The case for and against muscle synergies

Current Opinion in Neurobiology

Volumn 19, Issue 6, 2009, Pages 601-607

  Tresch, Matthew C ^a,b   Jarc, Anthony ^a  

^a Northwestern University   (United States)

^b NORTHWESTERN UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BODY MOVEMENT; CENTRAL NERVOUS SYSTEM FUNCTION; ELECTROMYOGRAM; EVOKED MUSCLE RESPONSE; HUMAN; LOCOMOTION; MOTOR ACTIVITY; MOTOR CONTROL; MUSCLE ACTION POTENTIAL; MUSCLE FUNCTION; MUSCLE REFLEX; NEUROPHYSIOLOGICAL RECRUITMENT; NONHUMAN; PRIORITY JOURNAL; REVIEW; SENSORIMOTOR FUNCTION;

ANIMALS; BIOMECHANICS; CENTRAL NERVOUS SYSTEM; HUMANS; MOTOR NEURONS; MOVEMENT; MUSCLE, SKELETAL;

EID: 72549112263     PISSN: 09594388     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.conb.2009.09.002     Document Type: Review

References (73)

1. Sherrington C.S. Flexion-reflex of the limb, crossed extension-reflex, and reflex stepping and standing. J Physiol 40 (1910) 28-121
2. Jordan L. Brainstem and spinal cord mechanisms for the initiation of locomotion. In: Shimamura M., Grillner S., and Edgerton V.R. (Eds). Neurological Basis of Human Locomotion (1991), Japan Scientific Societies Press 3-20
3. Grillner S. Neurobiological bases of rhythmic motor acts in vertebrates. Science 228 (1985) 143-149
4. Ting L.H., and McKay J.L. Neuromechanics of muscle synergies for posture and movement. Curr Opin Neurobiol 17 (2007) 622-628
5. Ting L.H. Dimensional reduction in sensorimotor systems: a framework for understanding muscle coordination of posture. Prog Brain Res 165 (2007) 299-321
6. Stein P.S. Motor pattern deletions and modular organization of turtle spinal cord. Brain Res Rev 57 (2008) 118-124
7. Lee W.A. Neuromotor synergies as a basis for coordinated intentional action. J Mot Behav 16 (1984) 135-170
8. Giszter S., Patil V., and Hart C. Primitives, premotor drives, and pattern generation: a combined computational and neuroethological perspective. Prog Brain Res 165 (2007) 323-346
9. Macpherson J.M. How flexible are muscle synergies?. In: Humphrey D.R., and Freund H.-J. (Eds). Motor Control: Concepts and Issues (1991), John Wiley and Sons Ltd 33-47
10. Tresch M.C., Saltiel P., d'Avella A., and Bizzi E. Coordination and localization in spinal motor systems. Brain Res Brain Res Rev 40 (2002) 66-79
11. Bizzi E., d'Avella A., Saltiel P., and Tresch M. Modular organization of spinal motor systems. Neuroscientist 8 (2002) 437-442
12. Gentner R., and Classen J. Modular organization of finger movements by the human central nervous system. Neuron 52 (2006) 731-742
13. McCrea D.A., and Rybak I.A. Organization of mammalian locomotor rhythm and pattern generation. Brain Res Rev 57 (2008) 134-146
14. d'Avella A., and Bizzi E. Shared and specific muscle synergies in natural motor behaviors. Proc Natl Acad Sci U S A 102 (2005) 3076-3081
15. Tresch M.C., Saltiel P., and Bizzi E. The construction of movement by the spinal cord. Nat Neurosci 2 (1999) 162-167
16. Ting L.H., and Macpherson J.M. A limited set of muscle synergies for force control during a postural task. J Neurophysiol 93 (2005) 609-613
17. d'Avella A., Saltiel P., and Bizzi E. Combinations of muscle synergies in the construction of a natural motor behavior. Nat Neurosci 6 (2003) 300-308
18. Todorov E., Li W., and Pan X. From task parameters to motor synergies: a hierarchical framework for approximately-optimal control of redundant manipulators. J Robot Syst 22 (2005) 691-710
19. Chhabra M., and Jacobs R.A. Properties of synergies arising from a theory of optimal motor behavior. Neural Comput 18 (2006) 2320-2342
20. Todorov E., and Ghahramani Z. Unsupervised Learning of Sensory-Motor Primitives (2003), IEEE Engineering in Medicine and Biology Society 1750-1753
21. Sanger T.D. Optimal unsupervised motor learning for dimensionality reduction of nonlinear control systems. IEEE Trans Neural Netw 5 (1994) 965-973
22. Loeb G.E., Brown I.E., and Cheng E.J. A hierarchical foundation for models of sensorimotor control. Exp Brain Res 126 (1999) 1-18
23. Lockhart D.B., and Ting L.H. Optimal sensorimotor transformations for balance. Nat Neurosci 10 (2007) 1329-1336
24. Hart C.B., and Giszter S.F. Modular premotor drives and unit bursts as primitives for frog motor behaviors. J Neurosci 24 (2004) 5269-5282
25. Dipietro L., Krebs H.I., Fasoli S.E., Volpe B.T., Stein J., Bever C., and Hogan N. Changing motor synergies in chronic stroke. J Neurophysiol 98 (2007) 757-768
26. Cruz T.H., and Dhaher Y.Y. Evidence of abnormal lower-limb torque coupling after stroke: an isometric study. Stroke 39 (2008) 139-147
27. Drew T., Kalaska J., and Krouchev N. Muscle synergies during locomotion in the cat: a model for motor cortex control. J Physiol 586 (2008) 1239-1245
28. Cheung V.C., d'Avella A., Tresch M.C., and Bizzi E. Central and sensory contributions to the activation and organization of muscle synergies during natural motor behaviors. J Neurosci 25 (2005) 6419-6434
29. d'Avella A., Portone A., Fernandez L., and Lacquaniti F. Control of fast-reaching movements by muscle synergy combinations. J Neurosci 26 (2006) 7791-7810
30. Ivanenko Y.P., Cappellini G., Dominici N., Poppele R.E., and Lacquaniti F. Modular control of limb movements during human locomotion. J Neurosci 27 (2007) 11149-11161
31. Merkle L.A., Layne C.S., Bloomberg J.J., and Zhang J.J. Using factor analysis to identify neuromuscular synergies during treadmill walking. J Neurosci Methods 82 (1998) 207-214
32. Olree K.S., and Vaughan C.L. Fundamental patterns of bilateral muscle activity in human locomotion. Biol Cybern 73 (1995) 409-414
33. Patla A.E. Some characteristics of EMG patterns during locomotion: implications for the locomotor control process. J Mot Behav 17 (1985) 443-461
34. Soechting J.F., and Lacquaniti F. An assessment of the existence of muscle synergies during load perturbations and intentional movements of the human arm. Exp Brain Res 74 (1989) 535-548
35. Torres-Oviedo G., and Ting L.H. Muscle synergies characterizing human postural responses. J Neurophysiol 98 (2007) 2144-2156
36. Torres-Oviedo G., Macpherson J.M., and Ting L.H. Muscle synergy organization is robust across a variety of postural perturbations. J Neurophysiol 96 (2006) 1530-1546
37. Weiss E.J., and Flanders M. Muscular and postural synergies of the human hand. J Neurophysiol 92 (2004) 523-535
38. Kargo W.J., and Giszter S.F. Rapid correction of aimed movements by summation of force-field primitives. J Neurosci 20 (2000) 409-426
39. Krouchev N., Kalaska J.F., and Drew T. Sequential activation of muscle synergies during locomotion in the intact cat as revealed by cluster analysis and direct decomposition. J Neurophysiol 96 (2006) 1991-2010
40. Kutch J.J., Kuo A.D., Bloch A.M., and Rymer W.Z. Endpoint force fluctuations reveal flexible rather than synergistic patterns of muscle cooperation. J Neurophysiol 100 (2008) 2455-2471. A very clever study that uses a subtle analysis of force variability to examine muscle recruitment properties. The authors exploit the signal-dependent noise characteristics of the motor system to estimate muscle activations during the production of very small fingertip forces. By examining the shape of the variability for different directions of force, they are able to estimate the underlying muscle recruitments. Their results suggest an independent recruitment of individual muscles within this task, rather than control of muscle synergies.
41. Kurtzer I., Pruszynski J.A., Herter T.M., and Scott S.H. Primate upper limb muscles exhibit activity patterns that differ from their anatomical action during a postural task. J Neurophysiol 95 (2006) 493-504
42. Valero-Cuevas F.J., Venkadesan M., and Todorov E. Structured variability of muscle activations supports the minimal intervention principle of motor control. J Neurophysiol 102 (2009) 59-68. This study provides direct evidence for the minimum intervention principle at the level of individual muscles. The authors examine the activation of every major muscle contributing to index finger force during a force tracking task and examine the within trial, moment to moment, variability in muscle activations. Since the task is isometric and they can record all contributing muscles, they are able to directly estimate the task relevant and task irrelevant variance in the EMGs. They show that subjects allow more task irrelevant variance, consistent with the minimum intervention principle. They also find no clear evidence supporting the existence of muscle synergies in the EMG variability.
43. Bunderson N.E., Burkholder T.J., and Ting L.H. Reduction of neuromuscular redundancy for postural force generation using an intrinsic stability criterion. J Biomech 41 (2008) 1537-1544
44. d'Avella A., Fernandez L., Portone A., and Lacquaniti F. Modulation of phasic and tonic muscle synergies with reaching direction and speed. J Neurophysiol 100 (2008) 1433-1454. A robust demonstration of the ability of muscle synergies to explain a wide range of behaviors. The authors use time-varying synergies to explain the muscle activations observed during reaching in humans, across different targets and different speeds. They show that this wide range of behavior can be well described as combination of muscle synergies.
45. Perreault E.J., Chen K., Trumbower R.D., and Lewis G. Interactions with compliant loads alter stretch reflex gains but not intermuscular coordination. J Neurophysiol 99 (2008) 2101-2113. The authors examine the organization of reflexes involved in postural stabilization in both stiff and compliant environments. They show that people modulate their long latency reflexes systematically to the direction of the perturbation and that these reflexes are altered between the different environments. However, they show that the coordination patterns (or muscle synergies) underlying these changes are very similar between the two environments. These results are surprising since previous work suggested that there should be a differential regulation of biarticular muscles across the two environments.
46. Overduin S.A., d'Avella A., Roh J., and Bizzi E. Modulation of muscle synergy recruitment in primate grasping. J Neurosci 28 (2008) 880-892
47. Cheung V.C., d'Avella A., and Bizzi E. Adjustments of motor pattern for load compensation via modulated activations of muscle synergies during natural behaviors. J Neurophysiol 101 (2009) 1235-1257
48. Kargo W.J., and Giszter S.F. Individual premotor drive pulses, not time-varying synergies, are the units of adjustment for limb trajectories constructed in spinal cord. J Neurosci 28 (2008) 2409-2425. The authors perform a very nice analysis of the effects of muscle spindle afferents on muscle activations during wiping in the frog. They show clearly that sets of muscles vary as a group, both in their timing and in the amplitudes, as a consequence of this spindle stimulation. They further use their results to argue for 'synchronous' synergies in the control of this wiping behavior, rather than time-varying synergies.
49. Todorov E., and Jordan M.I. Optimal feedback control as a theory of motor coordination. Nat Neurosci 5 (2002) 1226-1235
50. Latash M.L., Scholz J.P., and Schoner G. Toward a new theory of motor synergies. Motor Control 11 (2007) 276-308
51. Scholz J.P., and Schoner G. The uncontrolled manifold concept: identifying control variables for a functional task. Exp Brain Res 126 (1999) 289-306
52. Liu D., and Todorov E. Evidence for the flexible sensorimotor strategies predicted by optimal feedback control. J Neurosci 27 (2007) 9354-9368
53. Krishnamoorthy V., Latash M.L., Scholz J.P., and Zatsiorsky V.M. Muscle modes during shifts of the center of pressure by standing persons: effect of instability and additional support. Exp Brain Res 157 (2004) 18-31
54. Danna-Dos-Santos A., Shapkova E.Y., Shapkova A.L., Degani A.M., and Latash M.L. Postural control during upper body locomotor-like movements: similar synergies based on dissimilar muscle modes. Exp Brain Res 193 (2009) 565-579
55. Berniker M., Jarc A., Bizzi E., and Tresch M.C. Simplified and effective motor control based on muscle synergies to exploit musculoskeletal dynamics. Proc Natl Acad Sci U S A 106 (2009) 7601-7606. The authors propose a specific principle underlying the specification of muscle synergies: that they should be chosen as those which most effectively control the natural dynamics of the limb. They used techniques from nonlinear model order reduction to identify a set of muscle synergies for the frog hindlimb. They then showed that control of the limb using these synergies was very similar to that when using individual muscles, demonstrating that muscle synergies allow for effective and efficient control.
56. Neptune R.R., Clark D.J., and Kautz S.A. Modular control of human walking: a simulation study. J Biomech 42 (2009) 1282-1287. A nice demonstration of the sufficiency of muscle synergies to allow effective control. The authors first identified muscle synergies in humans during locomotion. They then used these synergies and the timing of their activations, with small adjustments, to drive a complex musculoskeletal simulation of the human leg during locomotion. Surprisingly, they found that this synergy based control allowed for effective locomotion with only minor adjustments.
57. Kargo W.J., Ramakrishnan A., Hart C.B., Rome L.C., and Giszter S.F. A simple experimentally-based model using proprioceptive regulation of motor primitives captures adjusted trajectory formation in spinal frogs. J Neurophysiol (2009) http://jn.physiology.org/cgi/reprint/01054.2007v1. The authors perform a simulation analysis based on a musculoskeletal model to demonstrate that muscle synergies can be successfully used to produce experimentally observed wiping behaviors in the frog. They find a set of muscles which reproduce each of the force fields observed experimentally during wiping, then use these muscle sets to produce wiping behaviors. They demonstrate that sequencing these synergies appropriately results in wiping trajectories which are very similar to those observed experimentally. Further, they suggest a relatively simple regulation of synergy activation based on afferent feedback to reproduce the effects of limb starting position on limb trajectories.
58. Raasch C.C., and Zajac F.E. Locomotor strategy for pedaling: muscle groups and biomechanical functions. J Neurophysiol 82 (1999) 515-525
59. Morrow M.M., Pohlmeyer E.A., and Miller L.E. Control of muscle synergies by cortical ensembles. Adv Exp Med Biol 629 (2009) 179-199
60. Jankowska E. Interneuronal relay in spinal pathways from proprioceptors. Prog Neurobiol 38 (1992) 335-378
61. Tantisira B., Alstermark B., Isa T., Kummel H., and Pinter M. Motoneuronal projection pattern of single C3-C4 propriospinal neurones. Can J Physiol Pharmacol 74 (1996) 518-530
62. Perlmutter S.I., Maier M.A., and Fetz E.E. Activity of spinal interneurons and their effects on forearm muscles during voluntary wrist movements in the monkey. J Neurophysiol 80 (1998) 2475-2494
63. Simoncelli E.P., and Olshausen B.A. Natural image statistics and neural representation. Annu Rev Neurosci 24 (2001) 1193-1216
64. Lewicki M.S., and Sejnowski T.J. Learning overcomplete representations. Neural Comput 12 (2000) 337-365
65. Olshausen B.A., and Field D.J. Sparse coding with an overcomplete basis set: a strategy employed by V1?. Vision Res 37 (1997) 3311-3325
66. Fetz E.E. Volitional control of neural activity: implications for brain-computer interfaces. J Physiol 579 (2007) 571-579
67. Moritz C.T., Perlmutter S.I., and Fetz E.E. Direct control of paralysed muscles by cortical neurons. Nature 456 (2008) 639-642
68. Ganguly K., and Carmena J.M. Emergence of a stable cortical map for neuroprosthetic control. PLoS Biol 7 (2009) e1000153
69. Ajiboye A.B., and Weir R.F. Muscle synergies as a predictive framework for the EMG patterns of new hand postures. J Neural Eng 6 (2009) 036004
70. McKay J.L., and Ting L.H. Functional muscle synergies constrain force production during postural tasks. J Biomech 41 (2008) 299-306
71. Tresch M.C., Cheung V.C., and d'Avella A. Matrix factorization algorithms for the identification of muscle synergies: evaluation on simulated and experimental data sets. J Neurophysiol 95 (2006) 2199-2212
72. Pfeifer R., Lungarella M., and Iida F. Self-organization, embodiment, and biologically inspired robotics. Science 318 (2007) 1088-1093. The authors review how the interactions of the controller, body, and environment must be examined to fully understand behavior. They argue that the control problem is distributed across all aspects of the system. They argue that the field of robotics can be improved by replicating such principles derived from nature.
73. Smeets J.B., and van der Gon J.J. An unsupervised neural network model for the development of reflex co-ordination. Biol Cybern 70 (1994) 417-425