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Reversible large-scale modification of cortical networks during neuroprosthetic control

Abstract

Brain-machine interfaces (BMIs) provide a framework for studying cortical dynamics and the neural correlates of learning. Neuroprosthetic control has been associated with tuning changes in specific neurons directly projecting to the BMI (hereafter referred to as direct neurons). However, little is known about the larger network dynamics. By monitoring ensembles of neurons that were either causally linked to BMI control or indirectly involved, we found that proficient neuroprosthetic control is associated with large-scale modifications to the cortical network in macaque monkeys. Specifically, there were changes in the preferred direction of both direct and indirect neurons. Notably, with learning, there was a relative decrease in the net modulation of indirect neural activity in comparison with direct activity. These widespread differential changes in the direct and indirect population activity were markedly stable from one day to the next and readily coexisted with the long-standing cortical network for upper limb control. Thus, the process of learning BMI control is associated with differential modification of neural populations based on their specific relation to movement control.

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Figure 1: Modification of neural firing properties during brain control.
Figure 2: Differential modulation of neuronal populations during brain control.
Figure 3: Stability of neural properties.
Figure 4: Stability of state-dependent changes in neural properties during a session.
Figure 5: Stability of neural properties across consecutive days of brain control.

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References

  1. Wise, S.P., Moody, S.L., Blomstrom, K.J. & Mitz, A.R. Changes in motor cortical activity during visuomotor adaptation. Exp. Brain Res. 121, 285–299 (1998).

    Article  CAS  Google Scholar 

  2. Paz, R. & Vaadia, E. Learning-induced improvement in encoding and decoding of specific movement directions by neurons in the primary motor cortex. PLoS Biol. 2, e45 (2004).

    Article  Google Scholar 

  3. Paz, R., Boraud, T., Natan, C., Bergman, H. & Vaadia, E. Preparatory activity in motor cortex reflects learning of local visuomotor skills. Nat. Neurosci. 6, 882–890 (2003).

    Article  CAS  Google Scholar 

  4. 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).

    Article  CAS  Google Scholar 

  5. Li, C.S., Padoa-Schioppa, C. & Bizzi, E. 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 (2001).

    Article  CAS  Google Scholar 

  6. Padoa-Schioppa, C., Li, C.S. & Bizzi, E. Neuronal correlates of kinematics-to-dynamics transformation in the supplementary motor area. Neuron 36, 751–765 (2002).

    Article  CAS  Google Scholar 

  7. Rokni, U., Richardson, A.G., Bizzi, E. & Seung, H.S. Motor learning with unstable neural representations. Neuron 54, 653–666 (2007).

    Article  CAS  Google Scholar 

  8. Arce, F., Novick, I., Mandelblat-Cerf, Y. & Vaadia, E. Neuronal correlates of memory formation in motor cortex after adaptation to force field. J. Neurosci. 30, 9189–9198 (2010).

    Article  CAS  Google Scholar 

  9. Chapin, J.K., Moxon, K.A., Markowitz, R.S. & Nicolelis, M.A. Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex. Nat. Neurosci. 2, 664–670 (1999).

    Article  CAS  Google Scholar 

  10. Birbaumer, N. et al. A spelling device for the paralyzed. Nature 398, 297–298 (1999).

    Article  CAS  Google Scholar 

  11. Serruya, M.D., Hatsopoulos, N.G., Paninski, L., Fellows, M.R. & Donoghue, J.P. Instant neural control of a movement signal. Nature 416, 141–142 (2002).

    Article  CAS  Google Scholar 

  12. Taylor, D.M., Tillery, S.I. & Schwartz, A.B. Direct cortical control of 3D neuroprosthetic devices. Science 296, 1829–1832 (2002).

    Article  CAS  Google Scholar 

  13. Carmena, J.M. et al. Learning to control a brain-machine interface for reaching and grasping by primates. PLoS Biol. 1, e42 (2003).

    Article  Google Scholar 

  14. Musallam, S., Corneil, B.D., Greger, B., Scherberger, H. & Andersen, R.A. Cognitive control signals for neural prosthetics. Science 305, 258–262 (2004).

    Article  CAS  Google Scholar 

  15. Wolpaw, J.R. & McFarland, D.J. Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. Proc. Natl. Acad. Sci. USA 101, 17849–17854 (2004).

    Article  CAS  Google Scholar 

  16. Leuthardt, E.C., Schalk, G., Wolpaw, J.R., Ojemann, J.G. & Moran, D.W. A brain-computer interface using electrocorticographic signals in humans. J. Neural Eng. 1, 63–71 (2004).

    Article  Google Scholar 

  17. Santhanam, G., Ryu, S.I., Yu, B.M., Afshar, A. & Shenoy, K.V. A high-performance brain-computer interface. Nature 442, 195–198 (2006).

    Article  CAS  Google Scholar 

  18. Hochberg, L.R. et al. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 442, 164–171 (2006).

    Article  CAS  Google Scholar 

  19. Velliste, M., Perel, S., Spalding, M.C., Whitford, A.S. & Schwartz, A.B. Cortical control of a prosthetic arm for self-feeding. Nature 453, 1098–1101 (2008).

    Article  CAS  Google Scholar 

  20. Galán, F. et al. A brain-actuated wheelchair: asynchronous and non-invasive Brain-computer interfaces for continuous control of robots. Clin. Neurophysiol. 119, 2159–2169 (2008).

    Article  Google Scholar 

  21. Moritz, C.T., Perlmutter, S.I. & Fetz, E.E. Direct control of paralyzed muscles by cortical neurons. Nature 456, 639–642 (2008).

    Article  CAS  Google Scholar 

  22. Jarosiewicz, B. et al. Functional network reorganization during learning in a brain-computer interface paradigm. Proc. Natl. Acad. Sci. USA 105, 19486–19491 (2008).

    Article  CAS  Google Scholar 

  23. Ganguly, K. & Carmena, J.M. Emergence of a stable cortical map for neuroprosthetic control. PLoS Biol. 7, e1000153 (2009).

    Article  Google Scholar 

  24. Fetz, E.E. Volitional control of neural activity: implications for brain-computer interfaces. J. Physiol. (Lond.) 579, 571–579 (2007).

    Article  CAS  Google Scholar 

  25. Humphrey, D.R., Schmidt, E.M. & Thompson, W.D. Predicting measures of motor performance from multiple cortical spike trains. Science 170, 758–762 (1970).

    Article  CAS  Google Scholar 

  26. Ganguly, K. et al. Cortical representation of ipsilateral arm movements in monkey and man. J. Neurosci. 29, 12948–12956 (2009).

    Article  CAS  Google Scholar 

  27. Ganguly, K. & Carmena, J.M. Neural correlates of skill acquisition with a cortical brain-machine interface. J. Mot. Behav. 42, 355–360 (2010).

    Article  Google Scholar 

  28. Chestek, C.A. et al. Single-neuron stability during repeated reaching in macaque premotor cortex. J. Neurosci. 27, 10742–10750 (2007).

    Article  CAS  Google Scholar 

  29. Nicolelis, M.A. et al. Chronic, multisite, multielectrode recordings in macaque monkeys. Proc. Natl. Acad. Sci. USA 100, 11041–11046 (2003).

    Article  CAS  Google Scholar 

  30. Grossman, S.E., Fontanini, A., Wieskopf, J.S. & Katz, D.B. Learning-related plasticity of temporal coding in simultaneously recorded amygdala-cortical ensembles. J. Neurosci. 28, 2864–2873 (2008).

    Article  CAS  Google Scholar 

  31. Greenberg, P.A. & Wilson, F.A. Functional stability of dorsolateral prefrontal neurons. J. Neurophysiol. 92, 1042–1055 (2004).

    Article  Google Scholar 

  32. Caminiti, R., Johnson, P.B. & Urbano, A. Making arm movements within different parts of space: dynamic aspects in the primate motor cortex. J. Neurosci. 10, 2039–2058 (1990).

    Article  CAS  Google Scholar 

  33. Ajemian, R. et al. Assessing the function of motor cortex: single-neuron models of how neural response is modulated by limb biomechanics. Neuron 58, 414–428 (2008).

    Article  CAS  Google Scholar 

  34. Lebedev, M.A. et al. Cortical ensemble adaptation to represent velocity of an artificial actuator controlled by a brain-machine interface. J. Neurosci. 25, 4681–4693 (2005).

    Article  CAS  Google Scholar 

  35. Carmena, J.M., Lebedev, M.A., Henriquez, C.S. & Nicolelis, M.A. Stable ensemble performance with single-neuron variability during reaching movements in primates. J. Neurosci. 25, 10712–10716 (2005).

    Article  CAS  Google Scholar 

  36. 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).

    Article  CAS  Google Scholar 

  37. Nicolelis, M.A. & Lebedev, M.A. Principles of neural ensemble physiology underlying the operation of brain-machine interfaces. Nat. Rev. Neurosci. 10, 530–540 (2009).

    Article  CAS  Google Scholar 

  38. Fetz, E.E. Operant conditioning of cortical unit activity. Science 163, 955–958 (1969).

    Article  CAS  Google Scholar 

  39. Green, A.M. & Kalaska, J.F. Learning to move machines with the mind. Trends Neurosci. 34, 61–75 (2011).

    Article  CAS  Google Scholar 

  40. Lebedev, M.A. et al. Cortical ensemble adaptation to represent velocity of an artificial actuator controlled by a brain-machine interface. J. Neurosci. 25, 4681–4693 (2005).

    Article  CAS  Google Scholar 

  41. Fetz, E.E. & Baker, M.A. Operantly conditioned patterns on precentral unit activity and correlated responses in adjacent cells and contralateral muscles. J. Neurophysiol. 36, 179–204 (1973).

    Article  CAS  Google Scholar 

  42. Legenstein, R., Pecevski, D. & Maass, W. A learning theory for reward-modulated spike timing–dependent plasticity with application to biofeedback. PLoS Comput. Biol. 4, e1000180 (2008).

    Article  Google Scholar 

  43. Davidson, A.G., Chan, V., O'Dell, R. & Schieber, M.H. Rapid changes in throughput from single motor cortex neurons to muscle activity. Science 318, 1934–1937 (2007).

    Article  CAS  Google Scholar 

  44. Georgopoulos, A.P., Schwartz, A.B. & Kettner, R.E. Neuronal population coding of movement direction. Science 233, 1416–1419 (1986).

    Article  CAS  Google Scholar 

  45. Briggman, K.L., Abarbanel, H.D. & Kristan, W.B. Jr. Optical imaging of neuronal populations during decision-making. Science 307, 896–901 (2005).

    Article  CAS  Google Scholar 

  46. Churchland, M.M., Yu, B.M., Sahani, M. & Shenoy, K.V. Techniques for extracting single-trial activity patterns from large-scale neural recordings. Curr. Opin. Neurobiol. 17, 609–618 (2007).

    Article  CAS  Google Scholar 

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Acknowledgements

This work was supported by the Department of Veterans Affairs, Veterans Health Administration, Rehabilitation Research and Development, and the American Heart Association/American Stroke Association (to K.G.), the National Institute of Neurological Disorders and Stroke grant number NS21135 (to J.D.W.), the Alfred P. Sloan Foundation, the Christopher and Dana Reeve Foundation, the National Science Foundation CAREER Award #0954243 and the Defense Advanced Research Projects Agency contract N66001-10-C-2008 (to J.M.C.).

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K.G. and J.M.C. designed the experiments. K.G. and J.M.C. performed behavioral training. K.G. performed the experiments and analyzed the data. K.G. and J.M.C. wrote the paper. D.F.D., J.D.W., J.M.C. and K.G. performed surgical procedures. K.G., J.D.W. and J.M.C. revised the paper.

Corresponding author

Correspondence to Jose M Carmena.

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Ganguly, K., Dimitrov, D., Wallis, J. et al. Reversible large-scale modification of cortical networks during neuroprosthetic control. Nat Neurosci 14, 662–667 (2011). https://doi.org/10.1038/nn.2797

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