameters, and a wide range of predictive equations allow for its estimation through both BIA and anthropometry. Although it is not clear which of these two techniques is more accurate for estimating FM%, the choice of the predictive equation could be a determining factor. The present study aimed to examine the validity of BIA and anthropometry in estimating FM% with different predictive equations, using dual X-ray absorptiometry (DXA) as a reference, in a group of futsal players. A total of 67 high-level male futsal players (age 23.7 ± 5.4 years) underwent BIA, anthropometric measurements, and DXA scanning. Four generalized, four athletic, and two sport-specific predictive equations were used for estimating FM% from raw bioelectric and anthropometric parameters. DXA-derived FM% was used as a reference. BIA-based generalized equations overestimated FM% (ranging from 1.13 to 2.69%, p < 0.05), whereas anthropometry-based generalized equations underestimated FM% in the futsal players (ranging from −1.72 to −2.04%, p < 0.05). Compared to DXA, no mean bias (p > 0.05) was observed using the athletic and sport-specific equations. Sport-specific equations allowed for more accurate and precise FM% estimations than did athletic predictive equations, with no trend (ranging from r = −0.217 to 0.235, p > 0.05). Regardless of the instrument, the choice of the equation determines the validity in FM% prediction. In conclusion, BIA and anthropometry can be used interchangeably, allowing for valid FM% estimations, provided that athletic and sport-specific equations are applied.
Neural and morphological adaptations combine to underpin the enhanced muscle strength following prolonged exposure to strength training, although their relative importance remains unclear. We ...investigated the contribution of motor unit (MU) behavior and muscle size to submaximal force production in chronically strength-trained athletes (ST) versus untrained controls (UT). Sixteen ST (age: 22.9 ± 3.5 yr; training experience: 5.9 ± 3.5 yr) and 14 UT (age: 20.4 ± 2.3 yr) performed maximal voluntary isometric force (MViF) and ramp contractions (at 15%, 35%, 50%, and 70% MViF) with elbow flexors, whilst high-density surface electromyography (HDsEMG) was recorded from the biceps brachii (BB). Recruitment thresholds (RTs) and discharge rates (DRs) of MUs identified from the submaximal contractions were assessed. The neural drive-to-muscle gain was estimated from the relation between changes in force (ΔFORCE, i.e. muscle output) relative to changes in MU DR (ΔDR, i.e. neural input). BB maximum anatomical cross-sectional area (ACSA
) was also assessed by MRI. MViF (+64.8% vs. UT,
< 0.001) and BB ACSA
(+71.9%,
< 0.001) were higher in ST. Absolute MU RT was higher in ST (+62.6%,
< 0.001), but occurred at similar normalized forces. MU DR did not differ between groups at the same normalized forces. The absolute slope of the ΔFORCE - ΔDR relationship was higher in ST (+66.9%,
= 0.002), whereas it did not differ for normalized values. We observed similar MU behavior between ST athletes and UT controls. The greater absolute force-generating capacity of ST for the same neural input demonstrates that morphological, rather than neural, factors are the predominant mechanism for their enhanced force generation during submaximal efforts.
In this study, we observed that recruitment strategies and discharge characteristics of large populations of motor units identified from biceps brachii of strength-trained athletes were similar to those observed in untrained individuals during submaximal force tasks. We also found that for the same neural input, strength-trained athletes are able to produce greater absolute muscle forces (i.e., neural drive-to-muscle gain). This demonstrates that morphological factors are the predominant mechanism for the enhanced force generation during submaximal efforts.
Abstract
Objective.
High-density surface electromyography (HD-sEMG) allows the reliable identification of individual motor unit (MU) action potentials. Despite the accuracy in decomposition, there is ...a large variability in the number of identified MUs across individuals and exerted forces. Here we present a systematic investigation of the anatomical and neural factors that determine this variability.
Approach
. We investigated factors of influence on HD-sEMG decomposition, such as synchronization of MU discharges, distribution of MU territories, muscle-electrode distance (MED—subcutaneous adipose tissue thickness), maximum anatomical cross-sectional area (ACSA
max
), and fiber cross-sectional area. For this purpose, we recorded HD-sEMG signals, ultrasound and magnetic resonance images, and took a muscle biopsy from the biceps brachii muscle from 30 male participants drawn from two groups to ensure variability within the factors—untrained-controls (UT = 14) and strength-trained individuals (ST = 16). Participants performed isometric ramp contractions with elbow flexors (at 15%, 35%, 50% and 70% maximum voluntary torque—MVT). We assessed the correlation between the number of accurately detected MUs by HD-sEMG decomposition and each measured parameter, for each target force level. Multiple regression analysis was then applied.
Main results.
ST subjects showed lower MED (UT = 5.1 ± 1.4 mm; ST = 3.8 ± 0.8 mm) and a greater number of identified MUs (UT: 21.3 ± 10.2 vs ST: 29.2 ± 11.8 MUs/subject across all force levels). The entire cohort showed a negative correlation between MED and the number of identified MUs at low forces (
r
= −0.6,
p
= 0.002 at 15% MVT). Moreover, the number of identified MUs was positively correlated to the distribution of MU territories (
r
= 0.56,
p
= 0.01) and ACSA
max
(
r
= 0.48,
p
= 0.03) at 15% MVT. By accounting for all anatomical parameters, we were able to partly predict the number of decomposed MUs at low but not at high forces.
Significance.
Our results confirmed the influence of subcutaneous tissue on the quality of HD-sEMG signals and demonstrated that MU spatial distribution and ACSA
max
are also relevant parameters of influence for current decomposition algorithms.
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