Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Overlap of internal models in motor cortex for mechanical loads during reaching

Abstract

A hallmark of the human motor system is its ability to adapt motor patterns for different environmental conditions, such as when a skilled ice-hockey player accurately shoots a puck with or without protective equipment. Each object (stick, shoulder pad, elbow pad) imparts a distinct load upon the limb, and a key problem in motor neuroscience is to understand how the brain controls movement for different mechanical contexts1,2. We addressed this issue by training non-human primates to make reaching movements with and without viscous loads applied to the shoulder and/or elbow joints, and then examined neural representations in primary motor cortex (MI) for each load condition. Even though the shoulder and elbow loads are mechanically independent, we found that some neurons responded to both of these single-joint loads. Furthermore, changes in activity of individual neurons during multi-joint loads could be predicted from their response to subordinate single-joint loads. These findings suggest that neural representations of different mechanical contexts in MI are organized in a highly structured manner that may provide a neural basis for how complex motor behaviour is learned from simpler motor tasks.

Your institute does not have access to this article

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Figure 1: Two hypotheses about the neural control of different mechanical loads. A single-controller that encapsulates all load contexts (a), or multiple controllers, each of which represent individual loads (b).
Figure 2: Neural activity of two cells during movement in different dynamic loads.
Figure 3: Changes in cell activity across three load conditions.
Figure 4: Neural representation of mechanically dependent loads.

References

  1. Wolpert, D. M. & Ghahramani, Z. Computational principles of movement neuroscience. Nature Neurosci. 3 suppl., 1212–1217 (2000)

    CAS  Article  Google Scholar 

  2. Wolpert, D. M. & Kawato, M. Multiple paired forward and inverse models for motor control. Neural Netw. 11, 1317–1329 (1998)

    CAS  Article  Google Scholar 

  3. Shadmehr, R. & Mussa-Ivaldi, F. A. Adaptive representation of dynamics during learning of a motor task. J. Neurosci. 14, 3208–3224 (1994)

    CAS  Article  Google Scholar 

  4. Kalaska, J. F., Cohen, D. A., Hyde, M. L. & Prud'homme, M. 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 (1989)

    CAS  Article  Google Scholar 

  5. Gandolfo, F., Li, C., Benda, B. J., Schioppa, C. P. & Bizzi, E. Cortical correlates of learning in monkeys adapting to a new dynamical environment. Proc. Natl Acad. Sci. USA 97, 2259–2263 (2000)

    ADS  CAS  Article  Google Scholar 

  6. Kalaska, J. F., Scott, S. H., Cisek, P. & Sergio, L. E. Cortical control of reaching movements. Curr. Opin. Neurobiol. 7, 849–859 (1997)

    CAS  Article  Google Scholar 

  7. Porter, R. & Lemon, R. Corticospinal Function and Voluntary Movement (Oxford Univ. Press, Oxford, 1995)

    Book  Google Scholar 

  8. Ashe, J. Force and the motor cortex. Behav. Brain Res. 87, 255–269 (1997)

    CAS  Article  Google Scholar 

  9. Scott, S. H. Apparatus for measuring and perturbing shoulder and elbow joint positions and torques during reaching. J. Neurosci. Methods 89, 119–127 (1999)

    CAS  Article  Google Scholar 

  10. Scott, S. H., Gribble, P. L., Graham, K. M. & Cabel, D. W. Dissociation between hand motion and population vectors from neural activity in motor cortex. Nature 413, 161–165 (2001)

    ADS  CAS  Article  Google Scholar 

  11. Humphrey, D. R. & Reed, D. J. in Motor Control Mechanisms in Health and Disease Advances in Neurology no. 39 (ed. Desmedt, J.) 347–372 (Raven, New York, 1983)

    Google Scholar 

  12. Fetz, E. E., Cheney, P. D., Mewes, K. & Palmer, S. in Peripheral Control of Posture and Locomotion (eds Allum, J. A. H. & Hulliger, M.) 437–449 (Elsevier, New York, 1989)

    Book  Google Scholar 

  13. Cabel, D. W., Cisek, P. & Scott, S. H. Neural activity in primary motor cortex related to mechanical loads applied to the shoulder and elbow during a postural task. J. Neurophysiol. 86, 2102–2108 (2001)

    CAS  Article  Google Scholar 

  14. Sanes, J. N. & Schieber, M. H. Orderly somatotopy in primary motor cortex: does it exist? Neuroimage 13, 968–974 (2001)

    CAS  Article  Google Scholar 

  15. McKiernan, B. J., Marcario, J. K., Karrer, J. H. & Cheney, P. D. Corticomotorneuronal postspike effects in shoulder, elbow, wrist, digit, and intrinsic hand muscles during a reach and prehension task. J. Neurophysiol. 80, 1961–1980 (1998)

    CAS  Article  Google Scholar 

  16. Imamizu, H. et al. Human cerebellar activity reflecting an acquired internal model of a new tool. Nature 403, 192–195 (2000)

    ADS  CAS  Article  Google Scholar 

  17. Wolpert, D. M., Miall, R. C. & Kawato, M. Internal models in the cerebellum. Trends Cogn. Sci. 2, 338–347 (1998)

    CAS  Article  Google Scholar 

  18. Schmidt, R. A. & Wrisbert, C. A. Motor Learning and Performance: A Problem-Based Learning Approach (Human Kinetics, Champaign, 2000)

    Google Scholar 

  19. Wightman, D. C. & Lintern, G. Part-task training for tracking and manual control. Hum. Factors 27, 267–283 (1985)

    Article  Google Scholar 

  20. Scott, S. H. & Kalaska, J. F. Reaching movements with similar hand paths but different arm orientations. I. Activity of individual cells in motor cortex. J. Neurophysiol. 77, 826–852 (1997)

    CAS  Article  Google Scholar 

  21. Gribble, P. L. & Scott, S. H. Method for assessing directional characteristics of non-uniformly sampled neural activity. J. Neurosci. Methods 113, 187–197 (2002)

    Article  Google Scholar 

  22. Loeb, G. E. & Gans, C. Electromyography for Experimentalists (Univ. Chicago Press, Chicago, 1986)

    Google Scholar 

Download references

Acknowledgements

We thank K. Moore for technical assistance, and D.W. Cabel and S. Chan who assisted in some of the training and neuronal recording sessions. We thank D. Munoz, M. Pare and K. Rose for comments on this manuscript. This work was supported by a CIHR grant and scholarship (S.H.S.) and a CIHR postdoctoral fellowship (P.L.G.).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Stephen H. Scott.

Ethics declarations

Competing interests

S.H.S. holds a US patent for the robotic device used in these experiments.

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Gribble, P., Scott, S. Overlap of internal models in motor cortex for mechanical loads during reaching. Nature 417, 938–941 (2002). https://doi.org/10.1038/nature00834

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nature00834

Further reading

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing