Control of Muscle Synergies by Cortical Ensembles

  • Michelle M. Morrow
  • Eric A. Pohlmeyer
  • Lee E. Miller
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 629)


Since its introduction in the early 1980s, the concept of a “preferred direction” for neuronal discharge has proven to be a powerful means of studying diverse properties of individual neurons in the motor areas of the brain. More recently, the activity recorded from ensembles of neurons, each with an identified preferred direction, has been used to predict hand movement, both off-line, and in real-time. Our recent experiments have addressed similar issues, but have focused on the relation between primary motor cortical discharge and muscle activity, rather than limb kinematics.

We recently introduced the concept of a “muscle-space” preferred direction (PDM), that is analogous to the familiar hand-space preferred direction (PDH). In this manuscript, we show that there is considerable variety in the direction of these PDM vectors across neurons, but that for a given task and neuron, two successive measurements of PDM are very similar. We found that these vectors tend to form clusters in particular regions of the muscle space that may reflect neurons that control synergistically important groups of muscles.

We have also shown that the discharge measured from neural ensembles can be used to predict the activity of individual muscles, in much the way that kinematic signals have been predicted by other groups. In fact, the accuracy of these predictions is similar to that of kinematic signals, despite the stochastic nature and greater bandwidth of the EMG signals. PDMs represent a divergence from one neuron to numerous muscles, while the prediction of muscle activity represents convergence from many neurons to individual muscles. We are continuing to investigate the nature of this complex matrix of functional interconnections.


Hand Position Neuronal Discharge Intrinsic Hand Muscle Precision Grasp Kinematic Signal 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Alstermark, B., & Lundberg, A. (1992). The C3-C4 propriospinal system: target reaching and food taking. In l. Jami, E. Pierrot-Deseilligny & D. Zytnicki (Eds.), Muscle afferents and spinal control of movement (pp. 327–354). London: Pergamon Press.Google Scholar
  2. Armand, J. (1982). The origin, course and terminations of corticospinal fibers in various mammals. Prog Brain Res, 57, 329–360.PubMedCrossRefGoogle Scholar
  3. Ashe, J., & Georgopoulos, A. P. (1994). Movement parameters and neural activity in motor cortex and area 5. Cerebral Cortex, 6, 590–600.CrossRefGoogle Scholar
  4. Baker, S. N., & Lemon, R. N. (1998). Computer simulation of post-spike facilitation in spike-triggered averages of rectified EMG. J Neurophysiol, 80(3), 1391–1406.PubMedGoogle Scholar
  5. Bizzi, E., Giszter, S. F., Loeb, E., Mussa-Ivaldi, F. A., & Saltiel, P. (1995). Modular organization of motor behavior in the frog's spinal cord. Trends Neurosci, 18, 442–446.PubMedCrossRefGoogle Scholar
  6. Cabel, D. W., Cisek, P., & Scott, S. H. (2001). Neural activity in primary motor cortex related to mechanical loads applied to the shoulder and elbow during a postural task. J Neurophysiol, 86(4), 2102–2108.PubMedGoogle Scholar
  7. Caminiti, R., Johnson, P. B., & Urbano, A. (1990). Making arm movements within different parts of space: Dynamic aspects in the primate motor cortex. J Neurosci, 10, 2039–2058.PubMedGoogle Scholar
  8. Carmena, J. M., Lebedev, M. A., Crist, R. E., O'Doherty, J. E., Santucci, D. M., Dimitrov, D., et al. (2003). Learning to Control a Brain-Machine Interface for Reaching and Grasping by Primates. PLoS Biol, 1(2), 193–208.CrossRefGoogle Scholar
  9. Chapin, J. K., Moxon, K. A., Markowitz, R. S., & Nicolelis, M. A. (1999). Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex. Nat Neurosci, 2(7), 664–670.PubMedCrossRefGoogle Scholar
  10. d'Avella, A., & Bizzi, E. (2005). Shared and specific muscle synergies in natural motor behaviors. Proc Natl Acad Sci USA, 102(8), 3076–3081.PubMedCrossRefGoogle Scholar
  11. Evarts, E. V. (1968). Relation of pyramidal tract activity to force exerted during voluntary movement. J Neurophysiol, 31, 14–27.PubMedGoogle Scholar
  12. Fabiani, G. E., McFarland, D. J., Wolpaw, J. R., & Pfurtscheller, G. (2004). Conversion of EEG activity into cursor movement by a brain-computer interface (BCI). IEEE Trans Neural Syst Rehabil Eng, 12(3), 331–338.PubMedCrossRefGoogle Scholar
  13. Fetz, E. E., & Cheney, P. D. (1980). Postspike facilitation of forelimb muscle activity by primate corticomotoneuronal cells. J Neurophysiol, 44, 751–772.PubMedGoogle Scholar
  14. Fetz, E. E., Cheney, P. D., & German, D. C. (1976). Corticomotoneuronal connections of precentral cells detected by post-spike averages of EMG activity in behaving monkeys. Brain Res, 114, 505–510.PubMedCrossRefGoogle Scholar
  15. Georgopoulos, A. P. (1988). Neural integration of movement: Role of motor cortex in reaching. FASEB J, 2, 2849–2857.PubMedGoogle Scholar
  16. Georgopoulos, A. P. (1995). Current issues in directional motor control. Trends Neurosci, 18, 506–510.PubMedCrossRefGoogle Scholar
  17. Georgopoulos, A. P., Kalaska, J. F., Caminiti, R., & Massey, J. T. (1982). On the relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex. J Neurosci, 2, 1527–1537.PubMedGoogle Scholar
  18. Georgopoulos, A. P., Schwartz, A. B., & Kettner, R. E. (1986). Neuronal population coding of movement direction. Science, 233, 1416–1419.PubMedCrossRefGoogle Scholar
  19. Hochberg, L. R., Serruya, M. D., Friehs, G. M., Mukand, J. A., Saleh, M., Caplan, A. H., et al. (2006). Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature, 442(7099), 164–171.PubMedCrossRefGoogle Scholar
  20. Holdefer, R. N., & Miller, L. E. (2002). Primary motor cortical neurons encode functional muscle synergies. Exp Brain Res, 146, 233–243.PubMedCrossRefGoogle Scholar
  21. Hunter, I. W., & Korenberg, M. J. (1986). The identification of nonlinear biological systems: Wiener and Hammerstein cascade models. Biol Cybern, 55(2–3), 135–144.PubMedGoogle Scholar
  22. Johnson, M. T. V., Coltz, J. D., & Ebner, T. J. (1999). Encoding of target direction and speed during visual instruction and arm tracking in dorsal premotor and primary motor cortical neurons. Eur J Neurosci, 11(12), 4433–4445.PubMedCrossRefGoogle Scholar
  23. Kakei, S., Hoffman, D. S., & Strick, P. L. (1999). Muscle and movement representations in the primary motor cortex. Science, 285(5436), 2136–2139.PubMedCrossRefGoogle Scholar
  24. Kakei, S., Hoffman, D. S., & Strick, P. L. (2001). Direction of action is represented in the ventral premotor cortex. Nat Neurosci, 4(10), 1020–1025.PubMedCrossRefGoogle Scholar
  25. Kalaska, J. F., Cohon, D. A. D., Hyde, M. L., & Prud'homme, M. (1989). A comparison of movement direction-related versus load direction-related activity in primate motor cortex, using a two-dimensional reaching task. J Neurosci, 9, 2080–2102.PubMedGoogle Scholar
  26. Kennedy, P. R., & Bakay, R. A. (1998). Restoration of neural output from a paralyzed patient by a direct brain connection. Neuroreport, 9(8), 1707–1711.PubMedCrossRefGoogle Scholar
  27. Kuypers, H. G. J. M. (1981). Anatomy of the descending pathways. In J.M. Brookhart V.B. Mountcastle V.B. Brooks S.R. Geiger (Ed.), Handbook of Physiology, The Nervous System: American Physiological Society Bethesda.Google Scholar
  28. Lamarre, Y., Spidalieri, G., & Lund, J. P. (1981). Patterns of muscular and motor cortical activity during a simple arm movement in the monkey. Can J Physiol Pharmacol, 59, 748–756.PubMedGoogle Scholar
  29. Li, C.-S. R., Padoa-Schioppa, C., & Bizzi, E. (2001). Neuronal correlates of motor performance and motor learning in the primary motor cortex of monkeys adapting to an external force field. Neuron, 30, 593–607.PubMedCrossRefGoogle Scholar
  30. McKiernan, B. J., Marcario, J. K., Karrer, J. H., & Cheney, P. D. (1998). Corticomotoneuronal postspike effects in shoulder, elbow, wrist, digit, and intrinsic hand muscles during a reach and prehension task. J Neurophysiol, 80(4), 1961–1980.PubMedGoogle Scholar
  31. McKiernan, B. J., Marcario, J. K., Karrer, J. H., & Cheney, P. D. (2000). Correlations between corticomotoneuronal (CM) cell postspike effects and cell-target muscle covariation. J Neurophysiol, 83(1), 99–115.PubMedGoogle Scholar
  32. Miller, L. E., & Sinkjaer, T. (1998). Primate red nucleus discharge encodes the dynamics of limb muscle activity. J Neurophysiol, 80, 59–70.PubMedGoogle Scholar
  33. Miller, L. E., van Kan, P. L. E., Sinkjaer, T., Andersen, T., Harris, G. D., & Houk, J. C. (1993). Correlation of primate red nucleus discharge with muscle activity during free-form arm movements. J Physiol London, 469, 213–243.PubMedGoogle Scholar
  34. Moran, D. W., & Schwartz, A. B. (1999a). Motor cortical activity during drawing movements: Population representation during spiral tracing. J Neurophysiol, 82(5), 2693–2704.Google Scholar
  35. Moran, D. W., & Schwartz, A. B. (1999b). Motor cortical representation of speed and direction during reaching. J Neurophysiol, 82(5), 2676–2692.Google Scholar
  36. Morrow, M. M., & Miller, L. E. (2003). Representation of kinematic variables and muscle activity patterns in M1 discharge across two workspaces.: Society for Neuroscience.Google Scholar
  37. Mussa-Ivaldi, F. A., & Bizzi, E. (2000). Motor learning through the combination of primitives. Philos Trans R Soc Lond B Biol Sci, 355(1404), 1755–1769.PubMedCrossRefGoogle Scholar
  38. Paninski, L., Fellows, M. R., Hatsopoulos, N. G., & Donoghue, J. P. (2004). Spatiotemporal tuning of motor cortical neurons for hand position and velocity. J Neurophysiol, 91(1), 515–532.PubMedCrossRefGoogle Scholar
  39. Poliakov, A. V., & Schieber, M. H. (1998). Multiple fragment statistical analysis of post-spike effects in spike-triggered averages of rectified EMG. J Neurosci Methods, 79(2), 143–150.PubMedCrossRefGoogle Scholar
  40. Saltiel, P., Wyler-Duda, K., D'Avella, A., Tresch, M. C., & Bizzi, E. (2001). Muscle synergies encoded within the spinal cord: Evidence from focal intraspinal NMDA iontophoresis in the frog. J Neurophysiol, 85(2), 605–619.PubMedGoogle Scholar
  41. Schwartz, A. B., Kettner, R. E., & Georgopoulos, A. P. (1988). Primate motor cortex and free arm movements to visual targets in three-dimensional space. I. Relations between single cell discharge and direction of movement. J Neurosci, 8(8), 2913–2927.PubMedGoogle Scholar
  42. Scott, S. H., & Kalaska, J. F. (1997). Reaching movements with similar hand paths but different arm orientations. I. Activity of individual cells in motor cortex. J Neurophysiol, 77, 826–852.PubMedGoogle Scholar
  43. Serruya, M. D., Hatsopoulos, N. G., Paninski, L., Fellows, M. R., & Donoghue, J. P. (2002). Instant neural control of a movement signal. Nature, 416(6877), 141–142.PubMedCrossRefGoogle Scholar
  44. Shah, A., Fagg, A. H., & Barto, A. G. (2004). Cortical involvement in the recruitment of wrist muscles. J Neurophysiol, 91(6), 2445–2456.PubMedCrossRefGoogle Scholar
  45. Shen, L., & Alexander, G. (1997). Neural correlates of a spatial sensory-to-motor transformation in primary motor cortex. J Neurophysiol, 77, 1171–1194.PubMedGoogle Scholar
  46. Shinoda, Y., Yamaguchi, T., & Futami, T. (1986). Multiple axon collaterals of single corticospinal axons in the cat spinal cord. J Neurophysiol, 55, 425–448.PubMedGoogle Scholar
  47. Shinoda, Y., Yokota, J., & Futami, T. (1981). Divergent projection of individual corticospinal axons to motoneurons of multiple muscles in the monkey. Neurosci Lett, 23(1), 7–12.PubMedCrossRefGoogle Scholar
  48. Soechting, J. F., Burton, J. E., & Onoda, N. (1978). Relationships between sensory input, motor output and unit activity in interpositus and red nuclei during intentional movement. Brain Res, 152, 65–79.PubMedCrossRefGoogle Scholar
  49. Taylor, D. M., Tillery, S. I., & Schwartz, A. B. (2002). Direct cortical control of 3D neuroprosthetic devices. Science, 296(5574), 1829–1832.PubMedCrossRefGoogle Scholar
  50. Thach, W. T. (1978). Correlation of neural discharge with pattern and force of muscular activity, joint position, and direction of next movement in motor cortex and cerebellum. J Neurophysiol, 41, 654–676.PubMedGoogle Scholar
  51. Tresch, M. C., & Bizzi, E. (1999). Responses to spinal microstimulation in the chronically spinalized rat and their relationship to spinal systems activated by low threshold cutaneous stimulation. Exp Brain Res, 129(3), 401–416.PubMedCrossRefGoogle Scholar
  52. Wessberg, J., Stambaugh, C. R., Kralik, J. D., Beck, P. D., Laubach, M., Chapin, J. K., et al. (2000). Real-time prediction of hand trajectory by ensembles of cortical neurons in primates. Nature, 408(6810), 361–365.PubMedCrossRefGoogle Scholar
  53. Westwick, D. T., Pohlmeyer, E. A., Solla, S. A., Miller, L. E., & Perreault, E. J. (2006). Identification of multiple-input systems with highly coupled inputs: Application to EMG prediction from multiple intracortical electrodes. Neural Comput, 18(2), 329–355.PubMedCrossRefGoogle Scholar
  54. Wolpaw, J. R., & McFarland, D. J. (2004). Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. Proc Natl Acad Sci USA, 101(51), 17849–17854.PubMedCrossRefGoogle Scholar
  55. Wu, W., Gao, Y., Bienenstock, E., Donoghue, J. P., & Black, M. J. (2006). Bayesian population decoding of motor cortical activity using a Kalman filter. Neural Comput, 18(1), 80–118.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Michelle M. Morrow
    • 1
  • Eric A. Pohlmeyer
  • Lee E. Miller
  1. 1.Center for the Neural Basis of CognitionUniversity of PittsburghPittsburgh PA 15261

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