Motor control is the underlying process for how humans initiate, control and regulate the muscles and limbs upon performance of a voluntary movement or motor task, which requires the co- operative interaction between the CNS (consisting of the brain and spinal cord) and the musculoskeletal system. The first step in initiating a movement is the receipt of information by the prefrontal motor cortex, regarding the goal of the intended movement or task. The primary motor cortex generates a neural signal descending down its axons through the pyramidal tract of
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the spinal cord. Neurons in the pyramidal tract (more specifically the corticospinal tract) relay the signal down the spinal cord exciting the alpha motor neurons that initiate the sequence of muscle contraction (see section 2.3.1) in those skeletal muscles/muscle groups required to perform the movement. To ensure the stability or control of a task executed the CNS receives constant sensory (afferent) feedback from proprioceptors (e.g. Golgi tendon organs and muscle spindle receptors) about limb position and exerted force (Gandevia, 1996). This feedback is used to adjust and correct the subsequent descending neural drive and thus the planning and execution of the task. At the level of the spinal cord, central pattern generators have been shown to help regulate motorneuron firing through the receipt of sensory feedback (Pearson, 1995). Central pattern generators are located between the brain and the motor neurons and have been shown to produce automatic movements such as locomotion through coordinated motor patterns (Brown, 1911; Pearson & Gordon, 2000). In ballistic movements, due to their rapidity, sensory feedback cannot be relied upon to the same extent and instead the movement is regulated using feedforward control (i.e. responding to a control signal in a pre-defined way) (Kawato, 1999). Although. it is suggested that the optimal control of movement is suggested to result from a combination of both feedback and feedforward processes (Desmurget & Grafton, 2000). Practice of a particular skill or task improves the automaticity of the movement, requiring less conscious control. This can be described by the concept of a motor program, which is defined as the establishment of precise timing of muscle activations to achieve a given movement or task. Using EMG analyses, the existence of motor programs have been suggested to control locomotion (e.g. walking and running) (d'Avella & Bizzi, 2005; Ivanenko et al., 2004, 2006).
Due to the multiple degrees of freedom available to the motor system within the body’s subsystems, there exist multiple ways in which a movement can be executed to achieve the same task goal. This ‘problem’ arises from the redundancy of the motor system, first illustrated by Nikolai Bernstein (1967) through the observation of the hammering technique of expert blacksmiths. Bernstein found that while the end point of the hammer strokes were consistent with repeated execution of the task (i.e. low between-trial/within-subject variability of hammer trajectory), the kinematic patterns executed at the shoulder, elbow and wrist varied with each repetition (i.e. greater between-trial/within-subject variability). Redundancy has long been considered a problem for the motor system. However, this classical formulation has been questioned by researchers who suggest that the CNS does not suffer from a problem of motor redundancy, but instead may be fortunate to have the “bliss of motor abundance” (Gelfand & Latash, 1998; Latash, 2000; Latash, 2012). The multiple degrees of freedom of the motor system provide greater flexibility for performing a movement but also make understanding the control of movement very complex, particularly for tasks that are multi-joint, such as maximal cycling exercise.
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Several studies have highlighted that the CNS reduces the number of coordination strategies required to accomplish a task goal (e.g. the maximisation of power), in an attempt to reduce the complexity of the pedalling movement (Raasch et al., 1997; van Soest & Casius, 2000; Yoshihuku & Herzog, 1996). One particular strategy which has been evidenced by EMG and modelling analyses is that the CNS divides the neural drive between groups of muscles (i.e. muscle synergies), instead of each individual muscle, as a means to simplify the number of motor outputs required for a given task. The notion of muscle synergies have been shown for walking (Cappellini et al., 2006), upper limb reaching movements (d'Avella et al., 2008), rowing (Turpin et al., 2011) and cycling (Hug et al., 2010; Raasch & Zajac, 1999). Specific to the pedalling movement, the CNS appears to simplify the control of pedalling movement by sending a common neural drive to only three or four groups of muscles (or synergies). More specifically, Raasch and Zajac (1999) identified an extensor group (over the downstroke phase), a flexor group (during the upstroke phase) and two groups acting across TDC (RF and TA) and BDC (HAM, GAS and SOL) transition zones respectively; while several years later Hug et al. (2010) using EMG identified three synergies: 1) knee (VAS and RF) and hip (GMAX) extensors; 2) knee flexors (HAM) and ankle plantar-flexors (GAS); and 3) ankle dorsi-flexors (TA) and RF (Figure 2.8). Although, the theory of muscle synergies as a motor control strategy has recently been confronted with alternative assumptions put forward such as the minimal intervention principal (Kutch & Valero- Cuevas, 2012; Valero-Cuevas et al., 2009).
Figure 2.8. Schematic representations of muscle synergies identified for maximal cycling. A: illustrates synergies identified by Raasch and Zajac (1999), while B: illustrates synergies identified by Hug et al. (2010) Synergy # 1 includes VAS, RF and GMAX, synergy #2 includes HAM and GAS and synergy #3 includes TA and RF. Taken from Hug et al. (2010).
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2.3.3.1Changes in inter-muscular coordination
As outlined in section 2.3.1.1 above, individually the lower limb muscles have different functional roles and patterns of activation throughout a pedal cycle; however the effective application of force to the crank requires coordination of all these muscles (i.e. inter-muscular coordination). Inter-muscular coordination provides an insight into how the CNS and musculoskeletal systems interact to perform a movement or task (Pandy & Zajac, 1991). Indeed, previous studies have illustrated that optimal patterns of muscle activation and co-activation of the lower limb muscles determines how muscle power is transferred to the crank, and the resulting level of maximal external power produced (Dorel et al., 2012; Hug et al., 2011; Raasch et al., 1997; Rouffet & Hautier, 2008; van Ingen Schenau, 1989). Using normalised EMG profiles the co-activation (or co-contraction) of two muscles during a given time frame can be quantified using an equation to calculate an index of co-activation. This index has been used previously to assess muscle co- activation with regards to joint laxity (Lewek et al., 2004), knee osteoarthritis (Hubley-Kozey et al., 2009), walking (Arias et al., 2012) and more recently fatigue in sprint cycling (O'Bryan et al., 2014).
The co-activation of agonist-antagonist muscle pairs (e.g. GMAX-RF and VAS-HAM) is necessary in activities such as running, jumping and cycling to transfer forces across the lower limb joints and control the movement being executed (i.e. the direction of external force) (van Ingen Schenau, 1989; Van Ingen Schenau et al., 1992). Although, the co-activation of these opposing muscle pairs has been suggested as uneconomical due to their contributing forces cancelling out (Gregor et al., 1985). Further, the co-activation of agonist-antagonist muscle pairs has been suggested to provide joint stability (Hirokawa, 1991). EMG analyses have also indicated that the coordination of the lower limb muscles are sensitive to factors such as training history (e.g. novice vs trained cyclist (Chapman et al., 2008a)), power output (e.g. submaximal vs maximal) (Dorel et al., 2012; Ericson, 1986), pedalling rate (Baum & Li, 2003; Marsh & Martin, 1995; Neptune et al., 1997; Samozino et al., 2007), cycling posture and surface incline (Li & Caldwell, 1998), bicycle setup (Ericson, 1986), shoe/pedal interface (Cruz & Bankoff, 2001)) and fatigue (Dorel et al., 2009; O'Bryan et al., 2014).
2.3.3.2Changes in movement variability
The redundancy within the motor system allows the CNS to produce slightly different EMG and joint motion patterns even when the movement or task goal performed is the same (Bernstein, 1967; Srinivasan & Mathiassen, 2012). Indeed, a level of variability (albeit low for some) exists with the execution of a movement task, regardless of the population, stemming from inherent variability within the subsystems of the neuro-musculo-skeletal systems. Even during repetitive
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tasks that are highly constrained (e.g. the kinematics of the cycling movement), variability is ever present (Enders et al., 2013). The views of Hamill (1999) suggests that variability is an essential part of normal human functioning supporting the dynamical systems approach to motor control. Several different theories and methods (e.g. variance ratios, coefficient of variation) exist for the assessment of variability which can make interpretation across studies difficult. In cycling research the calculation of a variance ratio (VR) has been used for the analysis of intra-individual and inter-individual variability of EMG and mechanical patterns (Burden et al., 2003; Hug et al., 2008; Rouffet & Hautier, 2008). Using this method a lower VR indicates less variability in the pattern assessed. Using VR and other analyses such as coefficient of variation, the variability of lower limb EMG and joint kinematics during the pedalling movement have been shown to depend on the individual muscle (typically dependent on the architecture and function of the muscle, i.e. mono-articular vs bi-articular) (Hug et al., 2008; Ryan & Gregor, 1992); exercise intensity and the skill of the population (Chapman et al., 2006; Hug et al., 2004).
In contrast to simple movements (e.g. those involving a single-joint), multi-segment movements (such as cycling) for which muscles contribute to forces acting at joints they do not cross can further complicate the understanding of motor variability (Zajac & Gordon, 1989), despite the cycling movement being constrained by the circular trajectory of the pedal, unlike open kinetic chain movements such as running and jumping. Ryan and Gregor (1992) were some of the first authors to investigate the between-cycle variability in EMG patterns during cycling at a cadence of 90 rpm and workload of 250 W. Their analyses showed that single-joint GMAX, VM and VL exhibited the least amount of variability while bi-articular HAM had the highest. Some years later, Hug et al. (2008) found that EMG patterns of bi-articular muscles (but in particular RF and HAM) demonstrated higher levels of inter-individual variability at submaximal intensities (150 W and 250 W), however this level of variability was not reflected in the level of force applied to the pedal (i.e. kinetics). When interpreted using concepts presented by Muller and Sternad (2009), the result variable (e.g. force amplitude) could still be maintained through different execution variables (e.g. activation of muscles exercises). Although these findings provide detail for constant cycling, it has been shown that muscular demand (e.g. power output) appears to affect EMG variability, with less variability occurring at 300 W than at 150 W (Enders et al., 2013). These authors and others have noted that the solution space (defined as the combination of solutions that are actually used by humans rather than the theoretical number of possible solutions available to the task) reduces as the intensity of the exercise increases or when the task being performed needs to be executed in a specific manner (Hasson et al., 2012).
As noted in a plethora of motor control experiments, variability can be reduced following a period of practice interpreted as an indicator of learning and thus improved performance of the given task (Muller & Sternad, 2009). Sides and Wilson (2012) reported that lower levels of
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variability in patterns of joint motion and force application for expert performers of a task. With regards to training status and history, those unskilled in the pedalling movement appear to exhibit muscle recruitment strategies that are less refined than those who are trained, evidenced by greater intra-individual variation in EMG patterning (Chapman et al., 2008a). Bi-articular GAS and BF have been shown to exhibit less variable patterns of activation in well-trained cyclists compared to novices, potentially reflective of a strategy to control force across BDC through to the upstroke (De Marchis et al., 2013; Hug et al., 2010). In a later study by Chapman (2009) cycling novices exhibited greater variability in the lower limb joints which was considered to be reflective of meagre movement skill. In contrast to the reduction observed in most of the lower limb muscles, TA presented more inter-individual variability for competitive riders (Neptune et al., 1997), while the kinematics of the ankle joint varied within a cohort of similar ability cyclists (Kautz et al., 1991). Although variability appears to be lower in most muscles for those highly trained in cycling, a low-level of EMG variability is still present (Hug et al., 2004; Hug et al., 2008). Based on these findings and others that suggest that the CNS is already employing the most effective coordination strategy to produce maximal power output (Raasch et al., 1997; Yoshihuku & Herzog, 1996), EMG variability would be further reduced when cycling is performed at maximal intensities (e.g. sprinting), however there is currently no research to support this. Further the effect of variability in EMG or kinematic profiles on the limits of NMF have not been previously explored. Knowing that the CNS cannot eliminate variability and that the role of variability is equivocal, perhaps understanding what the optimal level of variability for a given movement is may be most important.