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Motor cortex retains and reorients neural dynamics during motor imagery

Nature Human Behaviour volume 8, pages 729–742 (2024)Cite this article

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Abstract

The most prominent characteristic of motor cortex is its activation during movement execution, but it is also active when we simply imagine movements in the absence of actual motor output. Despite decades of behavioural and imaging studies, it is unknown how the specific activity patterns and temporal dynamics in motor cortex during covert motor imagery relate to those during motor execution. Here we recorded intracortical activity from the motor cortex of two people who retain some residual wrist function following incomplete spinal cord injury as they performed both actual and imagined isometric wrist extensions. We found that we could decompose the population activity into three orthogonal subspaces, where one was similarly active during both action and imagery, and the others were active only during a single task type—action or imagery. Although they inhabited orthogonal neural dimensions, the action-unique and imagery-unique subspaces contained a strikingly similar set of dynamic features. Our results suggest that during motor imagery, motor cortex maintains the same overall population dynamics as during execution by reorienting the components related to motor output and/or feedback into a unique, output-null imagery subspace.

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Fig. 1: Motor cortex is active during both imagery and action.
Fig. 2: Population activity contains distinct action, imagery, and shared subspaces.
Fig. 3: Temporal components of action and imagery are similar within the shared subspace and across unique subspaces.
Fig. 4: Unique subspaces contain novel dynamic features.
Fig. 5: Action-unique and imagery-unique subspaces contain more complex force-specific responses.
Fig. 6: Moment-by-moment force can be decoded more accurately from the action-unique subspace than from the shared subspace.
Fig. 7: Hypothesized population-level architecture of motor cortex.

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Data availability

Given the potential sensitivity concerns, deidentified data from this study are posted on DABI, a repository for data related to the National Institutes of Health Brain Research Through Advancing Neurotechnologies Initiative. The data for this specific sub-project can be found at https://doi.org/10.18120/70gm-a975 and are available upon request. A portion of the data included in this paper (action conditions only) was used in a previous publication61.

Code availability

The code central to the results presented in this manuscript is publicly available at https://github.com/pitt-rnel/action_imagery.

References

  1. Sirigu, A. et al. The mental representation of hand movements after parietal cortex damage. Science 273, 1564–1568 (1996).

    CAS  PubMed  Google Scholar 

  2. Sirigu, A. et al. Congruent unilateral impairments for real and imagined hand movements. NeuroReport 6, 997–1001 (1995).

    CAS  PubMed  Google Scholar 

  3. Clark, L. V. Effect of mental practice on the development of a certain motor skill. Res. Q. 31, 560–569 (1960).

    Google Scholar 

  4. Frank, C., Land, W. M., Popp, C. & Schack, T. Mental representation and mental practice: experimental investigation on the functional links between motor memory and motor imagery. PLoS ONE 9, e95175 (2014).

    PubMed  PubMed Central  Google Scholar 

  5. Ladda, A. M., Lebon, F. & Lotze, M. Using motor imagery practice for improving motor performance—a review. Brain Cogn. 150, 105705 (2021).

    PubMed  Google Scholar 

  6. Yue, G. & Cole, K. J. Strength increases from the motor program: comparison of training with maximal voluntary and imagined muscle contractions. J. Neurophysiol. 67, 1114–1123 (1992).

    CAS  PubMed  Google Scholar 

  7. Hotz-Boendermaker, S. et al. Preservation of motor programs in paraplegics as demonstrated by attempted and imagined foot movements. NeuroImage 39, 383–394 (2008).

    PubMed  Google Scholar 

  8. Jeannerod, M. Motor Cognition: What Actions Tell the Self (Oxford Univ. Press, 2006).

  9. Kilteni, K., Andersson, B. J., Houborg, C. & Ehrsson, H. H. Motor imagery involves predicting the sensory consequences of the imagined movement. Nat. Commun. 9, 1617 (2018).

    PubMed  PubMed Central  Google Scholar 

  10. Rastogi, A. et al. Neural representation of observed, imagined, and attempted grasping force in motor cortex of individuals with chronic tetraplegia. Sci. Rep. 10, 1429 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Vargas-Irwin, C. E. et al. Watch, imagine, attempt: motor cortex single-unit activity reveals context-dependent movement encoding in humans with tetraplegia. Front. Hum. Neurosci. 12, 450 (2018).

  12. Cisek, P. & Kalaska, J. F. Neural correlates of mental rehearsal in dorsal premotor cortex. Nature 431, 993–996 (2004).

    CAS  PubMed  Google Scholar 

  13. Stephan, K. M. et al. Functional anatomy of the mental representation of upper extremity movements in healthy subjects. J. Neurophysiol. 73, 373–386 (1995).

    CAS  PubMed  Google Scholar 

  14. Aflalo, T. et al. Decoding motor imagery from the posterior parietal cortex of a tetraplegic human. Science 348, 906–910 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Jiang, X., Saggar, H., Ryu, S. I., Shenoy, K. V. & Kao, J. C. Structure in neural activity during observed and executed movements is shared at the neural population level, not in single neurons. Cell Rep. 32, 108006 (2020).

    CAS  PubMed  Google Scholar 

  16. Elsayed, G. F., Lara, A. H., Kaufman, M. T., Churchland, M. M. & Cunningham, J. P. Reorganization between preparatory and movement population responses in motor cortex. Nat. Commun. 7, 13239 (2016).

  17. Kaufman, M. T., Churchland, M. M., Ryu, S. I. & Shenoy, K. V. Cortical activity in the null space: permitting preparation without movement. Nat. Neurosci. 17, 440–448 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Elsayed, G. F. & Cunningham, J. P. Structure in neural population recordings: an expected byproduct of simpler phenomena? Nat. Neurosci. 20, 1310–1318 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Gallego, J. A., Perich, M. G., Miller, L. E. & Solla, S. A. Neural manifolds for the control of movement. Neuron 94, 978–984 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Gallego, J. A., Perich, M. G., Chowdhury, R. H., Solla, S. A. & Miller, L. E. Long-term stability of cortical population dynamics underlying consistent behavior. Nat. Neurosci. 23, 260–270 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Remington, E. D., Narain, D., Hosseini, E. A. & Jazayeri, M. Flexible sensorimotor computations through rapid reconfiguration of cortical dynamics. Neuron 98, 1005–1019.e5 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Sadtler, P. T. et al. Neural constraints on learning. Nature 512, 423–426 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Shenoy, K. V., Sahani, M. & Churchland, M. M. Cortical control of arm movements: a dynamical systems perspective. Annu. Rev. Neurosci. 36, 337–359 (2013).

    CAS  PubMed  Google Scholar 

  24. Kaufman, M. T. et al. The implications of categorical and category-free mixed selectivity on representational geometries. Curr. Opin. Neurobiol. 77, 102644 (2022).

    CAS  PubMed  Google Scholar 

  25. Russo, A. A. et al. Motor cortex embeds muscle-like commands in an untangled population response. Neuron 97, 953–966.e8 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Shalit, U., Zinger, N., Joshua, M. & Prut, Y. Descending systems translate transient cortical commands into a sustained muscle activation signal. Cereb. Cortex 22, 1904–1914 (2012).

    PubMed  Google Scholar 

  27. Albert, S. T. et al. Postural control of arm and fingers through integration of movement commands. Elife 9, e52507 (2020).

  28. Ryan, E. D. & Simons, J. Efficacy of mental imagery in enhancing mental rehearsal of motor skills. J. Sport Exerc. Psychol. 4, 41–51 (1982).

    Google Scholar 

  29. Schack, T., Essig, K., Frank, C. & Koester, D. Mental representation and motor imagery training. Front. Hum. Neurosci. 8, 328 (2014).

  30. Sheahan, H. R., Ingram, J. N., Žalalytė, G. M. & Wolpert, D. M. Imagery of movements immediately following performance allows learning of motor skills that interfere. Sci. Rep. 8, 14330 (2018).

    PubMed  PubMed Central  Google Scholar 

  31. Churchland, M. M. et al. Neural population dynamics during reaching. Nature 487, 51–56 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Perich, M. G. et al. Motor cortical dynamics are shaped by multiple distinct subspaces during naturalistic behavior. Preprint at bioRxiv https://doi.org/10.1101/2020.07.30.228767 (2020).

  33. Cisek, P. & Kalaska, J. F. Neural correlates of reaching decisions in dorsal premotor cortex: specification of multiple direction choices and final selection of action. Neuron 45, 801–814 (2005).

    CAS  PubMed  Google Scholar 

  34. Churchland, M. M., Cunningham, J. P., Kaufman, M. T., Ryu, S. I. & Shenoy, K. V. Cortical preparatory activity: representation of movement or first cog in a dynamical machine? Neuron 68, 387–400 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Dushanova, J. & Donoghue, J. Neurons in primary motor cortex engaged during action observation. Eur. J. Neurosci. 31, 386–398 (2010).

    PubMed  PubMed Central  Google Scholar 

  36. Hari, R. et al. Activation of human primary motor cortex during action observation: a neuromagnetic study. Proc. Natl Acad. Sci. USA 95, 15061–15065 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Holmes, P., Collins, D. & Calmels, C. Electroencephalographic functional equivalence during observation of action. J. Sports Sci. 24, 605–616 (2006).

    PubMed  Google Scholar 

  38. Muthukumaraswamy, S. D. & Johnson, B. W. Primary motor cortex activation during action observation revealed by wavelet analysis of the EEG. Clin. Neurophysiol. 115, 1760–1766 (2004).

    PubMed  Google Scholar 

  39. Papadourakis, V. & Raos, V. Neurons in the macaque dorsal premotor cortex respond to execution and observation of actions. Cereb. Cortex 29, 4223–4237 (2019).

    PubMed  Google Scholar 

  40. Rizzolatti, G., Fadiga, L., Gallese, V. & Fogassi, L. Premotor cortex and the recognition of motor actions. Cogn. Brain Res. 3, 131–141 (1996).

    CAS  Google Scholar 

  41. Stefan, K. et al. Formation of a motor memory by action observation. J. Neurosci. 25, 9339–9346 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Tkach, D., Reimer, J. & Hatsopoulos, N. G. Congruent activity during action and action observation in motor cortex. J. Neurosci. 27, 13241–13250 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Mazurek, K. A., Rouse, A. G. & Schieber, M. H. Mirror neuron populations represent sequences of behavioral epochs during both execution and observation. J. Neurosci. 38, 4441–4455 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Vigneswaran, G., Philipp, R., Lemon, R. N. & Kraskov, A. M1 corticospinal mirror neurons and their role in movement suppression during action observation. Curr. Biol. 23, 236–243 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Rizzolatti, G., Fogassi, L. & Gallese, V. Neurophysiological mechanisms underlying the understanding and imitation of action. Nat. Rev. Neurosci. 2, 661–670 (2001).

    CAS  PubMed  Google Scholar 

  46. Rubin, D. B. et al. Learned motor patterns are replayed in human motor cortex during sleep. J. Neurosci. 42, 5007–5020 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Hennig, J. A. et al. Learning is shaped by abrupt changes in neural engagement. Nat. Neurosci. 24, 727–736 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Kaufman, M. T. et al. The largest response component in the motor cortex reflects movement timing but not movement type. eNeuro 3, ENEURO.0085-16.2016 (2016).

  49. Vyas, S. et al. Neural population dynamics underlying motor learning transfer. Neuron 97, 1177–1186.e3 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Kim, O. A., Forrence, A. D. & McDougle, S. D. Motor learning without movement. Proc. Natl Acad. Sci. USA 119, e2204379119 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Kendall, F. P., McCreary, E. K. & Provance, P. G. Muscles: Testing and Function 4th edn (Williams & Wilkins, 1993).

  52. Kirshblum, S. C. et al. International standards for neurological classification of spinal cord injury (revised 2011). J. Spinal Cord Med. 34, 535–546 (2011).

    PubMed  PubMed Central  Google Scholar 

  53. Flesher, S. N. et al. Intracortical microstimulation of human somatosensory cortex. Sci. Transl. Med. 8, 361ra141 (2016).

    PubMed  Google Scholar 

  54. Degenhart, A. D. et al. Stabilization of a brain–computer interface via the alignment of low-dimensional spaces of neural activity. Nat. Biomed. Eng. 4, 672–685 (2020).

    PubMed  PubMed Central  Google Scholar 

  55. Gower, J. C. Generalized Procrustes analysis. Psychometrika 40, 33–51 (1975).

    Google Scholar 

  56. Boumal, N., Mishra, B., Absil, P.-A. & Sepulchre, R. Manopt, a Matlab toolbox for optimization on manifolds. J. Mach. Learn. Res. 15, 1455–1459 (2014).

    Google Scholar 

  57. Kobak, D. et al. Demixed principal component analysis of neural population data. eLife 5, e10989 (2016).

    PubMed  PubMed Central  Google Scholar 

  58. Jude, J., Perich, M., Miller, M. & Hennig, M. Robust alignment of cross-session recordings of neural population activity by behaviour via unsupervised domain adaptation. In Proc. 39th International Conference on Machine Learning, PMLR 162, 10462–10475 (2022).

  59. Glaser, J. I. et al. Machine learning for neural decoding. eNeuro 7, ENEURO.0506-19.2020 (2020).

  60. Westwick, D. T., Pohlmeyer, E. A., Solla, S. A., Miller, L. E. & Perreault, E. J. Identification of multiple-input systems with highly coupled inputs: application to EMG prediction from multiple intracortical electrodes. Neural Comput. 18, 329–355 (2006).

    PubMed  PubMed Central  Google Scholar 

  61. Balasubramanian, K., Arce-McShane, F. I., Dekleva, B. M., Collinger, J. L. & Hatsopoulos, N. G. Propagating motor cortical patterns of excitability are ubiquitous across human and non-human primate movement initiation. iScience 26, 106518 (2023).

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Acknowledgements

We thank N. Copeland and Mr. Dom for their continued efforts and commitment to this study. We also thank the research team, especially D. Harrington for regulatory management as well as C. Schoenewald, J. Ting, D. Sarma, A. Sethi and J. Weiss for their help with data collection. The research reported in this publication was supported by the National Institute of Neurological Disorders and Stroke of the National Institutes of Health under award numbers UH3NS107714 and U01NS108922. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Authors and Affiliations

  1. Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA

    Brian M. Dekleva, Michael L. Boninger & Jennifer L. Collinger

  2. Department of Physical Medicine & Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA

    Brian M. Dekleva, Michael L. Boninger & Jennifer L. Collinger

  3. Center for the Neural Basis of Cognition, Pittsburgh, PA, USA

    Brian M. Dekleva, Raeed H. Chowdhury, Aaron P. Batista, Steven M. Chase & Byron M. Yu

  4. Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA

    Raeed H. Chowdhury, Aaron P. Batista, Michael L. Boninger & Jennifer L. Collinger

  5. Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA

    Steven M. Chase, Byron M. Yu & Jennifer L. Collinger

  6. Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA

    Steven M. Chase

  7. Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA

    Byron M. Yu

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Contributions

B.M.D. designed the task and collected the data. B.M.D., R.H.C. and S.M.C. contributed to the analysis. All authors contributed to the interpretation of the results. B.M.D. wrote the manuscript, with input from all authors.

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Correspondence to Jennifer L. Collinger.

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Nature Human Behaviour thanks Jonathan Michaels, Daniel O’Shea and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary Figs. 1–9, Table 1 and associated methodological descriptions.

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Dekleva, B.M., Chowdhury, R.H., Batista, A.P. et al. Motor cortex retains and reorients neural dynamics during motor imagery. Nat Hum Behav 8, 729–742 (2024). https://doi.org/10.1038/s41562-023-01804-5

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