Motor adaptations to unilateral quadriceps fatigue during a bilateral pedaling task

Nielsen, N.-P. Brøchner; Hug, François; Guével, Arnaud; Fohanno, Vincent; Lardy, Julien; Dorel, Sylvain

doi:10.1111/sms.12811

Cited by 7 publications

(3 citation statements)

References 48 publications

(112 reference statements)

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“…5) can be due to compensatory mechanisms, which means that the CNS modulates the control of the contralateral leg in an unnatural and possibly less synchonrized manner to conduct the bilateral exercise of sit-to-stand, to compensate for the reduction of natural response of the fatigued limb. It should be noted that the possibility of changes in contralateral control due to fatigue has also been acknowledged in the literature [16], [17], [45], which supports the results of the proposed network analysis. Thus, in summary it can be mentioned that this paper, for the first time illustrates the global muscle network modulation in a bilateral manner due to fatigue.…”

Section: Discussionsupporting

confidence: 83%

Non-parametric Functional Muscle Network as a Robust Biomarker of Fatigue

O'Keeffe¹,

Shirazi²,

Yang

et al. 2021

Preprint

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The possibility of muscle fatigue detection using surface electromyography has been explored and multiple biomarkers, such as median frequency, have been suggested. However, there are contradictory reports in the literature which result in an inconsistent understanding of the biomarkers of fatigue. Thus, there is an unmet need for a statistically robust sEMG-based biomarker for fatigue detection. This paper, for the first time, demonstrates the superior capability of a non-parametric muscle network to reliably detect fatigue-related changes. Seven healthy volunteers completed a lower limb exercise protocol, which consisted of the 30s of a sit-to-stand exercise before and after the completion of fatiguing leg press sets. A non-parametric muscle network was constructed, using Spearman's power correlation, and showed a very reliable decrease in network metrics associated with fatigue (degree, weighted clustering coefficient (WCC)). The network metrics displayed a significant decrease at the group level (degree, WCC: p-value<0.001), individual subject level (degree: p-value<0.035 WCC: p-value<0.004) and particular muscle level (degree: p-value<0.017). Regarding the decrease in mean degree connectivity at particular muscles, all seven subjects followed the group trend. In contrast to the robust results achieved by the proposed non-parametric muscle network, classical spectrotemporal measurements showed heterogeneous trends at the particular muscle and individual subject levels. Thus, this paper for the first time shows that a non-parametric muscle network is a reliable biomarker of fatigue and could be used in a broad range of applications.

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Section: Discussionsupporting

confidence: 83%

Non-parametric Functional Muscle Network as a Robust Biomarker of Fatigue

O'Keeffe¹,

Shirazi²,

Yang

et al. 2021

Preprint

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“…Finally, changes in muscle coordination can occur on the uninjured limb even after a unilateral alteration of forcegenerating capacity, mostly because of changes in motor control in both limbs (9,10). Such a cross-sectional design therefore precludes the possibility of considering coordination of the contralateral limb as a "preinjury" status and in turn, prevents strong conclusions regarding a causal association between injury and muscle coordination observed in the injured limb.…”

Section: Methodological Considerationsmentioning

confidence: 99%

Bilateral differences in hamstring coordination in previously injured elite athletes

Avrillon

Hug

Guilhem

2020

Journal of Applied Physiology

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Hamstring strain injuries (HSIs) involve tissue disruption and pain, which can trigger long-term adaptations of muscle coordination. However, little is known about the effect of previous HSIs on muscle coordination and in particular, after the completion of rehabilitation and in the absence of symptoms. This study aimed to determine if elite athletes with a prior unilateral HSI have bilateral differences in coordination between the hamstring muscle heads after returning to sport. Seventeen athletes with a unilateral history of biceps femoris (BF) injury participated in the experiment. Surface electromyography was recorded from three hamstring muscles [BF, semimembranosus (SM), and semitendinosus] during submaximal isometric torque-matched tasks at 20% and 50% of maximal voluntary contraction. The product of normalized electromyographic amplitude with functional physiological cross-sectional area (PCSA) and moment arm was considered as an index of individual muscle torque. The contribution of the injured muscle to total knee flexion torque was lower in the injured than the uninjured limb (−5.6 ± 10.2%, P = 0.038). This reduced contribution of BF was compensated by a higher contribution of the SM muscle in the injured limb (+5.6 ± 7.5%, P = 0.007). These changes resulted from a lower contribution of PCSA from the injured muscle (BF) and a larger contribution of activation from an uninjured synergist muscle (SM). In conclusion, bilateral differences in coordination were observed in previously injured athletes despite the completion of rehabilitation. Whether these bilateral differences in hamstring coordination could constitute an intrinsic risk factor that contributes to the high rate of hamstring injury recurrence remains to be investigated. NEW & NOTEWORTHY We used an experimental approach, combining the assessment of muscle activation, physiological cross-sectional area, and moment arm to estimate force-sharing strategies among hamstring muscles during isometric knee flexions. We tested athletes with a history of hamstring injury. We observed a lower contribution of the injured biceps femoris to the total knee flexor torque in the injured limb than in the contralateral limb. This decreased contribution was mainly due to selective atrophy of the injured biceps femoris muscle and was compensated by an increased activation of the semimembranosus muscle.

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“…Each of these cycles was then interpolated to 200 time points and an ensemble-averaged cycle was obtained. The RMS EMG amplitude was calculated between − 5.5 and 44.4% of cycle, which corresponded to the downstroke phase [340°-160°; (Brochner Nielsen et al 2017)]. EMG was considered during this phase as it represents the main phase of activity for both the knee extensors and the plantarflexors (Hug and Dorel 2009).…”

Section: Submaximal Emg Amplitudementioning

confidence: 99%

Do individual differences in the distribution of activation between synergist muscles reflect individual strategies?

et al. 2018

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Individual differences in the distribution of activation between synergist muscles have been reported during a wide variety of tasks. Whether these differences represent actual individual strategies is unknown. The aims of this study were to: (i) test the between-day reliability of the distribution of activation between synergist muscles, (ii) to determine the robustness of these strategies between tasks, and to (iii) describe the inter-individual variability of activation strategies in a large sample size. Eighty-five volunteers performed a series of single-joint isometric tasks with their dominant leg [knee extension and plantarflexion at 25% of maximal voluntary contraction (MVC)] and locomotor tasks (pedalling and walking). Of these participants, 62 performed a second experimental session that included the isometric tasks. Myoelectrical activity of six lower limb muscles (the three superficial heads of the quadriceps and the three heads of the triceps surae) was measured using surface electromyography (EMG) and normalized to that measured during MVC. When considering isometric contractions, distribution of normalized EMG amplitude among synergist muscles, considered here as activation strategies, was highly variable between individuals (15.8% < CV < 42.7%) and robust across days (0.57 < ICC < 0.82). In addition, individual strategies observed during simple single-joint tasks were correlated with those observed during locomotor tasks [0.37 < r < 0.76 for quadriceps (n = 83); 0.30 < r < 0.66 for triceps surae (n = 82); all P < 0.001]. Our results provide evidence that people who bias their activation to a particular muscle do so during multiple tasks. Even though inter-individual variability of EMG signals has been well described, it is often considered noise which complicates the interpretation of data. This study provides evidence that variability results from actual differences in activation strategies.

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Motor adaptations to unilateral quadriceps fatigue during a bilateral pedaling task

Cited by 7 publications

References 48 publications

Non-parametric Functional Muscle Network as a Robust Biomarker of Fatigue

Non-parametric Functional Muscle Network as a Robust Biomarker of Fatigue

Bilateral differences in hamstring coordination in previously injured elite athletes

Do individual differences in the distribution of activation between synergist muscles reflect individual strategies?

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