改变肌肉协同作用可以控制未受损的步态吗？

Can Altered Muscle Synergies Control Unimpaired Gait?

Naser Meharbi1*, Michael H. Schwartz2, Katherine M. Steele1
1Mechanical Engineering, University of Washington, Seattle, WA, USA
2Gillette’s Children Specialty Healthcare, Saint Paul, MN, USA and University of Minnesota,Minneapolis, MN, USA


扫描文末“二维码”，免费阅读全文ABSTRACT

Recent studies have postulated that the human motor control system recruitsgroups of muscles through low-dimensional motor commands, or musclesynergies. This scheme simplifies the neural control problem associated with thehigh-dimensional structure of the neuromuscular system. Several lines of evidencehave suggested that neurological injuries, such as stroke or cerebral palsy, mayreduce the dimensions that are available to the motor control system, and thesealtered dimensions or synergies are thought to contribute to impaired walkingpatterns. However, no study has investigated whether impaired low-dimensionalcontrol spaces necessarily lead to impaired walking patterns. In this study, using atwo-dimensional model of walking, we developed a synergy-based controlframework that can simulate the dynamics of walking. The simulation analysisshowed that a synergy-based control scheme can produce well-coordinatedmovements of walking matching unimpaired gait. However, when the dimensionsavailable to the controller were reduced, the simplified emergent pattern deviatedfrom unimpaired gait. A system with two synergies, similar to those seen afterneurological injury, could not produce an unimpaired walking pattern. Thesefindings provide further evidence that altered muscle synergies can contribute toimpaired gait patterns and may need to be directly addressed to improve gait afterneurological injury.

INTRODUCTION

Locomotion is a complex motor task which requires precise coordination of highly nonlinear andredundant musculotendinous units. The redundancy of the musculotendinous units, where thenumber of muscles crossing each joint exceeds the kinematic degrees of freedom, provides aflexible but intricate mechanism for controlling the human body. Prior research analyzing muscleactivity with surface electromyography (EMG) recordings suggests that the human centralnervous system may reduce motor control complexity by recruiting muscles through individualmotor commands sent to weighted groups of muscles (Ivanenko, et al., 2005; Tresch, et al.,1999; Davis & Vaughan, 1993). This results in a low dimensional control space that may berepresented by a small set of muscle synergies. In this manuscript, a muscle synergy (sometimescalled module or motor primitive) is defined as a weighted group of muscles that are activatedtogether during functional motor tasks. The complex muscle activation patterns during walking,and their variability across walking speeds, can be accounted for by independent control ofmuscle synergies (Cappellini, et al., 2006). Muscle synergies can be identified by applyingvarious factorization techniques to EMG data (e.g., factor analysis, independent componentanalysis, and nonnegative matrix factorization). The muscle synergies identified during walkinghave relatively consistent muscle organizations and activation patterns regardless of thetechnique used (Tresch, et al., 2006), and appear to be organized around functional motor tasksof walking (e.g., body support and forward propulsion) (Ting & Macpherson, 2005; Neptune, etal., 2009). Several research studies have reported that four to six functionally relevant muscle synergies areneeded to describe muscle activity during unimpaired walking (Clark, et al., 2010; Ivanenko, etal., 2006; Cappellini, et al., 2006). Motor control complexity increases during human development (Dominici, et al., 2011), and may be reduced following a neurological injury(Ivanenko, et al., 2013). Individuals who have impaired walking due to a stroke (Clark, et al.,2010), incomplete spinal cord injury (Fox, et al., 2013) or cerebral palsy (Steele, et al., 2015)recruit fewer muscle synergies compared to unimpaired individuals. Often, in individuals whohave had a stroke, the impaired synergies resemble merging of specific unimpaired synergies(Clark, et al., 2010). Similar conclusions have been drawn for upper-extremity movements afterstroke (Cheung, et al., 2012; Roh, et al., 2015). Altered synergies have been hypothesized tocontribute to not only impaired movement, but also the increased muscle activity and greaterenergetic costs commonly observed among individuals with neuromuscular disorders comparedto unimpaired individuals (Rose, et al., 1990; Steele, et al., 2017; Kramer, et al., 2016; van derKrogt, et al., 2012).Using computer simulations of musculoskeletal models, previous studies have provided evidencethat synergy-based control can produce well-coordinated steady-state forward walking (Neptune,et al., 2009; Allen & Neptune, 2012; Sartori, et al., 2013). Synergy-based control has beenpreviously used to incorporate subject-specific motor impairments in control to simulateimpaired walking patterns (Meyer, et al., 2016; Walter, et al., 2014). However, whether alteredsynergies, such as those commonly observed after neurological injury, can control unimpairedgait remains unknown. The answer to this question can provide useful insight into theneurological complexity required for walking. Specifically, the ability of a simulation to achieveunimpaired gait using “impaired” synergies may suggest that unimpaired gait is feasible despitethe neurological injury and may be obtained through targeted treatments such as strength trainingor orthopaedic surgery. However, the impaired synergies may require greater muscle activity orenergetic cost to achieve this unimpaired gait pattern. Alternatively, failure to achieve an unimpaired gait with impaired synergies would suggest that altered motor control enforcesfundamental constraints or reflects compensatory strategies that underlie impaired gait. Theseconstraints or compensations must be acknowledged and addressed in the treatment of nonneurological issues to achieve unimpaired walking.In the present study, we combined muscle synergy theory with musculoskeletal simulation andoptimal control theory to develop a synergy-based controller that can simulate the forwarddynamics of walking. Muscle synergies were identified by applying factorization techniques tomodel-based muscle excitations. Each synergy was then controlled through a low-dimensionalset of motor commands computed by a synergy-based controller. Unlike previous studies(Neptune, et al., 2009; Sartori, et al., 2013) that assumed a fixed number of synergies, thisresearch examined multiple potential synergy solutions to determine whether a given number ofsynergies could achieve unimpaired gait. This research aimed to use synergy-based control to (a)investigate whether an unimpaired gait can be achieved from low-dimensional control spacesrepresented by altered muscle synergies, (b) analyze the kinematics and muscle activitiesresulting from synergy-based control, and (c) evaluate the effort required by the resulting gaitpattern to examine how muscle recruitment may change with synergy-based control, and whetherthese changes contribute to a less-efficient gait pattern. Addressing these aims will enhance ourunderstanding about the impacts of altered synergies from neurological injuries on impaired gait,and the implications of these synergies on treatment planning.ResultsESULTS

The kinematics predicted by individual muscle control closely matched those observed inunimpaired gait. The average RMSE of hip, knee, and ankle kinematics compared toexperimental data were 3.1o, 2.0o, and 2.1o, respectively over a gait cycle (Fig. 3, left). Simulatedmodel-based neural excitations were consistent with on-off activity of muscles from EMG dataduring unimpaired gait (Fig. 3, right). In the simulations, RF was negligibly active, and the HFLwas activated earlier than expected from experimental EMG recordings.The muscle synergies extracted by the NNMF algorithm from the model-based neural excitationshad a total variance accounted for (tVAF) greater than 0.97 when three or more synergies wereidentified (0.98 and 0.99 for four and five synergies, respectively) and 0.87 when two musclesynergies were identified (Fig. 4b). The VAFs of individual muscle were greater than 0.80 for allmuscles in five, four, and three synergies solutions. The two synergy solution described over0.80 of VAF for all muscles except for TA, which dropped to 0.30. In the five-synergy solution(first column in Fig. 4a), the first synergy (ankle plantarflexor: SOL and GAS), second synergy(hip and knee extensors: VAS, GLU, and SOL), third synergy (hip flexor: HFL), fourth synergy(ankle dorsiflexor-hip flexor: TA, HFL), and fifth synergy (hip extensor: HAM, GLU) accountedfor 0.60, 0.16, 0.16, 0.11, and 0.10 of the total variation in individual muscle excitations,respectively. In the four-synergy solution, the ankle dorsiflexor-hip flexor synergy merged withthe hip flexor and the hip extensor synergies, and in the three-synergy solution, the threeaforementioned synergies formed one combined synergy (second and third columns in Fig. 4a).The independent burst of synergy activation during late swing and early stance disappeared inthe two-synergy solution, resulting in lower tVAF.Compared to synergy-based control with five fixed synergies, the tracking performance ofcontrol with two fixed synergies was significantly degraded. The RMSE of hip, knee, and anklekinematics between individual muscle and two-synergy control were 4.9o, 5.8o, and 21.8o, whilethey were 0.1o, 0.2o, and 0.2obetween individual muscle and five-synergy control, respectively.With the fixed two-synergy control, the hip flexion and ankle dorsiflexion angles were outside of1SD of unimpaired gait for 30% and 60% of gait cycle, respectively. The neural excitation effortfor fixed two-synergy control was increased by 50% compared to five-synergy control (Fig. 3).The neural excitation effort of SOL and GAS had large increases of 98% and 93%, while TA hada reduction of 71%.The simulated kinematics with the flexible two-synergy control showed improvement in trackingcompared to the original fixed two-synergy control (RMSE of hip 6.5%, knee 1.3%, and ankle15.7% were reduced compared to the original two-synergy control); however, the ankle stillfailed to track the unimpaired trajectory (Fig. 5). The flexible synergy weights adjusted such that,in the extensor synergy, the weight of TA notably increased while VAS and HAM decreased,and synergy activation during stance phase increased (Fig. 6). A small increase of synergyactivation during swing reduced the ankle tracking error while the total muscle excitation effortincreased.The simulation results showed that synergy activation and muscle neural excitation patternschange during control with a reduced number of synergies, and they may not match the synergyactivation patterns computed by NNMF from model-based neural excitations of unimpaired gait.The optimal synergy activations predicted by the five-synergy control with fixed synergies werehighly correlated with those extracted using NNMF algorithm from model-based neuralexcitations of tracking an unimpaired gait (average Pearson correlation coefficient, p = 0.96).However, there was less correlation between the synergy activations of the two-synergy controland the NNMF-identified two-synergy solution from unimpaired gait. In the two-synergy control,the optimal synergy activation corresponding to the flexor synergy (SOL, GAST, and HFL) hadtwo consecutive bursts that may separately correspond to ankle plantar flexor and hip flexorsynergies. Similarly, the optimal activation of the extensor synergy (TA, VAST, HAM, andGLU) had a large burst of synergy activations in stance and a small burst later in swing that maycorrespond to the hip and knee extensors and ankle dorsiflexion synergies, respectively.DISCUSSION

This study demonstrated that impaired motor control, modeled as a reduced number of synergies,could not accurately track an unimpaired gait pattern. This finding can be used to inform ourunderstanding of how altered synergies may contribute to impaired gait after neurologic injurywith implications for treatment planning. For example, these results suggest that treatments thattarget only orthopaedic problems without considering underlying neurological capacity areunlikely to succeed in producing unimpaired post-treatment gait patterns. Recent research hasindicated that there are minimal changes in synergies after orthopaedic surgery and othertreatments common in cerebral palsy. These treatments also have inconsistent outcomes betweenindividuals (Hicks, et al., 2011). These unsatisfactory outcomes may be tied to the underlyingneuromuscular control strategy that is not addressed by surgical intervention. This simulationframework may provide new tools to identify and optimize individualized treatments targeted atthe neurological impairment to improve walking ability.The simulation results of control with two synergies demonstrated that, with this reduced controlspace, the synergy-based controller failed to accurately track unimpaired gait kinematics,especially at the ankle, and had inferior tracking performance compared to control with fivefixed synergies (Fig. 3). This observation suggests that even if the motor system can optimize theneural commands based on the confined control space, there would be no command that canachieve an unimpaired gait in the two synergies control space. To further test the robustness ofthis conclusion, we also conducted a post-hoc analysis to test additional sets of synergies within thetwo synergies control space. Specifically, we removed the lightly-weighted error term on synergyweights from the objective function with flexible synergies and initiated the optimization with differentsets of random synergy weights. Despite expanding our search space with these changes, we still foundthe two-synergy solution could not track unimpaired kinematics and converged to similar synergyweights and activation profiles. Interestingly, the weights of the two synergy solution were alsosimilar to prior experimental analyses of synergies from rhythmic-stepping in infants, childrenwith cerebral palsy, and adult stroke survivors (Dominici, et al., 2011; Steele, et al., 2015; Clark,et al., 2010). It is plausible that the objective of the human control system is not to create an“unimpaired gait”, but rather to minimize some physiological cost. Further research mightexplore the optimality of the simulated walking patterns with altered synergies in terms of energyconsumption and predict patterns that minimize this cost for subject-specific muscle synergies.The neural excitation effort of tracking unimpaired kinematics in the reduced control spacerepresented by two synergies was largely increased compared to the control space represented byfive synergies or by individual muscles. This may provide an explanation on why it is moredifficult to achieve an unimpaired gait following a neurological injury, and also accords withearlier observations, which showed that energy cost tends to be larger for individuals with aneuromuscular disorder such as CP (Rose, et al., 1990; Steele, et al., 2017) or stroke (Kramer, etal., 2016), or with functional weakness, which may be a consequence of poor motor control (vander Krogt, et al., 2012). However, additional analyses that also incorporate other factors such asweakness or poor balance are needed to understand the mechanisms with which impairedneuromuscular control impacts metabolic energy consumption.The simulation with flexible synergies aimed to investigate the effect of muscle synergyreorganization and neuroplasticity in an impaired low-dimensional control space on trackingperformance and walking patterns. Previous studies evaluating EMGs during walking observedthat the muscle activity patterns and the synergy weights could be changed when thebiomechanical demands of walking were altered (e.g., walking with added trunk load or with aweight support system) (McGowan, et al., 2010; McGowan, et al., 2008). By allowing thesynergy weights in the flexible two-synergy control to change, we deviated from the typicaldefinition of muscle synergy and shifted towards other studies that allow some flexibility insynergy structure and weights (Ivanenko, et al., 2005). However, the simulation results furthersupport the conjecture that there were no muscle synergy configurations within this lowerdimensional control space that could accurately track the unimpaired gait kinematics.It is important to note the limitations of the methods of this research when interpreting thesimulation results. The musculoskeletal model represents the dynamics of walking in the sagittalplane and neglects the contributions of muscle forces to the frontal and transverse planes. Thus,this model cannot capture motor control strategies used by the central nervous system to stabilizethe body in three-dimensions. While two synergies were unable to accurately track unimpairedgait with this simplified model, if greater complexity were added (e.g., more muscles or 3Dkinematics) then three or more synergies may also struggle to accurately track unimpaired gait.We also assumed symmetry between the right and left legs, which may not be a valid assumptionfor individuals with unilateral cerebral palsy or stroke. Determining whether applying impaired synergies for one leg prevents unimpaired walking kinematics remains unknown. The musclesynergies used in this research were identified by applying NNMF to the model-based neuralexcitations predicted by individual muscle control for tracking the kinematics of an unimpairedgait. The identified model-based synergy activations and weights were relatively similar to thoseidentified from EMG in prior studies (Clark, et al., 2010; Ivanenko, et al., 2006), suggesting thesagittal-plane walking model can simulate the neural control requirements of support andprogression during gait. Other factors that may contribute to impaired gait, such as weakness,spasticity, or poor balance were not included in this study to focus on the impacts of alteredsynergies. Examining the impacts of these factors in combination with altered synergies mayprovide greater insight into the mechanisms contributing to impaired gait and increased energeticcost for individuals with neuromuscular disorders.In this investigation, we examined whether unimpaired gait kinematics can be achieved fromlower dimensional control spaces that are commonly observed after neurological injuries. Inaccordance with previous studies (Neptune, et al., 2009; Sartori, et al., 2013), the present resultsdemonstrated that synergy-based control could produce well-coordinated forward walking.However, lower-dimensional control spaces represented with altered muscle synergies could nottrack an unimpaired gait, and resulted in elevated neural exertion levels. This finding suggeststhat if synergies are an underlying neural control mechanism, gait may be impaired due to thereduction of the number of synergies available to the neuromuscular system. Treatments thattarget the underlying neurological capacity to increase the number and complexity of musclessynergies may be required to improve an individual’s walking pattern. For example, the flexiblesynergy controller used in this research could be applied to identify rehabilitation targets that arecustomized to an individual’s unique gait pattern and control strategy. Alternatively, if synergies are challenging to alter, simulation could be used to help identify and guide optimal walkingpatterns for an individual’s neurological capacity. Determining the plasticity of synergies andpotential changes in gait with targeted treatment remain open and important areas for futureresearch.REFERENCES

Ackermann, M. & Van den Bogert, A. J., 2010. Optimality principles for model-based predictionof human gait. Journal of biomechanics, 43(6), pp. 1055-1060. Allen, J. L. & Neptune, R. R., 2012. Three-dimensional modular control of human walking.Journal of biomechanics, 45(12), pp. 2157-2163. Brown, P. & McPhee, J., 2016. A continuous velocity-based friction model for dynamics andcontrol with physically meaningful parameters. ASME Journal of Computational andNonlinear Dynamics, 11(5), pp. 054502-6. Cappellini, G., Ivanenko, Y. P., Poppele, R. E. & Lacquaniti, F., 2006. Motor patterns in humanwalking and running. Journal of neurophysiology, 95(6), pp. 3426-3437. Cheung, V. C. et al., 2012. Muscle synergy patterns as physiological markers of motor corticaldamage. Proceedings of the National Academy of Sciences, 109(36), pp. 14652-14656.Clark, D. J. et al., 2010. Merging of healthy motor modules predicts reduced locomotorperformance and muscle coordination complexity post-stroke. Journal of neurophysiology,103(2), pp. 844-857. Davis, B. L. & Vaughan, C. L., 1993. Phasic behavior of EMG signals during gait: Use ofmultivariate statistics. Journal of Electromyography and Kinesiology, 3(1), pp. 51-60.Dominici, N. et al., 2011. Locomotor primitives in newborn babies and their development.Science, 334(6058), pp. 997-999. Dorn, T. W., Wang, J. M., Hicks, J. L. & Delp, S. L., 2015. Predictive simulation generateshuman adaptations during loaded and inclined walking. PloS one, 10(4), p. e0121407. Fox, E. J. et al., 2013. Modular control of varied locomotor tasks in children with incompletespinal cord injuries. Journal of neurophysiology, 110(6), pp. 1415-1425. Geyer, H. & Herr, H., 2010. A Muscle-Reflex Model That Encodes Principles of LeggedMechanics Produces Human Walking Dynamics and Muscle Activities. IEEE Transactionson Neural Systems and Rehabilitation Engineering, 18(3), pp. 263-273. Hicks, J. L., Delp, S. L. & Schwartz, M. H., 2011. Can biomechanical variables predictimprovement in crouch gait?. Gait & posture, 34(2), pp. 197-201. Ivanenko, Y. P. et al., 2005. Coordination of locomotion with voluntary movements in humans.Journal of Neuroscience, 25(31), pp. 7238-7253. Ivanenko, Y. P. et al., 2013. Plasticity and modular control of locomotor patterns in neurologicaldisorders with motor deficits. Frontiers in computational neuroscience, Volume 7, p. 123. Ivanenko, Y. P., Poppele, R. E. & Lacquaniti, F., 2006. Motor control programs and walking.The Neuroscientist, 12(4), pp. 339-348.Kramer, S., Johnson, L., Bernhardt, J. & Cumming, T., 2016. Energy expenditure and costduring walking after stroke: a systematic review. Archives of physical medicine andrehabilitation, 497(4), pp. 619-632. Lee, D. D. & Seung, H. S., 1999. Learning the parts of objects by non-negative matrixfactorization. Nature, 401(6755), p. 788. Liu, M. Q., Anderson, F. C., Schwartz, M. H. & Delp, S. L., 2008. Muscle contributions tosupport and progression over a range of walking speeds. Journal of biomechanics, 41(15),pp. 3243-3252. McGowan, C. P., Neptune, R. R., Clark, D. J. & Kautz, S. A., 2010. Modular control of humanwalking: adaptations to altered mechanical demands. Journal of biomechanics, 43(3), pp.412-419. McGowan, C. P., Neptune, R. R. & Kram, R., 2008. Independent effects of weight and mass onplantar flexor activity during walking: implications for their contributions to body supportand forward propulsion. Journal of applied physiology, 105(2), pp. 486-494. Meyer, A. J. et al., 2016. Muscle synergies facilitate computational prediction of subject-specificwalking motions. Frontiers in bioengineering and biotechnology, Volume 4, p. 77. Neptune, R. R., Clark, D. J. & Kautz, S. A., 2009. Modular control of human walking: asimulation study. Journal of biomechanics, 42(9), pp. 1282-1287. Roh, J., Rymer, W. Z. & Beer, R. F., 2015. Evidence for altered upper extremity musclesynergies in chronic stroke survivors with mild and moderate impairment. Frontiers inhuman neuroscience, 9(6). Rose, J. et al., 1990. Energy expenditure index of walking for normal children and for childrenwith cerebral palsy. Developmental Medicine & Child Neurology, 32(4), pp. 333-340.Sartori, M., Gizzi, L., Lloyd, D. G. & Farina, D., 2013. A musculoskeletal model of humanlocomotion driven by a low dimensional set of impulsive excitation primitives. Frontiers incomputational neuroscience, Volume 7, p. 79. Steele, K. M., Rozumalski, A. & Schwartz, M. H., 2015. Muscle synergies and complexity ofneuromuscular control during gait in cerebral palsy. Developmental Medicine & ChildNeurology, 57(12), pp. 1176-1182. Steele, K. M., Shuman, B. R. & Schwartz, M. H., 2017. Crouch severity is a poor predictor ofelevated oxygen consumption in cerebral palsy. Journal of biomechanics, Volume 60, pp.170-174. Ting, L. H. & Macpherson, J. M., 2005. A limited set of muscle synergies for force controlduring a postural task. Journal of neurophysiology, 93(1), pp. 609-613. Tresch, M. C., Cheung, V. C. & d'Avella, A., 2006. Matrix factorization algorithms for theidentification of muscle synergies: evaluation on simulated and experimental data sets.Journal of neurophysiology, 98(4), pp. 2199-2212. Tresch, M. C., Saltiel, P. & Bizzi, E., 1999. The construction of movement by the spinal cord.Nature neuroscience, 2(2), pp. 162-167. Umberger, B. R., 2010. Stance and swing phase costs in human walking. Journal of the RoyalSociety Interface, 7(50), pp. 1329-1340. van der Krogt, M. M., Delp, S. L. & Schwartz, M. H., 2012. How robust is human gait to muscleweakness?. Gait & posture, 36(1), pp. 113-119. Wchter, A. & Biegler, L. T., 2006. On the implementation of an interior-point filter line-searchalgorithm for large-scale nonlinear programming. Mathematical programming, 106(1), pp.Walter, J. P. et al., 2014. Muscle synergies may improve optimization prediction of knee contactforces during walking. Journal of biomechanical engineering, 136(2), p. 021031. Winter, D. A., 1987. The biomechanics and motor control of human gait. s.l.:University ofWaterloo Press.

微信字数限制无法显示全文

扫描二维码查看源文（附PDF下载）

◎声明：本站所提供内容均来源于网友提供或网络搜集，由本站编辑整理，仅供个人研究、交流学习使用，不涉及商业盈利目的。如涉及版权问题，请联系本站管理员予以更改或删除。

投稿邮箱：duanxinyu@ctth.net   期待您的来稿！

         微信公众号  ：Sports_training


长按二维码 加入神经肌电学术交流圈

谢谢楼主